Miracle-dz/UniFAR-FTUs huggingface dataset:
POLITENESS STRATEGIES AND SOCIAL DIMENSIONS IN CHLOÉ ZHAO'S FILM NOMAD LAND: SOCIOPRAGMATICS STUDY

This research aims to identify the most dominant kinds of politeness strategies used in Chloé Zhao's film Nomad land and to analyze the most dominant social dimensions that influence the selection of politeness strategies used in Chloé Zhao's film Nomad land. The method used in this research is a qualitative descriptive method and to get the data needed for this research, the data collection is conducted by choosing and watching the film, reading the film's script, collecting the data, identifying the data, classifying the data, then analyzing the data based on its politeness strategies and social dimensions, and drawing a conclusion related to the results of the data that has been analyzed. The theoretical framework used in this research is the theory of politeness strategies from Brown and Levinson (1987) and the theory of social dimensions from Holmes (2013). The results of this study show that there are 4 politeness strategies found from the 35 data analyzed, the most dominant politeness strategy used in Chloé Zhao's film Nomadland are 18 data (51,43%) of bald on record strategy, followed by 8 data of positive politeness strategy (22.86%), then 6 data (17,14%) of negative politeness strategy, and 3 data (8,57%) of off record strategy. Meanwhile, the most dominant social dimensions that influenced the selection of politeness strategies used in Chloé Zhao's film Nomadland are 19 data (54,29%) of the solidarity–social distance scale, followed by 6 data (17,14%) of the formality scale, then 6 data (17,14%) of the referential and affective function scales, and the last is 4 data (11,43%) of the status scale.
3 D Convolutional Networks for Brain Tumor Segmentation

This paper presents our work on applying 3D Convolutional Networks for brain tumor segmentation for the BRATS challenge. We are currently experimenting with different 3D fully convolutional architectures. We present preliminary results using these architectures and outline our future steps and experiments, which involve hyperparameter optimization, comparison of the models’ performance and implementation of a post-processing stage to eliminate false positive predictions.
Optimized echo state networks with leaky integrator neurons for EEG-based microsleep detection

The performance of a microsleep detection system was calculated in terms of its ability to detect the behavioural microsleep state (1-s epochs) from spectral features derived from 16-channel EEG sampled at 256 Hz. Best performance from a single classifier model was achieved using leaky integrator neurons on an echo state network (ESN) classifier with a mean phi correlation (φ) of 0.38 and accuracy of 67.3%. A single classifier model of ESN with sigmoidal inputs achieved φ of 0.20 and accuracy of 48.5% and a single classifier model of linear discriminant analysis (LDA) achieved φ of 0.31 and accuracy of 53.6%. However, combining the output of several single classifier models (ensemble learning) via stacked generalization of the ESN with leaky integrator neurons approach led to a substantial increase in detection performance of φ of 0.51 and accuracy of 81.2%. This is a substantial improvement of our previous best result of φ = 0.39 on this data with LDA and stacked generalization.
Dynamic Rating of Sports Teams

We consider the problem of dynamically rating sports teams on the basis of categorical outcomes of paired comparisons such as win, draw and loss in football. Our modelling framework is the cumulative link model for ordered responses, where latent parameters represent the strength of each team. A dynamic extension of this model is proposed with close connections to nonparametric smoothing methods. As a consequence, recent results have more influence in estimating current abilities than results in the past. We highlight the importance of using a specific constrained random walk prior for time-changing abilities which guarantees an equal treatment of all teams. Estimation is done with an extended Kalman filter and smoother algorithm. An additional hyperparameter which determines the temporal dynamic of the latent team abilities is chosen on the basis of the optimal one-step-ahead predictive power. Alternative estimation methods are also considered. We apply our method to the results from the German football league Bundesliga 1996-1997 and to the results from the American National Basketball Association 1996-1997.
The Affective Slider: A Digital Self-Assessment Scale for the Measurement of Human Emotions

