Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Economic Inequality – A Socio-Ecological Perspective on Consumption

We live in times of multiple crisis characterised in particular by social and environmental problems. Within the social dimension, we find especially rising economic and social inequalities unfolding since the beginning of the 1980ies (Atkinson 2015; Piketty 2014). In terms of environmental challenges, we observe that environmental pressures are putting our planet more and more at risk and resulting in ecosystem changes (Steffen et al. 2015).
Learning Speaker Aware Offsets for Speaker Adaptation of Neural Networks

In this work, we present an unsupervised long short-term memory (LSTM) layer normalization technique that we call adaptation by speaker aware offsets (ASAO). These offsets are learned using an auxiliary network attached to the main senone classifier. The auxiliary network takes main network LSTM activations as input and tries to reconstruct speaker, (speaker,phone) and (speaker,senone)-level averages of the activations by minimizing the mean-squared error. Once the auxiliary network is jointly trained with the main network, during test time we do not need additional information for the test data as the network will generate the offset itself. Unlike many speaker adaptation studies which only adapt fully connected layers, our method is applicable to LSTM layers in addition to fully-connected layers. In our experiments, we investigate the effect of ASAO of LSTM layers at different depths. We also show its performance when the inputs are already speaker adapted by feature space maximum likelihood linear regression (fMLLR). In addition, we compare ASAO with a speaker adversarial training framework. ASAO achieves higher senone classification accuracy and lower word error rate (WER) than both the unadapted models and the adversarial model on the HUB4 dataset, with an absolute WER reduction of up to 2%.
Automatic Detection of Nutritional Deficiencies In Coffee Tree Leaves Through Shape And Texture Descriptors

Nutritional deficiencies in coffee plants affect production and therefore it is important its early identification. The current research is focused on the automatic identification of nutritional deficiencies of Boron (B), Calcium (Ca), Iron (Fe) and Potassium (K), by using shape and texture descriptors in images of coffee tree leaves. After the acquisition of images containing coffee tree leaves, they are subjected to a segmentation process using Otsu’s method. Afterwards, for the resulting images they are applied the descriptors Blurred Shape Model (BSM) and Gray-Level Co-occurrence Matrix (GLCM) for extracting characteristics of shape and texture. Finally, the obtained image representation is used for training KNN, Naïve Bayes and Neural Network classifiers by using the extracted features, in order to infer the type of deficiency presented in each analyzed image. The experimental results show that the developed procedure has a high accuracy, being the better results associated to the identification of Boron (B) and Iron (Fe) deficiencies. Subject categories and descriptors [H.2. 4 Systems]: Textual databases; [H.5 Information Interfaces And Presentation]: [I.2. 7 Natural Language Processing]: Text analysis General Terms Textutal Data, Data Processing
Genome-Wide Characterization and Expression Profiling of Squamosa Promoter Binding Protein-like (SBP) Transcription Factors in Wheat (Triticum aestivum L.)

Transcription factors (TFs) play fundamental roles in the developmental processes of all living organisms. Squamosa Promoter Binding Protein-like (SBP/SBP-Box) is a major family of plant-specific TFs, which plays important roles in multiple processes involving plant growth and development. While some work has been done, there is a lot more that is yet to be discovered in the hexaploid wheat SBP (TaSBP) family. With the completion of whole genome sequencing, genome-wide analysis of SBPs in common hexaploid wheat is now possible. In this study, we used protein–protein Basic Local Alignment Search Tool (BLASTp) to hunt the newly released reference genome sequence of hexaploid wheat (Chinese spring). Seventy-four TaSBP proteins (belonging to 56 genes) were identified and clustered into five groups. Gene structure and motif analysis indicated that most TaSBPs have relatively conserved exon–intron arrangements and motif composition. Analysis of transcriptional data showed that many TaSBP genes responded to some biological and abiotic stresses with different expression patterns. Moreover, three TaSBP genes were generally expressed in the majority of tissues throughout the wheat growth and also responded to many environmental biotic and abiotic stresses. Collectively, the detailed analyses presented here will help in understanding the roles of the TaSBP and also provide a reference for the further study of its biological function in wheat.
DEVELOPMENT OF AN EXPERIMENTAL TEST SETUP FOR IN SITU STRAIN EVALUATION DURING SELECTIVE LASER MELTING

Selective Laser Melting (SLM) is an Additive Manufacturing (AM) process which still underlies a lack of profound process understanding. This becomes obvious when deformation and crack formation can be observed in SLM parts due to residual stresses. Controlling residual stresses is therefore an important topic of recent research in AM of metals. In order to minimize residual stresses further knowledge considering their cause and physical correlations of process parameters needs to be generated. In this paper an approach of measuring strains layer by layer during the SLM process by means of in situ X-ray diffraction is presented. For this purpose an experimental test setup is being constructed at the Technische Universität Berlin. The system requirements and operating principles are discussed in this paper. Furthermore, details of the current progress of the construction are highlighted.
Combinatorial Robustness Testing with Negative Test Cases

