Miracle-dz/UniFAR-FTUs huggingface dataset:
Online reinforcement learning control for aerospace systems

Reinforcement Learning (RL) methods are relatively new in the field of aerospace guidance, navigation, and control. This dissertation aims to exploit RL methods to improve the autonomy and online learning of aerospace systems with respect to the a priori unknown system and environment, dynamical uncertainties, and partial observability. In the first part of this dissertation, incremental Approximate Dynamic Programming (iADP) methods are proposed. Instead of using nonlinear function approximators to approximate the true cost-to-go, iADP methods use an (extended) incremental model to deal with the nonlinearity of unknown systems and uncertainties of the environment. In the second part, online Adaptive Critic Designs (ACDs) are proposed based on the incremental model. This method replaces the global system model approximator with an incremental model. This approach, therefore, does not need off-line training stages and may accelerate online learning. In the third part, the hybrid Hierarchical Reinforcement Learning (hHRL) method is proposed for guidance and navigation problems. This method consists of several hierarchical levels, where each level uses different methods to optimize the learning with different types of information and objectives. In conclusion, this dissertation contributes with several methods that improve the intelligence and autonomy of aerospace systems. These improvements are mainly from three perspectives: 1) enhancing the adaptability and efficiency of low-level control, 2) improving the intelligence and online learning ability of guidance, navigation, and control, and 3) creating a well-organized hierarchy to ensure coordination between each level. The proposed methods provide novel insights for both the reinforcement learning research community and for developers of aerospace automatic control system.
Understanding the Process of Economic Development: Essays on Culture and Institutions

This dissertation explores the origins of various institutions and cultural norms and the implications of these institutions and norms for economic development and development policy. In the first paper, I examine how matrilineal kinship systems affect spousal cooperation. In matrilineal kinship systems, lineage is traced through female members. I use experimental and physiological measures and a geographic regression discontinuity design along the “matrilineal belt” in Africa to show that matrilineal individuals are less cooperative with their spouses. However, children of matrilineal women are healthier and better educated, and matrilineal women experience less domestic violence. The results highlight how broader social structures affect bargaining within the household. The second paper, joint work with Eduardo Montero, examines how historical exposure to extractive institutions affects long-run development in the case of the Congo Free State. The CFS granted concessions to private companies that used violent tactics to force people to collect rubber. We use a geographic regression discontinuity design along the concession boundaries to show that greater exposure to extractive institutions causes significantly worse development outcomes. We explore the role of institutions and culture as channels. Chiefs within the former concessions provide fewer public goods and are less likely to be elected. However, individuals within the concessions are more pro-social. The results suggest that events of short duration can affect development through changes
Measurement Properties of the Short Child Occupational Profile (SCOPE)

The purpose of this study was to examine the reliability and validity of the Short Child Occupational Profile (SCOPE; version 2.0). The SCOPE is an occupation focused, clientcentered, theory driven assessment developed out of a practice/academic partnership. In this study, twenty practitioners from occupational therapy, physical therapy, speech therapy, and social work completed thirty-five SCOPE assessments with clients' ages two years, four months to twenty-one years with both physical and intellectual disabilities. All therapists rated one common client. Rasch analysis using Facets was used to analyze the items. Results reveal that the SCOPE items coalesce to represent the construct of occupational participation in a valid manner and that most clients can be measured in a valid way by those items. Therapists used the SCOPE in a similar way and analysis demonstrated there was limited variability among therapists. The SCOPE discriminated between clients who had varying levels of occupational participation. The results indicate a need to do minor revisions to the assessment in order to maximize the psychometric properties and enhance clinicians' understanding of the items and rating scale terms.
Stochastic Actor-Oriented Models for Network Dynamics

This article discusses the stochastic actor-oriented model for analyzing panel data of networks. The model is defined as a continuous-time Markov chain, observed at two or more discrete time moments. It can be regarded as a generalized linear model with a large amount of missing data. Several estimation methods are discussed. After presenting the model for evolution of networks, attention is given to coevolution models. These use the same approach of a continuous-time Markov chain observed at a small number of time points, but now with an extended state space. The state space can be, for example, the combination of a network and nodal variables, or a combination of several networks. This leads to models for the dynamics of multivariate networks. The article emphasizes the approach to modeling and algorithmic issues for estimation; some attention is given to comparison with other models.
SportsBuzzer: Detecting Events at Real Time in Twitter using Incremental Clustering

