Miracle-dz/UniFAR-FTUs huggingface dataset:
Control objectives in open source projects

Many studies on Open Source Software Development (OSSD) have been published in the past years. In these studies, OSSD has received positive comments and is proposed as a new way of developing software. We investigate how project management is used in Open Source Software (OSS) projects to exercise control of development activities. Project management in OSSD is worth studying, because research has shown that project management is a critical success factor in traditional software development. We use the COBIT framework to audit project management practices commonly found in OSS projects with the aim of determining which practices are currently missing or can be further improved upon. The framework identifies several potential threats to the long term continuity of OSS projects.
Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks

Accurate land use/land cover classification of synthetic aperture radar (SAR) images plays an important role in environmental, economic, and nature related research areas and applications. When fully polarimetric SAR data is not available, single- or dual-polarization SAR data can also be used whilst posing certain difficulties. For instance, traditional Machine Learning (ML) methods generally focus on finding more discriminative features to overcome the lack of information due to single- or dual-polarimetry. Beside conventional ML approaches, studies proposing deep convolutional neural networks (CNNs) come with limitations and drawbacks such as requirements of massive amounts of data for training and special hardware for implementing complex deep networks. In this study, we propose a systematic approach based on sliding-window classification with compact and adaptive CNNs that can overcome such drawbacks whilst achieving state-of-the-art performance levels for land use/land cover classification. The proposed approach voids the need for feature extraction and selection processes entirely, and perform classification directly over SAR intensity data. Furthermore, unlike deep CNNs, the proposed approach requires neither a dedicated hardware nor a large amount of data with ground-truth labels. The proposed systematic approach is designed to achieve maximum classification accuracy on single and dual-polarized intensity data with minimum human interaction. Moreover, due to its compact configuration, the proposed approach can process such small patches which is not possible with deep learning solutions. This ability significantly improves the details in segmentation masks. An extensive set of experiments over two benchmark SAR datasets confirms the superior classification performance and efficient computational complexity of the proposed approach compared to the competing methods.
Evolução tectônica de rift para margem passiva da faixa Paraguai

73 Introduction . 73 Regional Geology . 75 Material and Methods . 80 Results . 82 Magnetic Domains . 82 Gravimetric Domains . 83 Discussion . 85 Rift phase – Opening of Paraguay Basin . 86 Shortening phase of the Basin – Compressional Stage of the Basin . 89
Taking a Stand: Community-Engaged Scholarship on the Tenure Track

This article assesses the journey to tenure among higher education faculty whose scholarship focuses on community engagement. It provides examples for two categories of action—contextual interventions and structural interventions—that agents of the university enact in order to create space for their approach to scholarship. It also describes structural transformation, which is the product of strategically conceived and deployed structural interventions that fundamentally alter university reward structures and culture so as to promote and support community-engaged scholarship. Finally, this piece describes a contextual intervention by the author that has allowed him to work within local communities while meeting standards of research and teaching that move him toward tenure.
Future changes in five extreme precipitation indices in the lowlands of Romania

This paper presents the results of a study focused on the projected changes in extreme precipitation indices in Romania during three future periods: 2021–2040, 2041–2070, and 2071–2100. We investigated changes in future climate based on historical and modelled data sets of daily precipitation. We compared the values calculated for the three future periods with those obtained for the historical reference period 1961–1990. The historical observation data recorded at 30 weather stations and data extracted from six regional climate model outputs (ALADIN53, CCLM4‐8‐17, RACMO22E, RCA4, REMO2009, and WRF331F) under moderate (RCP4.5) and pessimistic (RCP8.5) scenarios were employed. Five indices established by the Expert Team for Climate Change Detection Monitoring and Indices were analysed: consecutive dry days, consecutive wet days, heavy precipitation days, very heavy precipitation days, and annual total wet‐day precipitation. The series of indices were generated using ClimPACT2 software. Based on bias correction methods, the quality of the modelled data considerably increased, and the errors obtained for the daily precipitation varied in the range of ±0.05 to ±1.22 mm/day. The main findings revealed an increase in three of the extreme precipitation indices based on all models considered compared to the historical period. The most important increase (more than 50%) compared to the historical period was detected in the frequency of very heavy precipitation days and the annual total wet‐day precipitation. They were followed by the heavy precipitation days index. The consecutive dry days index indicated mainly no change or a slow decrease, while inconsistencies among the results of regional climate models were detected for the consecutive wet days index. For all indices except the consecutive dry days index, the ALADIN53 and WRF331F models provided the highest projected values for both scenarios. The dispersion of values obtained as model output for each location was higher for RCP8.5 than for RCP4.5.
Stabilization of Soft Soil by Incinerated Sewage Sludge Ash from Municipal Wastewater Treatment Plant for Engineering Construction