Error-handling is an important means to improve the robustness of a system and testing error-handling is crucial to ensure its correctness. In this paper, we argue that error-handling leads to input masking which requires special treatment in for combinatorial testing. Therefore, we propose an extension to combinatorial testing including a robustness fault model and robustness combination strategy. We also provide an evaluation which compares its efficiency to normal combinatorial testing.
Calibrating DNN Posterior Probability Estimates of HMM/DNN Models to Improve Social Signal Detection from Audio Data

To detect social signals such as laughter or filler events from audio data, a straightforward choice is to apply a Hidden Markov Model (HMM) in combination with a Deep Neural Network (DNN) that supplies the local class posterior estimates (HMM/DNN hybrid model). However, the posterior estimates of the DNN may be suboptimal due to a mismatch between the cost function used during training (e.g. frame-level crossentropy) and the actual evaluation metric (e.g. segment-level F1 score). In this study, we show experimentally that by employing a simple posterior probability calibration technique on the DNN outputs, the performance of the HMM/DNN workflow can be significantly improved. Specifically, we apply a linear transformation on the activations of the output layer right before using the softmax function, and fine-tune the parameters of this transformation. Out of the calibration approaches tested, we got the best F1 scores when the posterior calibration process was adjusted so as to maximize the actual HMM-based evaluation metric.
A Novel Method for ECG Paper Records Digitization

Most of the ECG test recordings obtained from patients in clinic is in paper records. It is difficult for efficient and automatic diagnosis of cardiac diseases based on paper ECG recordings without expert experiences. Thus, the digitization of ECG papers is of great significance for both basic research and clinical purposes. However, most of current methods extracting signals from single lead ECG images are manual, prohibiting their use for large database. This study proposed an algorithm to extract ECG signals automatically from scanned 12 lead ECG paper recordingsThe original ECG recording images were pre-processed by operations including edge detection, image binarization and skew correction. After pre-processing, ECG waveforms were extracted from background grids based on Connected Component Analysis (CCA). For waveform segmentation, horizontal projection was applied to obtain segmentation boundaries. The ECG signal trace was then traversed to extract ECG signal time series. The extracted signal was plotted by using MATLAB as final ECG signal graphThe proposed algorithm was tested on 129 actual ECG recordings of patients. The results revealed that the extracted signals retained essential features of paper ECG recordings.
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Based on our experiences over the last year running 32K–64K core simulations on Cielo, a Cray XE6, we present strategies that we are using to enable large, long-running simulations to make predictable progress despite platform component failures. From an application perspective, complex systems like Cielo have multiple sources of failures or interrupts that combine to make the system appear unstable to users (file system issues, node dropouts, transient network errors, system time, etc.). As the system experts continue to drive toward the resolution of the root causes of component failures, the application developers and users of the system can mitigate some of the issues by employing scripting mechanisms that trap and identify failures and then recover where possible. We will discuss the component failures that we have experienced and identify those where recovery has been possible. We will also discuss the scripting mechanisms for error trapping and identification, recovery, and re-launch of applications.
The Relationship Between Breastfeeding Self-Efficacy and Depression in the Early Postpartum Period in Turkey

Background: Breastfeeding self-efficacy perception is an impor tant factor that increases mother's breastfeeding success and duration. While the emergence of positi ve situations such as satisfaction or excitement in creases self-efficacy; adverse events such as fatigue, pain , anxiety or depression reduce self-efficacy percep tion. Objectives: In our study, the relationship between mother's po tnatal depression risk status and breastfeeding self-efficacy was evaluated. In addition, the effec t of maternal socio-demographic characteristics and obstetric properties on postpartum depression risk status was determined. Methodology: This research was intended to be performed on a sam ple size of 357 mothers. Research volunteers completed the Postnatal Questionnare, Br astfeeding Self-Efficacy and Edinburgh Postpartum Depression Scale. According to the total score of the EPDS, mothers w hose EPDS scores were 12 or more classified as a risk group and to be referred to re levant units. Results: Mothers whose EPDS scores were 12 or more classifi ed as a risk group and prevalence was found to be 20.2%. In our study, it was determined that women w ith higher breastfeeding self-efficacy scores had l ower rates of depression. There was no significant relat ionship between educational and occupational status , number of children, type of birth, gender of the baby, and depression levels of mothers. Mothers who did not have social support (p=.021), evaluating the income level as ba d (p=.015), unplanned pregnancy (p<.001), and smoki ng during pregnancy (p=.038) were found to have a high risk of depression. Conclusions: It is important that the mother who has low breastf eeding self-efficacy level is evaluated by the health professional for postnatal depression.
Elpis, an Accessible Speech-to-Text Tool

