Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust machine learning methods for computational paralinguistics and multimodal affective computing (Hesaplamasal paralinguistik ve çok-kipli duyuşsal hesaplama için gürbüz yapay öğrenme yönemleri)

ROBUST MACHINE LEARNING METHODS FOR COMPUTATIONAL PARALINGUISTICS AND MULTIMODAL AFFECTIVE COMPUTING The analysis of affect (e.g. emotions or mood), traits (e.g. personality), and social signals (e.g. frustration, disagreement) are of increasing interest in human computer interaction, in order to drive human-machine communication to become closer to human-human communication. It has manifold applications ranging from intelligent tutoring systems to affect sensitive robots, from smart call centers to patient telemonitoring. The study of computational paralinguistics, which covers the analysis of speaker states and traits, faces with real life challenges of inter-speaker and inter-corpus variability. In this thesis, machine learning methods addressing these challenges are targeted. Automatic model selection methods are explored for modeling high dimensional paralinguistics data. These approaches can deal with different sources of variability in a parametric manner. To provide statistical models and classifiers with a compact set of potent features, novel feature selection methods based on discriminative projections are introduced. In addition, multimodal fusion techniques are sought for robust affective computing in the wild. The proposed methods and approaches are validated over a set of recent challenge corpora, including INTERSPEECH Computational Paralinguistics Challenge (2013-2015), Audio-Visual Emotion Challenge (2013/2014), and Emotion Recognition in the Wild Challenge 2014. The methods proposed in this thesis advance the state-of-the-art in most of these corpora and yield competitive results in others, while enjoying the properties of parsimony and computational efficiency.
Parameter sensitivity of a distributed enhanced temperature-index melt model

Abstract We investigate the sensitivity of a distributed enhanced temperature-index (ETI) melt model, in order to understand which parameters have the largest influence on model outputs and thus need to be accurately known. We use melt and meteorological data from two Alpine glaciers and one glacier in the Andes of Chile. Sensitivity analysis is conducted in a systematic way in terms of parameters and the different conditions (day, night, clear-sky, overcast), melt seasons and glaciers examined. The sensitivity of total melt to changes in individual parameters is calculated using a local method around the optimal value of the parameters. We verify that the parameters are optimal at the distributed scale and assess the model uncertainty induced by uncertainty in the parameters using a Monte Carlo technique. Model sensitivity to parameters is consistent across melt seasons, glaciers, different conditions and the daily statistics examined. The parameters to which the model is most sensitive are the shortwave-radiation factor, the temperature lapse rate for extrapolation of air temperature, the albedo parameters, the temperature threshold and the cloud transmittance factor parameters. A parameter uncertainty of 5% results in a model uncertainty of 5.6% of mean melt on Haut Glacier d’Arolla, Switzerland.
The Size Distribution of Cities: An Examination of the Pareto Law and Primacy - eScholarship

THE SIZE DISTRIBUTION OF CITIES: AN EXAMINATION OF THE PARETO LAW AND PRIMACY by Kenneth T. Rosen Associate Professor Schooi of Business Administration University of California, Berkeiey with Mitchei Resnick Not to be quoted without permission of the authors. Juiy 1979
Integrated Deep Learning Based Segmentation And Classification Method For Boundary Delineation Of Agricultural Fields In Multitemporal Satellite Images