Effective Management of Municipal Solid Waste cannot be achieved without involving wastewater treatment plants as they generate sludge that must be disposed of in an environmentally friendly manner. Therefore, recycling or reusing them are the preferred options for sustainable development. The study presented the use of incinerated sewage sludge ash (ISSA) as a soil stabilizing agent. Oxide compositions were determined by the X-Ray Fluorescence (XRF), X-Ray Diffraction (XRD) and Toxicity Characteristic Leaching Procedure (TCLP). Result showed that ISSA contains Silicon oxide, SiO2 (61.42 %), Aluminum oxide Al2O3 (23.51 %) and Iron oxide, Fe2O3 (4.24 %) in high proportion. Clay soil with low to medium plasticity (CL) from an A-7-6 group was replaced with 0 % 3 %, 5 %, 7 % and 10 % ISSA. Test such as California Bearing Ratio (CBR), Unconfined Compressive Strength (UCS), Compaction and Atterberg limit were conducted. Soil obtained lowest OMC and highest MDD values both at 7% ash content. PI dramatically reduced at short extension of curing age from 21 % to 7 % at 10 % ISSA content thereby improving it from category A7 to A2. With 2.25 % at 3 days realized as the maximum value for resistance to loss in strength, durability requirement is satisfied. ISSA effectively raised CBR values of soil from 15.6 % to 19.5 %, 32.6 %, 47.9 % and 46.4 % respectively with 7 % ISSA additive yielding best result. Therefore, the study concludes that 7 % ISSA additive effectively enhance the strength of soft soils.
A basic building block approach to CMOS design of analog neuro/fuzzy systems

Outlines a systematic approach to design fuzzy inference systems using analog integrated circuits in standard CMOS VLSI technologies. The proposed circuit building blocks are arranged in a layered neuro/fuzzy architecture composed of 5 layers: fuzzification, T-norm, normalization, consequent, and output. Inference is performed by using Takagi and Sugeno's (1989) IF-THEN rules, particularly where the rule's output contains only a constant term-a singleton. A simple CMOS circuit with tunable bell-like transfer characteristics is used for the fuzzification. The inputs to this circuit are voltages while the outputs are currents. Circuit blocks proposed for the remaining layers operate in the current-mode domain. Innovative circuits are proposed for the T-norm and normalization layers. The other two layers use current mirrors and KCL. All the proposed circuits emphasize simplicity at the circuit level-a prerequisite to increasing system level complexity and operation speed. A 3-input, 4-rule controller has been designed for demonstration purposes in a 1.6 /spl mu/m CMOS single-poly, double-metal technology. We include measurements from prototypes of the membership function block and detailed HSPICE simulations of the whole controller. These results operation speed in the range of 5 MFLIPS (million fuzzy logic inferences per second) with systematic errors below 1%. <<ETX>>
STATIC DICTIONARY FOR PRONUNCIATION MODELING

Speech Recognition is the process by which a computer can recognize spoken words. Basically it means talking to your computer and having it correctly recognize what you are saying. This work is to improve the speech recognition accuracy for Telugu language using pronunciation model. Speech Recognizers based upon Hidden Markov Model have achieved a high level of performance in controlled environments. One naive approach for robust speech recognition in order to handle the mismatch between training and testing environments, many techniques have been proposed. some methods work on acoustic model and some methods work on Language model. Pronunciation dictionary is the most important component of the Speech Recognition system. New pronunciations for the words will be considered for speech recognition accuracy
PDE-based geophysical modelling using finite elements: examples from 3D resistivity and 2D magnetotellurics

We present a general finite-element solver, escript, tailored to solve geophysical forward and inverse modeling problems in terms of partial differential equations (PDEs) with suitable boundary conditions. Escript's abstract interface allows geoscientists to focus on solving the actual problem without being experts in numerical modeling. General-purpose finite element solvers have found wide use especially in engineering fields and find increasing application in the geophysical disciplines as these offer a single interface to tackle different geophysical problems. These solvers are useful for data interpretation and for research, but can also be a useful tool in educational settings. This paper serves as an introduction into PDE-based modeling with escript where we demonstrate in detail how escript is used to solve two different forward modeling problems from applied geophysics (3D DC resistivity and 2D magnetotellurics). Based on these two different cases, other geophysical modeling work can easily be realized. The escript package is implemented as a Python library and allows the solution of coupled, linear or non-linear, time-dependent PDEs. Parallel execution for both shared and distributed memory architectures is supported and can be used without modifications to the scripts.
Effects of Selenized DC Sputtered Precursor Stacking Orders on the Properties of Cu2ZnSnSe4 Absorber Layer for Thin Film Solar Cells