Elpis is a speech-to-text tool which has been designed to give language workers, including linguists and speech scientists, access to cutting-edge automatic speech recognition software, without the specialist training typically required to run these systems. Our presentation would demonstrate local and serverbased versions of Elpis using sample data from the Abui (ISO 639: abz) language, about 17,000 speakers in Indonesia. Attendees would gain a sense of the benefits that a first-pass ASR transcription can bring to transcription workflows.
Measuring a speaker's acoustic correlates of pitch - but which? A contrastive analysis for perceived speaker charisma

Intonation is a multitasker. It conveys a plethora of non-lexical meanings and functions, and mirrors a speaker’s personality traits, one of them being charisma. In the age in which leaders communicate globally through digital mass media, speaker charisma has become an important field of research. The number of different f0 level and range measures that are used, not only in phonetics, to determine speaker charisma call for standardization. Which f0 measures correlate most strongly with perceived speaker charisma and should thus be used in future analyses? Our study addresses this question based on 51 speakers whose charisma was rated by 42 listeners. Results show that the f0 mean is the best parameter to measure perceived charisma in terms of pitch level, while the best pitch distribution measures are kurtosis and the 80-percentile f0 range. The results are discussed in terms of gender differences and issues of cross-linguistic generalization.
Magnetic Resonance Imaging of the Pediatric Brain

That's it, a book to wait for in this month. Even you have wanted for long time for releasing this book magnetic resonance imaging of the pediatric brain; you may not be able to get in some stress. Should you go around and seek fro the book until you really get it? Are you sure? Are you that free? This condition will force you to always end up to get a book. But now, we are coming to give you excellent solution.
Efficient use of acetone extractive of Oroxylum indicum for the improvement of quality of silk in silkworm Bombyx mori ( L . ) ( Race : PM x CSR 2 )

The present study was undertaken to study the influence of acetone extractive of bark of Oroxylum indicum on the cocoon characters; silk filament characters and esterase enzyme activity of silk worm Bombyx mori (L.) (Race: PM x CSR2). The soxhletation was followed for the obtaining acetone extractive of bark of Oroxylum indicum (L). Three concentrations (5ppm; 10ppm and 20ppm) of extractive were prepared. The fifth instar larvae were utilized for the experimentation. Soon after the fourth moult, the fifth instar larvae were grouped into five groups (each with hundred individuals) ( Untreated control; Acetone treated control; 5ppm extractive; 10ppm extractive and 20ppm extractive). Ten microliters of each concentration of extractives were topically applied to respective group to the individual larva at 48 hours after the fourth moult. The larvae were maintained through standard schedule. Ten larvae from each group were utilized for analysis of electrophoretic esterase pattern on fifth day. The silk worms were sacrificed, haemolymph and silk gland samples were isolated and analyzed by using 7.5% of native gel electrophoresis. Acetone extractives of O.indicum at 5 ppm, 10 ppm and 20 ppm concentrations recorded maximum cocoon weight (1.95,1.84,1.76 gm), shell weight (0.40, 0.35, 0.33gm), pupal weight (1.55, 1.49,1.43). All concentrations of the bark extract of O.indicum recorded higher cocoon, shell, and pupal weight than the control (untreated and acetone treated). There was a gradual increase in the silk yield with an increase in the concentrations of bark extracts O. indicum from 5 ppm, 10 ppm and 20 ppm concentration of Oroxylum indicum (L). Efficient use of acetone extractives of Oroxylum indicum (L) may open a new avenue in the silk yield.
WHAT DO MEASURES OF TAX AGGRESSIVENESS MEASURE ?

MEASURING TAX AGGRESSIVENESS IS AN important issue facing empirical tax researchers. We conduct a comprehensive review and analysis of various proposed measures of this construct. We examine nine tax aggressiveness measures and explore whether these measures capture the same, or at least similar, underlying constructs, yielding similar inferences regarding fi rms’ levels of tax aggressiveness. Our work, in conjunction with Hanlon and Heitzman (2010), is designed to provide guidance to future tax aggressiveness research. The conceptual challenge in this stream of research is the lack of a universal defi nition of tax aggressiveness. Chen et al. (2010, p. 1) defi ne tax aggressiveness as “downward management of taxable income through tax planning activities. ” Alternatively, Frischmann et al. (2008, p. 265) more narrowly defi ne tax aggressiveness as the act of “engaging in signifi cant tax positions with relatively weak supporting facts. ” Lisowsky et al. (2010) view tax aggressiveness as actions close to the end of a continuum of tax avoidance activities that range from legitimate tax planning to investments in abusive tax shelters. The tax aggressiveness measures we examine are as follows.1 Four are variants of effective tax rate (ETR) ratios in which the numerator contains a measure of tax burden and the denominator represents the ability to pay tax. Two measures are based solely on book-tax differences. The three remaining measures are based on the estimates of econometric models designed to capture “abnormal” tax planning behavior that may be more refl ective of aggressiveness. We fi rst defi ne the measures and discuss how each has been used in prior studies. Then, we examine how these measures relate to both one another and to fi rm characteristics.
Ultra-low-power RRAM-based FPGA: A Road towards Reconﬁgurable Edge Computing