The multi-temporal satellite data gives the data in periodic basis which helps for continuous monitoring, but due to the earth’s rotation, climatic changes, sensor characteristics, etc. There are too many distortions and noises which have to be removed before further processing for getting better results. In this study, a multispectral segmentation method for automated delineation of agricultural field boundaries in remotely sensed images is presented. In most image semantic segmentation tasks, the Deep Learning has shown its irreplaceable advantages, specifically the U-Net network architecture. With this motivation, in this work proposed a learning-based, simultaneous segmentation and classification method based on the Deep Multi-scale U-Net (DMU-Net) structure with deformable convolutional layers. In addition using cellular automata based Gaussian filtering is used to enhance the multitemporal images, to remove "noise" and to enhance the major direction.The second approach is linear feature extraction based on some parameters such as higher STD, length, strength and contrast. In this way, information from several spectral bands can be used for delineating field borders with different characteristics. Finally the experimental results reveal that the proposed segmentation method is more efficient than the existing segmentation techniques such as EGKL method and field boundary segmentation method in factors of each quantitative overall performance metrics and appropriateness for land-use classification.
Relevance, E � ort, and Perceived � ality: Language Learners’ Experiences with AI-Generated Contextually Personalized Learning Material

Arti � cial intelligence has enabled scalable auto-creation of context-aware personalized learning materials. However, it remains unclear how content personalization shapes the learners’ experience. We developed one personalized and two non-personalized, crowdsourced versions of a mobile language learning app: (1) with personalized auto-generated photo � ashcards, (2) the same � ashcards provided through crowdsourcing
Use of the Critical Period in Reservoir Analysis

The purpose of this study is to define ‘critical period’ in conjunction with reservoir design and operation and then to analyze the usefulness of the concept in the evaluation of deficiencies of water for project planning purposes. Streamflow records from a number of rivers in the United States are analyzed, and certain defined characteristics of their various critical periods are derived. These characteristics are shown to be functions of both the active storage and the degree of variation in annual streamflow. The dates of occurrence of the critical periods from historical records tend to be stable within certain geographic areas and over a range of active storages. A study is made of the significance of the critical periods and their associated droughts respecting their use for design purposes. This is done by generating large numbers of equally likely hydrographs of the same length as the historical record and comparing the critical periods and yields of these synthetic records with those found in the historical record. Some of the synthetic records produced critical periods in which the severity, measured by the decrease in the full record firm yield, was greater than the historical. However, the generated records as a whole had significantly less severity than the historical records of the same length. The critical periods also showed a strong tendency for nesting, i.e., the larger critical period includes within its duration the smaller critical periods over a wide range of storage and target draft. These observations suggest, but do not prove, that the more severe critical periods observed in historical records may be due to major perturbations of the cause-effect relationships not reflected by the hypothesis of static probability relationship (i.e., constant parameters).
Succession Culture-independent Methanogenic Archaeal from Manure Compost

Ribotyping analysis combining with denaturing gradient gel electrophoresis (DGGE) have been successfully used and described the biodiversity of microbes especially archaea from cattle manure. DGGE profiles of the archaea methanogens based on culture-independent showed variation patterns from early mesophilic to maturation phases. During early mesophilic to thermophilic phases the DGGE bands at the upper part of the gel showed less variation. Meanwhile at the thermophilic phases, the upper bands were disappeared. While at the end of thermophilic phase the DGGE bands showed more variation compared to that in the early of mesophilic phase. However, in the maturation phase only four bands appeared on the middle and bottom of the gel. Detail analysis by comparing the sequence of 16S rRNA gene fragments to the GenBank showed that all the bands were closed to Methanosaeta , uncultured archaeon, and Methanobrevibacter , which belonged to the phylum of Euryarchaeota . Phylogenetic analysis of the 16S rRNA gene sequences showed that all the bands belonged to Methanobacteriales and Methanosarcinales . 18 out of 25 DGGE bands showed that the bands are forming a new cluster at Euryarchaeota based on phylogenetic analysis, where 14 bands were closed to Methanobacteriales and 4 bands are part of Methanosarcinales . Most of Methanosarcinales order were found at thermophilic phase and Methanobacteriales were found in all phases. In addition the organisms were dominant order in both mesophilic and maturation phases.
Vertebrate diversity in a thirty year old analogue forest in Pitigala, Elpitiya, in the Galle District of Southern Sri Lanka