In the recent past, twitter users are highly regarded as social sensors who can report events and Twitter has been widely used to detect social and physical events such as earthquakes and traffic jam . Real time event detection in Twitter is the process of detecting events at real time from live tweet stream as soon as an event has happened. Real time event detection from sports tweets, such as Cricket is an interesting, yet a complex problem. Because, an event detection system needs to collect live sports tweets and should rapidly detect key events such as boundary and catch at real-time when the game is ongoing. In this paper, a novel framework is proposed for detecting key events at real time from live tweets of the Cricket sports domain. Feature vectors of live tweets are created using TF-IDF representation and tweet clusters are discovered using Locality Sensitive Hashing (LSH) where the post rate of each cluster based on the volume of tweets is computed. If the post rate is above the predefined threshold, then a key event recognized from that cluster using our domain specific event lexicon for Cricket sports. The predefined threshold helps to filter out small spikes in the tweets volume. The proposed real-time event detection algorithm is extensively evaluated on 2017 IPL T20 Cricket live tweets using ROC evaluation measure. The experimental results on the performance of the proposed approach show that the LSH approach detects sports events with nearly 90% true positive rate and around 10% false positive rate. The results have also demonstrated the influence of different parameters on the accuracy of the event detection.
Analysis of possibilities to use information from NetFlow protocol for improvement of performance of Wide Area Network

The main goal of this project is to analyze the data regarding to the connections of the Wide Area Networks (WAN) with the networks of Akademia Górniczo-Hutnicza (AGH University), and therefore try to obtain possibilities of optimizimization. Due to this, by analyzing the dumps of the connections, we will collect the characteristics of this network, and therefore to try to figure out how to resolve the troubleshooting this connections encountered. This document contains the obtained results of a deep analyze of the traffic produced on a WAN and along it (campus network), and also the methods and tools that have been used during the development of it. Once shown the results of the analyze, the study of possible solutions, deducted from previous studies will be presented, showing all the possibilities that might perform somehow the WAN and also the possible drawbacks of them.
Impacts of a conspicuity treatment on speed limit compliance

Impacts of a Conspicuity Treatment on Speed Limit Compliance. (May 2006) Roma Garg, B.E. ; M.E. , Thapar Institute of Engineering & Technology, Patiala, India Chair of Committee: Dr. H. Gene Hawkins, Jr. In reduced speed zones, where no other cues indicate the need to slow down, drivers sometimes fail to notice the standard Speed Limit sign and may be speeding inadvertently. To help reduce inadvertent speeding, a red border was installed around the Speed Limit sign in seven reduced speed areas and the impacts of the increased conspicuity on speed limit compliance were measured. The general study approach was to collect and compare speed data for a standard sign (before condition) and a red border sign (after condition). The short-term effects of a modified red border sign, which was achieved by replacing the thin black border of the standard sign with a four inch wide red border, were evaluated at four sites. Results of this modified border study indicated that there was a statistically significant reduction in the mean speeds as well as in the 85 percentile speeds for the red border sign conditions, however the reductions were not practically significant. This study also evaluated the effect of using a higher conspicuity sheeting material at two sites. The results indicated that use of higher conspicuity sheeting has some benefits for the standard sign but no additional benefits for the red border sign. The added border study evaluated the long-term effects (approximately nine to eleven months after the treatment) of adding a three inch wide red border to the standard Speed Limit sign at three sites. The results of this study indicated that impacts of the red border treatment increase with passage of time. The mean speeds decreased by 8.1 percent and the percent of vehicles exceeding the speed limit (55 mph) decreased by 21.7 percent. The decreases in speeds were both statistically and practically significant. A comparison of the thesis study with other similar studies found in literature shows comparable benefits of the red border sign with other speed management
Rethinking social media for qualitative research: The use of Facebook Activity Logs and Search History in interview settings

Following calls to rethink the repertoires of social research and take advantage of the new possibilities opened by digital data and devices, this article discusses the opportunities and challenges of using Facebook Activity Logs (FAL) and Search History (FSH) as digital probes during interviews. Drawing on empirical data, the article outlines the value of using social media features in qualitative research with regard to generating thick data and encouraging people to reflect upon the range of everyday practices captured by the platforms. This article argues, however, that to use social media features and data in interview settings researchers need to carefully identify and examine the different forms of liveliness generated by their use and the ways in which liveliness mediates and affects the research data and the situation of the interview itself. The article contends that critically engaging with the liveliness generated by these types of probes in interview settings will allow researchers to better discern how digital platforms and data can inform social enquiry while simultaneously forming a part of how we know social lives and practices.
Factors Affecting Internet Users' Information Privacy Protection