Self-assessment methods are broadly employed in emotion research for the collection of subjective affective ratings. The Self-Assessment Manikin (SAM), a pictorial scale developed in the eighties for the measurement of pleasure, arousal, and dominance, is still among the most popular self-reporting tools, despite having been conceived upon design principles which are today obsolete. By leveraging on state-of-the-art user interfaces and metacommunicative pictorial representations, we developed the Affective Slider (AS), a digital self-reporting tool composed of two slider controls for the quick assessment of pleasure and arousal. To empirically validate the AS, we conducted a systematic comparison between AS and SAM in a task involving the emotional assessment of a series of images taken from the International Affective Picture System (IAPS), a database composed of pictures representing a wide range of semantic categories often used as a benchmark in psychological studies. Our results show that the AS is equivalent to SAM in the self-assessment of pleasure and arousal, with two added advantages: the AS does not require written instructions and it can be easily reproduced in latest-generation digital devices, including smartphones and tablets. Moreover, we compared new and normative IAPS ratings and found a general drop in reported arousal of pictorial stimuli. Not only do our results demonstrate that legacy scales for the self-report of affect can be replaced with new measurement tools developed in accordance to modern design principles, but also that standardized sets of stimuli which are widely adopted in research on human emotion are not as effective as they were in the past due to a general desensitization towards highly arousing content.
The Geo-Ecological Risks of Oil Investments by China and the Global South: The Right to Development Revisited

: Addressing climate change and keeping global temperature rise within limits requires the phasing out of fossil fuels. Since four-fifths of fossil fuel resources that are currently available must remain underground (i.e., become stranded resources), nations and private investors should be cautious and strategic about investing in new sources, or else run the risk of having stranded assets. For emerging economies like China and historic or prospective oil investors in the global South (such as Nigeria and Kenya respectively), this raises an important question: What are the geo-ecological risks associated with investing in the fossil fuel industry by developing countries? This paper examines this question by drawing on theories at the nexus of climate change, the right to development, and stranded resources and assets, as well as through case studies of the transnationalization of oil politics in China, Nigeria as an OPEC country, and Kenya as a prospective oil investor. We first define and comprehensively assess the different geo-ecological risks, then argue that investing in fossil fuels today on the grounds of the right to development is akin to investing in ‘the emperor’s new clothes’ as the assets are expected to devalue quickly. Since most countries of the North will first want to divest their potentially stranded assets at the highest possible price, developing countries should be cautious about expanding and investing in these ‘hot’ assets as they may imminently bear the risks (and costs) of stranded assets.
Biotechnology and Bioengineering: Volume 116, Number 10, October 2019

Department of Physics, Savitribai Phule Pune University, Pune, Maharashtra, India Department of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India Department of Biotechnology, Savitribai Phule Pune University, Pune, Maharashtra, India Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India Centre for Nanosciences, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India Centre for Environmental Sciences and Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
Multiobjective fuzzy biclustering in microarray data: Method and a new performance measure

Objective of any biclustering algorithm in microarray data is to discover a subset of genes that are expressed similarly in a subset of conditions. The boundaries of biclusters usually overlap as genes and conditions may belong to different biclusters with different membership degrees. Hence the notion of fuzzy sets is useful for discovering such overlapping biclusters. In this article an attempt has been made to develop a multiobjective genetic algorithm based approach for probabilistic fuzzy biclustering that minimizes the residual and maximizes cluster size and expression profile variance. A novel variable string length encoding has been proposed in this regard that encodes multiple biclusters in a single string. Also a new performance measure that reflects how a bicluster is statistically distinguished from the background is proposed. Performance of the proposed algorithm has been compared with some well known biclustering algorithms.
Caught in the web: a meta-analysis of Internet addiction, excessive daytime sleepiness and depressive symptoms in adolescents