Most of the natural ecosystems in the wet zone are severely fragmented and interspersed between human managed agro ecosystems and home gardens. There is growing evidence that traditional agro-ecosystems contribute to sustain the regional biodiversity of many invertebrate and vertebrate species. Analogue forest as a concept is accepted by agronomists and conservationists, which would bring profits in the long-term sustainable basis. The Bangamukanda Estate is an example of a 18 hectares plantation (tea, rubber and cinnamon) that has been converted into an analogue forest. Objective of the study was to assess the current vertebrate diversity in this 30-year-old analogue forest. Total of 206 species of vertebrates belonging to 74 families were observed during the study period, out of that 58 species were endemic to Sri Lanka. The findings of the survey clearly highlighted the contribution of analogue forest systems towards sustaining a rich biodiversity. In addition analogue forest systems can be used to link the forest patches in the wet zone.
Smallholder milk-quality awareness in Indonesian dairy farms

In most low-and middle-income countries, milk is produced by smallholders, thereby contributing to the livelihood of their households. With the increasing importance of milk production in these countries, it is essential that milk quality is of a high level to ensure a safe product for consumers. It is, however, unclear whether smallholder dairy farmers are aware of the quality of their milk. The aim of this cross-sectional study was to gain insight on Indonesian smallholder dairy farmer awareness of milk quality parameters and to identify factors associated with the total plate count (TPC) and somatic cell count (SCC). A stratified sampling method was used to select smallholder farms in 4 districts in West Java, Indonesia, that were interviewed between August and September 2017. Factors putatively associated with awareness of TPC were investigated with multinomial regression models, whereas a Firth-type logistic regression was applied to identify factors associated with SCC awareness. Of the total 600 farmers surveyed, 264 (44%), 109 (18%), 170 (28%), 111 (19%), and 23 (4%) farmers were aware of TPC, total solid, fat content, milk density, and SCC, respectively, but did not know its value. Those that were conceptually aware of these quality parameters were generally unaware of their value. Furthermore, this study revealed that the following variables were significantly associated with dairy farmers’ awareness of TPC: cooperative to which the farmer belonged, distance to neighboring dairy farmer, technology adoption index, TPC as the most important quality factor for the buyer, milk production information from cooperatives, and cow health information from veterinarians. Similarly, cooperative, dairy business experience, and milk quality test adoption were significantly associated with dairy farmers’ awareness of SCC. Cooperative was the only variable that was significant in both final statistical models. This indicates that cooperatives play an important role in increasing farmer awareness of milk quality parameters in these smallholder dairies. This may be valid for other regions in the world also where milk production is dominated by smallholder dairy farmers.
ImmersiveIML – Immersive interactive machine learning for 3D point cloud classification: the neural network at your fingertips

We propose an initial exploration of an interactive machine-learning (IML) dialogue in immersive, interactive Virtual Reality (VR) for the classification of points in 3D point clouds. We contribute ImmersiveIML, an Immersive Analytics tool which builds on human-machine learning trial-and-error dialogue to support an iterative classification process of points in the 3D point cloud. The interactions in ImmersiveIML are designed to be both expressive and minimal; we designed the iterative process to be supported by (1) 6-DOF controllers to allow direct interaction in large 3D point clouds via a minimal brushing technique, (2) a fast trainable machine learning model, and (3) the direct visual feedback of classification results. This constitutes an iterative human-in-the-loop process that eventually converges to a classification according to human intent. We argue that this approach is a novel contribution that supports a constant improvement of the classification model and fast tracks classification tasks with this type of data, in an immersive scenario. We report on the design and implementation of ImmersiveIML and demonstrate its capabilities with two emblematic application scenarios: edge detection in 3D and classification of trees in a city LiDAR dataset.
Quasi-Stationary Waves in the Southern Hemisphere. Part II: Generation Mechanisms