Based on foreign models of privacy protection,this paper researches the factors affecting the Chinese Internet users' information privacy protection,including privacy concerns,perceived fairness and perceived complaining benefits,and some hypotheses are put forward and tested through structural path analysis using the data from questionnaire.The results show that the hypotheses are basically accepted,and the research supplies helpful references for the online companies in customer relationship management.
Issues in the Left Periphery of Modern Irish

Although the syntax of the left periphery of the Irish clausal architecture has been the subject of considerable research within the generative paradigm, many questions remain unresolved. The general goal of this thesis is to explore some of these understudied territories. Specifically, I consider two distinct, but ultimately related phenomena: headless relative clauses and dependent verbal morphology. I will make four major claims: The first two concern the syntax (and semantics) of the headless relative clause. First, despite the fact that the particles that appear in resumptive relative clauses and in headless relative clauses are morpho-phonologically identical as aN, headless relative clauses are derived by movement, not by means of resumption, and thus the particles in these two constructions are not the same. Second, headless relative clauses are amount relative clauses, in the sense of Carlson (1977); and thus I claim, adopting Grosu and Landman’s (1998) notion of complex degree, that the element that undergoes A′-movement in a headless relative clause is a complex degree, causing degree-abstraction in the semantics. The maximalization operator then applies to the degree-abstracted relative CP. I argue that it is this operator that triggers the appearance of the particle aN in the headless relative construction. The latter two claims concern the morphosyntax of the left periphery of Irish syntax: First, I claim that there are two tense features in a single finite clause domain of Irish, and that the so-called dependent forms of irregular verbs are the surface realization of the two tense features. This account provides a stepping stone to my final claim that a feature agreeing with the maximalization operator, but not the operator itself, is realized in the headless relative particle aN and that the particles found in resumptive relative clauses and in headless relative clauses are in fact distinct Vocabulary Items and thus they are homophonous. This thesis thus fills a gap in the descriptive account of Irish syntax, and provides new insights to the theory of relativization.
Levantamento parasitológico e sorológico de Leishmaniose em cães da Unidade de Controle de Zoonoses (UCZ) de Guaraí (TO)

Os cães são atingidos por diversos patógenos transmitidos o que pode acarretar testes falso-positivo quando existe reação cruzada. Para identificar essas reações foi realizado um levantamento parasitológico e sorológico de Leishmaniose em cães da Unidade de Controle de Zoonoses (UCZ) de Guaraí-TO, em 10 amostras de sangue periférico, linfa através de punção aspirativa nos linfonodos do membro posterior esquerdo e punção aspirativa por medula óssea no esterno. Adicionalmente foi realizado teste imunocromatográfico (DPP® LVC Bio-Manguinhos/FIOCRUZ), e confirmados com pesquisa de parasitos através de exame parasitológico direto. Embora todos se apresentaram positivos no teste rápido, o resultado se comprovou significativamente nas amostras examinadas no parasitológico apresentando prevalência de 100% para Leishmania, 60% para Babesia e 30% para Ehrlichia. O teste rápido (DPP®) mostrou-se eficiente no presente estudo, sendo que em todos os animais positivos foram confirmados no exame parasitológico.
Visualization abilities of mathematically gifted students: a comparison between the results from psychometric tests and the visualization abilities used for geometrical

Based on a review of the research, we observe that there is not a single opinion about the relationship between mathematical gift and visualization. One factor to be considered is the variety of instrument used to characterize the visualization. In this paper we compare the results from two visual tests with the visualization abilities shown in three curricular enrichment sessions. The results from a group of 25 students participating in a project for enrichment of the mathematical giftedness do not show any relationship between the visualization abilities shown in the tests and the visualization techniques used to solve geometrical tasks.
Promotion of the Environmental Knowledge and Behavior through the Moroccan Syllabus of Sciences in the Middle School

b,* Corresponding Author: Anouar Alami, Interdisciplinary Research Laboratory in Didactics of Science and Technology, LIRDIST, Faculty of Sciences Dhar Mahraz, Sidi Mohamed Ben Abdellah University, Fes, Morocco. E-mail:anouar.alami@usmba.ac. ma aBouchta El Batri, Interdisciplinary Research Laboratory in Didactics of Science and Technology, LIRDIST, Faculty of Sciences Dhar Mahraz, Sidi Mohamed Ben Abdellah University, Fes, Morocco. cMoncef Zaki, Interdisciplinary Research Laboratory in Didactics of Science and Technology, LIRDIST, Faculty of Sciences Dhar Mahraz, Sidi Mohamed Ben Abdellah University, Fes, Morocco. dYoussef Nafidi, Interdisciplinary Research Laboratory in Didactics of Science and Technology, LIRDIST, Faculty of Sciences Dhar Mahraz, Sidi Mohamed Ben Abdellah University, Fes, Morocco. eDriss Chenfour, Oum Lbanin High school, Fez Morocco. Abstract
INTERNET OF THING BASED CAR PARKING SYSTEM