PurposeIn response to growing concerns over the negative consequences of Internet addiction on adolescents’ mental health, coupled with conflicting results in this literature stream, this meta-analysis sought to (1) examine the association between Internet addiction and depressive symptoms in adolescents, (2) examine the moderating role of Internet freedom across countries, and (3) examine the mediating role of excessive daytime sleepiness.Design/methodology/approachIn total, 52 studies were analyzed using robust variance estimation and meta-analytic structural equation modeling.FindingsThere was a significant and moderate association between Internet addiction and depressive symptoms. Furthermore, Internet freedom did not explain heterogeneity in this literature stream before and after controlling for study quality and the percentage of female participants. In support of the displacement hypothesis, this study found that Internet addiction contributes to depressive symptoms through excessive daytime sleepiness (proportion mediated = 17.48%). As the evidence suggests, excessive daytime sleepiness displaces a host of activities beneficial for maintaining mental health. The results were subjected to a battery of robustness checks and the conclusions remain unchanged.Practical implicationsThe results underscore the negative consequences of Internet addiction in adolescents. Addressing this issue would involve interventions that promote sleep hygiene and greater offline engagement with peers to alleviate depressive symptoms.Originality/valueThis study utilizes robust meta-analytic techniques to provide the most comprehensive examination of the association between Internet addiction and depressive symptoms in adolescents. The implications intersect with the shared interests of social scientists, health practitioners, and policy makers.
Analysis of Tactical Structures in Team Handball by Means of Artificial Neural Networks

In the field of sports games the analysis of the game structure as well as the analysis of the opponent team is of major interest for the training process in order to optimize tactical skills. Based on methodological problems of present game analysis some recent approaches apply artificial neural networks to examine the game structure. With the intention to analyze types of tactical structures in team handball we use a neural network to identify a number of such types which represent play processes with similar tactical structures. Therefore a process oriented observation model of the offensive play was developed on the basis of offensive attempts. 15 matches (12 teams) of the Women's Junior World Championship 2001 were observed. Afterwards a prepared neural network (DyCoN) was trained with 2900 offensive attempts (processes) from all teams to coin offensive attempt patterns. In the contribution it is shown that the neural network can be used in order to identify typical tactics of different teams.
Differences in effectiveness across sub-collections

The relative performance of retrieval systems when evaluated on one part of a test collection may bear little or no similarity to the relative performance measured on a different part of the collection. In this paper we report the results of a detailed study of the impact that different sub-collections have on retrieval effectiveness, analyzing the effect over many collections, and with different approaches to sub-dividing the collections. The effect is shown to be substantial, impacting on comparisons between retrieval runs that are statistically significant. Some possible causes for the effect are investigated, and the implications of this work are examined for test collection design and for the strength of conclusions one can draw from experimental results.
Technical improvement of a new bioreactor for large scale micropropagation of several Vaccinium cultivars

Acta Hortic. 1180. ISHS 2017. DOI 10.17660/ActaHortic.2017. 1180.53 Proc. XI International Vaccinium Symposium Ed.: J.W. Olmstead 387 Technical improvement of a new bioreactor for large scale micropropagation of several Vaccinium cultivars M. Welander1, A. Sayegh2, F. Hagwall3, T. Kuznetsova4 and A. Holefors4 1Swedish University of Agricultural Sciences, Department of Plant Breeding and Biotechnology, Alnarp, Sweden; 2TCpropagation Ltd Park Bree, Ireland; 3Cultiva AB, Sweden; 4Invitroplanttech, Malmö, Sweden.
A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data

Logistic regression (LR) is a conventional statistical technique used for data classification problem. Logistic regression is a model-based method, and it uses nonlinear model structure. Another technique used for classification is feedforward artificial neural networks. Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function. In this study, a hybrid approach of model-based logistic regression technique and data-based artificial neural network was proposed for classification purposes. The proposed approach was applied to lung cancer data, and obtained results were compared. It was seen that the proposed hybrid approach was superior to logistic regression and feedforward artificial neural networks with respect to many criteria.
Maasai pastoralists kill lions in retaliation for depredation of livestock by lions

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2019 The Authors. People and Nature published by John Wiley & Sons Ltd on behalf of British Ecological Society 1Centre for Ecology and Conservation, University of Exeter, Exeter, UK 2International Livestock Research Institute, Nairobi, Kenya 3National Museums of Kenya, Nairobi, Kenya 4Big Life Kenya, Mbirikani Group Ranch, Kenya 5WildlifeDirect, Karen, Kenya
Letter Special Section on Information and Communication System Security—against Cyberattacks— Access Control to Prevent Malicious Javascript Code Exploiting Vulnerabilities of Webview in Android Os *