This study investigated the effects of selenized DC sputtered precursor stacking orders on the structural, electrical and optical properties of copper zinc tin selenide (CZTSe) absorber layer for thin film solar cells. The precursors were deposited sequentially on the soda lime glass substrate at room temperature by DC magnetron sputtering. The precursors were selenized by annealing in an atmosphere containing selenium pellets and nitrogen gas. The structural, morphological, optical and electrical properties were respectively characterized using X-Ray Diffractometer (XRD), atomic force microscopy (AFM), UV/VIS/NIR lambda 9/19 spectrophotometer, Hall Effect measurement system (HMS 3000) and a four point probe system. CZTSe samples made up of three different stacking orders with configuration Mo/Cu/Zn/Sn (stack A), Mo/Cu/Sn/Zn (stack B) and Mo/Zn/Sn/Cu (stack C) were prepared. All the films showed kesterite peaks with major peak oriented along (112) plane at an angle of 2 ≈ 27.2°. Stack C was a best stacking order since it had the largest grain size 67 nm, highest carrier concentration 3.4 1020 cm–3 and the lowest sheet resistance of 6.4 /□. Also this film demonstrated higher average absorption coefficient of about 6.84 104 cm–1. Furthermore, the films were observed to have a smooth surface and well compacted with average surface roughness of about 16 nm without voids.
What Does a Horgous Look Like? Nonsense Words Elicit Meaningful Drawings

To what extent do people attribute meanings to "nonsense" words? How general is such attribution of meaning? We used a set of words lacking conventional meanings to elicit drawings of made-up creatures. Separate groups of participants rated the nonsense words and the drawings on several semantic dimensions and selected what name best corresponded to each creature. Despite lacking conventional meanings, "nonsense" words elicited a high level of consistency in the produced drawings. Meaning attributions made to nonsense words corresponded with meaning attributions made by separate people to drawings that were inspired by the name. Naïve participants were able to recover the name that inspired the drawing with greater-than-chance accuracy. These results suggest that people make liberal and consistent use of non-arbitrary relationships between forms and meanings. OPEN RESEARCH BADGES: This article has been awarded Open Materials and Open Data badges. All materials and data are publicly accessible via the Open Science Framework at https://osf.io/8juyc/. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.
Adaptive Trajectory Tracking Control using Reinforcement Learning for Quadrotor

Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER) and Return Weighted Regression (RWR) are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.
Improved decoding with the SOVA in a parallel concatenated (Turbo-code) scheme

We show that the soft output of the soft-output Viterbi-decoder (SOVA) suffers from two distortions. Firstly, for bad channels the reliability information of the decoder output is too optimistic. The output can be considered as being multiplied by a factor, that depends on the current bit-error-rate (BER). To become closer to the true log-likelihood ratio the output has to be normalized. Secondly, the soft-output of the SOVA-when used in Turbo decoding-is effected by a correlation between the so called extrinsic and intrinsic information. Since the extrinsic information is fed forward to be the a-priori information in the next decoding stage and is treated as being uncorrelated with the systematic-information, a correcting term has to be introduced in order to compensate this correlation. We show how these distortions can be corrected and we apply the correcting measures on the SOVA that is used as component decoder of a Turbo-code scheme. Finally, we provide simulation results that illustrate the gain due to normalizing the extrinsic information and exploiting the correlation in the soft output.
Comprehensive self-consistent analysis of oxide-confined vertical-cavity surface-emitting lasers

We consider comprehensive description of vertical cavity surface emitting laser (VCSEL) structures in practical cylindrically symmetric geometry. A complete drift-diffusion model for carrier transport in a multilayer semiconductor laser heterostructure including p-n junction is developed. We evaluate the impact of interface grading in distributed Bragg reflectors (DBRs), modulation doping of the DBRs and surrounding layers of the quantum well (QW) as well as drastically material–dependent carrier mobility and recombination constants. Solution of the drift–diffusion model yields spatial profiles of the nonequilibrium carrier concentrations and current. The model is applied as an example to an oxideconfined GaAs/AlGaAs VCSEL. We address both depletion and diffusion capacitance of the device and show that both contributions to the capacitance as well as the differential series resistance critically depend on the injection current and the VCSEL chip design such that, in general, VCSEL cannot be properly modeled by an equivalent circuit approximation. We consider current injection into the aperture region and illustrate the current crowding effects, which result in substantial enhancement of the current density at the tips of the oxide aperture. The effective RC-product can be kept low at a small size of the oxide-confined aperture but only in case where the diameter of the VCSEL mesa is also small.
A case study of the sentence final particle “BA” ’s pragmatic functions in chinese-speaking children