In the current era, we are facing a new problem of parking of vehicles. It is a major problem in urban cities. The problem is more tough because of continues growing number of vehicle and also size of vehicles. Car parking is not just a major problem in India but also in all over the world. We know that one million vehicles burn oil every day. In this paper, we propose an automatic and real-time system for automated car parking. This system would be implemented by the use of internet of things (IOTs). IOT refers as any physical thing that is connected to internet or exchanging information or data between internet and physical device. Arduino Uno is a microcontroller used in IOT. It is used for building digital devices, and interactive objects that can sense and control physical devices. Our smart parking will be implementing using Arduino Uno board for car parking and Ethernet shield to connect parking area with web or internet. By using our automated parking system, a user can save much time for searching free parking space. An infrared sensor has to be employed in each slot to check that a particular plot is vacant or not. Searching smart parking system helps people to search parking space accessible with the ease of IOT automation by supplying parking slot free information. The user can book in advance the parking slot and update the information to the server. Every user has a unique id and password. In case a car in stolen and enter the parking IOT the server checks the database and inform the police.
Psychological correlates of reasons for nonbelief: tolerance of ambiguity, intellectual humility, and attachment

ABSTRACT The process of acquiring beliefs – including the belief that there is no God–is largely implicit. We propose that psychological factors that function largely outside of conscious awareness make different conscious reasons for nonbelief more plausible to different people. The present study focuses on intellectual humility, tolerance of ambiguity and attachment and how they relate to stated reasons for nonbelief. The survey was distributed online to an international network of over 100 atheist, secular, and freethought organisations. As predicted, emotional reasons for nonbelief were positively related to anxious attachment, and intellectual reasons for nonbelief were inversely related to tolerance of ambiguity. We did not find evidence for our hypotheses that intellectual reasons for belief would be related to intellectual humility, or that uncertainty reasons would be related to tolerance of ambiguity, intellectual humility, and avoidant attachment. We unexpectedly found that early socialisation reasons for nonbelief were negatively related to intellectual humility.
Appearance and motion information based human activity recognition

Activity recognition is an essential objective of a smart building system which responds to what is happening in a scene. In this paper, a view invariant activity recognition system is proposed to recognise human actions. Selection of applicable features is made and solutions are proposed to deal with probable challenges including differing views on actions and directionality issues. This paper explores a number of features that can be utilised in action recognition systems and chooses suitable features to mitigate the challenges properly. Motion History Image (MHI) based on historical appearance information is used in combination with local motion vectors which are computed through each iteration sequence of the MHI information using an optical ﬂow algorithm. A multi-view dataset (MuHaVi) and a single view dataset (Weizmann) are used to demonstrate and validate the proposed method. Our method, can detect a wide range of actions in multi-view scenarios and shows competitive performance in comparison with state-of-the-art action classiﬁcation techniques.
Training Strategies for Time Series: Learning for Prediction, Filtering, and Reinforcement Learning

Data driven approaches to modeling time-series are important in a variety of applications from market prediction in economics to the simulation of robotic systems. However, traditional supervised machine learning techniques designed for i.i. d. data often perform poorly on these sequential problems. This thesis proposes that time series and sequential prediction, whether for forecasting, filtering, or reinforcement learning, can be effectively achieved by directly training recurrent prediction procedures rather then building generative probabilistic models. To this end, we introduce a new training algorithm for learned time-series models, Data as Demonstrator (DaD), that theoretically and empirically improves multi-step prediction performance on model classes such as recurrent neural networks, kernel regressors, and random forests. Additionally, experimental results indicate that DaD can accelerate model-based reinforcement learning. We next show that latent-state time-series models, where a sufficient state parametrization may be unknown, can be learned effectively in a supervised way using predictive representations derived from observations alone. Our approach, Predictive State Inference Machines (PSIMs), directly optimizes – through a DaD-style training procedure – the inference performance without local optima by identifying the recurrent hidden state as a predictive belief over statistics of future observations. Finally, we experimentally demonstrate that augmenting recurrent neural network architectures with Predictive-State Decoders (Psds), derived using the same objective optimized by PSIMs, improves both the performance and convergence for recurrent networks on probabilistic filtering, imitation learning, and reinforcement learning tasks. Fundamental to our learning framework is that the prediction of observable quantities is a lingua franca for building AI systems.