Abstract In this Part II the authors investigate the role that Antarctic elevations, the rest of the world orography, thermal forcing from lower latitudes, and the transient eddy component of the flow play on the generation of the quasi-stationary wave field in the Southern Hemisphere. An approach based on the UCLA GCM is followed. Results from a control simulation with full orography and from experiments without the Antarctic elevations and without the rest of the world orography, suggest that the quasi-stationary wave with zonal wavenumber 1 (QS-wave 1) around Antarctica is primarily generated by mechanisms other than the Antarctic elevations. Comparison of a three-dimensional Eliassen-Palm flux vector in the control simulation, and those where the Antarctic elevation and the rest of the world orography are removed, suggests that wave activity propagates both from the subtropics and from polar latitudes. Although in qualitative agreement with results of Part I, the horizontal and vertical structure of t.
Modeling and Forecasting the Crude Oil Price in Nigeria

: This work is an attempt to examine empirically the best ARIMA and GARCH models for forecasting. The data employed in this study comprise of 189 monthly observations of crude oil price in Nigeria spanning from January, 1998 to September, 2013. At first the stationary condition of the data series are observed by autocorrelation function (ACF) and partial autocorrelation function (PACF) plots, then checked using Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and Augmented Dickey Fuller (ADF) test statistic. It has been found that crude oil price is non-stationary. After we taking first difference of logarithmic values of data series, the same types of plots and the same types of statistics show that the data is stationary. The best ARIMA and GARCH models have been selected by using the criteria such as AIC, HQC, and SIC. The model for which the values of criteria are smallest is considered as the best model. Hence ARIMA (3, 1, 1) and GARCH (2, 1) are found as the best model for forecasting the crude oil price data series.
Work Related Stress and job Performance of Scrub Nurses in the Operating Room of Selected Governmental Hospitals in Saudi Arabia

: Work stress, which is common in the nursing profession, has been associated with negative outcomes for both nurses and patients. Moreover, limited studies have focused on work stress and job performance among scrub nurses in the operating room (OR). Aim: The aim of this study was to assess levels of work stress and job performance of OR nurses. Methods: A descriptive correlational design was used to examine the association between work stress and demographic characteristics and their effect on job performance. A convenience sample of 124 scrub nurses recruited from two governmental hospitals completed three questionnaires that measured sociodemographic variables, work stress, and job performance. Result: High levels of stress among OR nurses, good levels of job performance, and a negative correlation (r= -.23, p=.05) between work stress levels and job performance were found. The result of this study also indicated strong relationships between work stress and nurse’ age, years of experience, and nationality. Conclusion: Work stress is high among OR nurses and may adversely affect their job performance. Moreover, demographic variables were found to be strongly related to work stress, which need to be considered when evaluating quality of nursing care.
THE DUAL MEDIATING EFFECTS OF JOB STRESS AND PSYCHOLOGICAL BURNOUT BETWEEN EXPERIENCE OF INCIVILITY IN THE WORKPLACE AND CHILD-CARE TEACHERS’ INTENTION TO LEAVE

: Our purpose in this study was to examine the dual mediating effects of job stress and psychological burnout between experience of incivility in the workplace and child-care teachers’ intention to leave. For this purpose, we surveyed 234 child-care teachers in D and S city of Chungcheongnam-do. We analyzed the collected data using SPSS PC+ 25 and PROCESS macro 3.5. We did reliability analysis, frequency analysis, correlation analysis, mean comparison analysis, and dual mediating effect analysis. First, the correlation analysis showed a significant positive correlation between the main variables. Second, path analysis showed that experience of incivility in the workplace increased job stress, psychological burnout, and intention to leave; job stress increased psychological burnout; and psychological burnout increased the intention to leave. Finally, analyzing the dual mediating effect showed that job stress and psychological burnout double mediated in the link between experience of incivility in the workplace and intention to leave. This study will provide a new model for reducing child-care center teachers’ intention to leave.
A COMPREHENSIVE ANALYSIS OF CLASSIFICATION MODELS BASED ON MACHINE AND DEEP LEARNING