The process of the language acquisition of children is reflected in two aspects; language structure and pragmatic functions. Data from "The Longtime Tracing Oral Corpus of Typical Development Children" (one child; 2,367 sentences) are analyzed. This study examines the child’s sentence final particle (SFP) “ba”, in which we focused on the intention of this word and concluding with the core pragmatic meaning and divided the different levels of “ba”. In this way, we combined the different sentence types, the intentions of utterance, and the degrees of “undetermined intention” and researched on the pragmatic function of child’s SFP “ba” in order to obtain the pragmatic acquisition of “ba” at the specific development period (1;12-4;01). The acquisition and development of children's language is the result of a combination of biological factors, discourse input, and cognitive ability. Finally, the enlightenment of the research conclusions on early childhood education are pointed out.
Design of Highway Construction Mixing Materials Ratio Based on Genetic Algorithm

The manual work or Excel based material mixing ratio method has low computer application level and lays high computer capacity on design construction workers. It is difficult to obtain the most optimal technical and economic program from a number of optional programs. The paper built mathematical model of materials mixing ratio based on genetic and identified constraint conditions. Data pre-processing was performed according to the constructed model. According to specific circus of material mixing ratio calculation, the fitness function and various operators were designed and parameters of model were also configured. At the same time of meeting requirements of project quality, multiple target optimizations were considered to give new model and constraints. The highway construction mixing materials ratio calculation based on genetic algorithm achieves expected goal and improve accuracy and efficiency of operations.
Dynamic Performance Prediction For Scalable Parallel Computing

Performance prediction is necessary in order to deal with multi-dimensional performance effects on parallel systems. The compiler-generated analytical model developed in this paper accounts for the effects of cache behavior, CPU execution time and message passing overhead for real programs written in high level data-parallel languages. The performance prediction technique is shown to be effective in analyzing several non-trivial applications as the problem size and number of processors vary. We leverage technology from the maple symbolic manipulation system and the S-PLUS statistical package in order to present users with critical performance information necessary for performance devugging, architectural enhancement and procurement of parallel systems. The usability of these results is improved through specifying confidence intervals as well as predicted execution times for parallel applications. Cost optimal analysis techniques are also developed using the symbolic equations for execution time.
Smart Travel Guide for Android-Enabled Devices

This is an era of smartphones. Nowadays Smartphones usage has become quite popular among the masses. There are quite a lot of android applications available in the market almost in every field. So this paper intents to develop an android app that acts as a smart tourist guide for the users. In Traditional methods, a problem is seen that the travellers do not get exact information while travelling and also the information is not provided on time. The app overcome this drawback by providing convenience to the user while travelling. The app serves following purposes: User can find a companion to share the vehicle(car-pooling),share GPS location and track location of person in friendlist, decide between paths suggested by the application on basis of time, distance, find help in case of any emergency, help the user when he/she is in unknown region ,maintain history of travelling.The app aims to provide detailed Maps and GPS location tracking so users can understand better and can take proper decisions. The travellers can communicate with each other by sending notifications of their GPS location through SMS to other users. The app works based on the principle of Near Neighbor Join algorithm to calculate distance between two nodes and to a given input node finding a set of nearest nodes using a join function.
SPEECH REHABILITATION PLATFORM FOR PARKINSON'S DISEASE

Around 90% of patients with Parkinson's disease (PD) experience speech disorders. These can be improved effectively by speech rehabilitation, which can be divided into two parts: hospital treatment and home practice. Home practice is a key factor, but because the quality of home practice is difficult to determine, its treatment effect is poor in most PD patients. This study deployed a platform for assisting the home speech practice of PD patients aimed at ensuring the quality of home practice. The proposed model of home rehabilitation is also suitable for other types of rehabilitation, such as for patients with stroke, orthopedic diseases, or neuromuscular diseases.
Umgang mit Spannungen und Krisen in der therapeutischen Beziehung: Erste Erfahrungen mit einem handlungsorientierten Ausbildungs- und Supervisionskonzept

Zusammenfassung Ziele Das allianzfokussierte Training (AFT) 1 schult therapeutische Kompetenzen, die für den Umgang mit Spannungen und Krisen in der therapeutischen Beziehung hilfreich sind unter Einsatz von Videoaufzeichnungen und Rollenspielen. Ziel der von der Heigl-Stiftung geförderten Pilotstudie war es, erste klinische Erfahrungen und Ergebnisse mit AFT im deutschsprachigen Raum vorzustellen und eine RCT-Folgestudie vorzubereiten. Methoden 7 Ausbildungskandidaten nahmen teil. Therapien von 15 Patienten mit depressiver Störung wurden analysiert. Ergebnisse und Schlussfolgerung AFT wurde als sehr hilfreich für die professionelle Entwicklung und den Umgang mit Spannungen und Krisen erlebt. Die therapeutische Kompetenz verbesserte sich signifikant im Selbsterleben und aus Beobachterperspektive. Die Ergebnisse sprechen dafür, dass AFT eine vielversprechende Möglichkeit zur Verbesserung der Psychotherapieausbildung ist und sich die Durchführung eines RCT zum Nachweis der Wirksamkeit lohnt.