—The trends in Internet-of-Thing (IoT) require specialized hardware systems to be more computing capable than ever while at the same time satisfying an ultra-low power budget. Field Programmable Gate Arrays (FPGAs), thanks to their recon-ﬁgurable nature, have been an ubiquitous media in many edge computing systems. However, low-power FPGAs generally suffers from large delay degradation (up to 2 × ), missing to achieve the computing capability required by many modern edge computing applications. In this paper, we investigate the opportunity of using Resistive Random Access Memories (RRAMs) in ultra-low-power FPGA architectures. We (i) evaluate the circuit design aspects of RRAM-based routing multiplexers; (ii) introduce a novel design ﬂow to accurate analyze FPGA architectures; and (iii) study the opportunity of building near-V t RRAM-based FPGA. Full-chip layouts and SPICE simulations present that at nominal operating voltage, RRAM-based FPGAs can improve up to 8%/22%/16% in area/delay/power, as compared to SRAM-based counterparts. Compared to SRAM-based FPGAs working at its nominal voltage, a near-V t RRAM-based FPGAs can outperform by about 2 × the Energy-Delay Product without delay overhead.
TOWARD A TOOL TO OPTIMIZE THE DAYLIGHTING INTO THE PROCESS OF 1 ARCHITECTURAL DESIGN – REVERSE APPROACH – 2 3

This paper traces the various steps of reasoning and development of a numerical model of dimensioning opening-elements based on the reverse approach of simulation of natural lighting. The model was developed by following two steps: firstly the calculation of the external light coming from a sky-element located in the celestial dome. Based on these data, a second numerical calculation was performed to generate the dimensions of an opening-element enabling to realize the daylight intentions of the designer. A method of comparative validation using standard softwares of daylight simulation and daylight optimization was subsequently adopted to test the reliability of the obtained values. Having regard to the pedagogical destination of the tool, the results of this research showed the opportunities that may offer a prospective integration of the developed model in the students’ awareness about the importance of daylight simulation. Therefore optimize the visual comfort and the energy efficiency inside the future buildings.
The I2R's Submission to VOiCES Distance Speaker Recognition Challenge 2019

This paper is about the I2R’s submission to the VOiCES from a distance speaker recognition challenge 2019. The submissions were based on the fusion of two x-vectors and two i-vectors subsystems. Main efforts have been focused on the frontend de-reverberation processing, PLDA backend design, score normalization and fusion studies in order to improve the system performance on single channel distant/farfield audio, under noisy conditions. We contribute to the fixed condition task under specific training and development data set. The experimental results showed that the de-reverberation approach can achieve 5% to 10% relative improvement on both EER and DCF for all subsystems and more than 10% improvement in the final fusion system on the Dev dataset and more than 15% relative improvement on the final evaluation dataset. Our final fusion system achieved about 2% EER rate and 0.240 minDCF on the Development Dataset.
Investigation on the Explosion-Driven Dispersion and Combustion of Aluminium Particles

Shock wave dispersion and ignition of solid combust i le particles is a fundamental problem of great importance for various applications. For example, m tallized explosives are designed and produced to generate specific effects different from those obta ined with homogeneous ones, such as delayed energy release; but their exact working mechanism r e ains roughly known. Previous studies at ISL [1], [2] were carried out on charges made of a mixt ure of liquid explosive saturated with aluminium particles, which were exploded in a semi-confined a rea. The goal was to investigate the global mechanisms involved during the explosion of thermob aric charges. The charges studied at that time where quite similar in formulation, shape and mass to those used in some military devices. Interesting results concerning that kind of heterogeneous explo sives have been also reported by Frost et al. [3]. Zhang et al. [4] also conducted studies on particle dispersion by an explosive. They worked on packed beds of solid particles saturated with a liquid exp losive. Neuwald et al. [5] have studied explosion driven dispersion of aluminium particles on a much smaller scale using a micro-calorimeter. Aluminium particles, on account of their particular physical and chemical properties are a privileged candidate for this domain of applications. Problem of detonation initiation in aluminium suspensions has been reviewed for example by Veyssière [6]. The purpose of the present work is to get a better understanding of the mechanisms of particle dispers ion and ignition by an explosive.