Having examined many scientific researches, classification can perceive as one of the prominent techniques which utilize widely in a variety of data mining modes. Acquiring insight such as efficiently of performance on large data sets, robustness in handling variety of data sets with corrupted data, and classification ability with aim of producing valid and new knowledge about the functionality of every classification model can exert very beneficial effects on tasks that are addressed with machine learning users. Inspired by these problems, in the current study we introduced a design of conditional Generative Adversarial Network – Deep Neural Network (cGAN-DNN) architecture for classification of records from two hospital that was collected from patients affected with breast cancer and diabetes. Our experimental results show that the model developed here, can reliably and with robust stability classify the subjects with an accuracy of 98.0% (diabetes) and 99.6% (breast cancer). In the terms of sensitivity, precision, and F1-score the obtained results are 98.4%, 98.5%, and 98.2% (diabetes), 99.7%, 99.4%, and 99.6% (breast cancer), respectively. Besides, the proposed model was validated in the comparison with machine learning approaches, which experimental results claim a high inconsistency among machine-learning based approaches. Regarding obtained results from the experiments and evaluation based on metrics, we can demonstrate that the proposed model can be tuned to utilize into various classification tasks.
Harvesting Idle Memory for Application-Managed Soft State with Midas

Many applications can benefit from data that increases performance but is not required for correctness (commonly referred to as soft state ). Examples include cached data from backend web servers and memoized computations in data analytics systems. Today’s systems generally statically limit the amount of memory they use for storing soft state in order to prevent un-bounded growth that could exhaust the server’s memory. Static provisioning, however, makes it difficult to respond to shifts in application demand for soft state and can leave significant amounts of memory idle. Existing OS kernels can only spend idle memory on caching disk blocks—which may not have the mostutility—becausetheydonotprovidetherightabstractions to safely allow applications to store their own soft state. To effectively manage and dynamically scale soft state, we propose soft memory , an elastic virtual memory abstraction with unmap-and-reconstruct semantics that makes it possible for applications to use idle memory to store whatever soft state they choose while guaranteeing both safety and efficiency. We present Midas, a soft memory management system that contains (1) a runtime that is linked to each application to manage soft memory objects and (2) OS kernel support that coordinates soft memory allocation between applications to maximize their performance. Our experiments with four real-world applications show that Midas can efficiently and safely harvest idle memory to store applications’ soft state, delivering near-optimal application performance and responding to extreme memory pressure without running out of memory.
Screening of Lactic Acid Bacteria as Biocontrol against Colletotrichum musae

In the preliminary study, three hundred and seventy six Lactic Acid Bacterial (LAB) isolates obtained from the various natural sources were morphologically characterized and screened against the fungal pathogen Colletotrichum musae , causal agent of anthracnose of banana. The dual culture assay was employed to determine in vitro antagonistic activity of the LAB isolates. Among these twenty four, LAB isolates inhibited the mycelial growth of the pathogen. Out of twenty four, only two isolates viz. LS-36 and LS-44 exhibited maximum zone of inhibition. Based on 16S rRNA gene sequencing, LS-36 and LS-44 revealed maximum similarity to Lactobacillus brevis .
An Intelligent Framework for CNN-Based Multimodal Sentiment Analysis

There are several social media analytics activities that rely heavily on sentiment analysis of user-generated textual information from the internet. Adding pictures and videos to one's social media posts has become common practice among today's internet users as a means of conveying one's point of view and experiences. Large-scale text and visual data sentiment analysis helps extract user feelings about brands or subjects. An intelligent multi-modal sentiment analysis framework is urgently needed to effectively mine information from several modalities in order to keep up with the explosion of massive multimodal data. Previous research mostly dealt on either textual or visual material. By using a "convolutional neural network" ("CNN") to extract features from several modalities, this study aims to provide a novel framework for multi-modal sentiment analysis. To showcase the sophistication of our models, we conduct "multi-model sentiment analysis" on two publicly accessible “datasets,” “including text,” “audio,” and “visual input. ”