SUMMARY Android applications that using WebView can load and display web pages. Interaction with web pages allows JavaScript code within the web pages to access resources on the Android device by using the Java object, which is registered into WebView. If this WebView feature were exploited by an attacker, JavaScript code could be used to launch attacks, such as stealing from or tampering personal information in the device. To address these threats, we propose an access control on the security-sensitive APIs at the Java object level. The proposed access control uses static analysis to identify these security-sensitive APIs, detects threats at runtime, and notifies the user if threats are detected, thereby preventing attacks from web pages.
Observer-based leakage detection in hydraulic systems with position and velocity feedback

Nonlinear observers with static feedback are considered for leakage detection in hydraulic servo systems. Two issues, namely fast dynamics reduction and elimination of multiple stationary points in the estimation error dynamics, are treated. It is shown that leakage detection performance is not degraded by the use of a reduced plant model, and that static feedback offers enough degrees of freedom to render one and only one stable stationary point of the error dynamics and that is at the origin. An algebraic condition guaranteeing the above property for the considered class of observers is provided.
Using miRNA-Analyzer for the Analysis of miRNA Data

MicroRNAs (miRNAs) are small biological molecules that play an important role during the mechanisms of protein formation. Recent findings have demonstrated that they act as both positive and negative regulators of protein formation. Thus, the investigation of miRNAs, i.e., the determination of their level of expression, has developed a huge interest in the scientific community. One of the leading technologies for extracting miRNA data from biological samples is the miRNA Affymetrix platform. It provides the quantification of the level of expression of the miRNA in a sample, thus enabling the accumulation of data and allowing the determination of relationships among miRNA, genes, and diseases. Unfortunately, there is a lack of a comprehensive platform able to provide all the functions needed for the extraction of information from miRNA data. We here present miRNA-Analyzer, a complete software tool providing primary functionalities for miRNA data analysis. The current version of miRNA-Analyzer wraps the Affymetrix QCTool for the preprocessing of binary data files, and then provides feature selection (the filtering by species and by the associated p-value of preprocessed files). Finally, preprocessed and filtered data are analyzed by the Multiple Experiment Viewer (T-MEV) and Short Time Series Expression Miner (STEM) tools, which are also wrapped into miRNA-Analyzer, thus providing a unique environment for miRNA data analysis. The tool offers a plug-in interface so it is easily extensible by adding other algorithms as plug-ins. Users may download the tool freely for academic use at https://sites.google. com/site/mirnaanalyserproject/d.
Association of child maltreatment and psychiatric diagnosis in Brazilian children and adolescents

OBJECTIVES: The objective of this study was to evaluate the association between different types of child maltreatment and the presence of psychiatric disorders in highly vulnerable children and adolescents served by a multidisciplinary program. METHODS: In total, 351 patients with a mean age of 12.47, of whom 68.7% were male and 82.1% lived in shelters, underwent psychiatric evaluations based on the Kiddie-Sads-Present and Lifetime Version. Two different methods were used to evaluate maltreatment: medical records were reviewed to identify previous diagnoses related to socioeconomic and psychosocial circumstances, and the Childhood Trauma Questionnaire was used to obtain a structured history of trauma. Bivariate associations were evaluated between psychiatric disorders and evidence of each type and the frequency of abuse. RESULTS: The most frequent psychiatric diagnoses were substance use disorders, affective disorders and specific disorders of early childhood, whereas 13.67% of the sample had no psychiatric diagnosis. All patients suffered neglect, and 58.4% experienced physical or sexual abuse. The presence of a history of multiple traumas was only associated with a diagnosis of substance use disorder. Mental retardation showed a strong positive association with reported physical abuse and emotional neglect. However, a negative correlation was found when we analyzed the presence of a history of multiple traumas and mental retardation. CONCLUSION: All children living in adverse conditions deserve careful assistance, but we found that physical abuse and emotional neglect were most strongly associated with mental retardation and multiple traumas with substance abuse.
Does Education Affect Religiosity? Causal Evidence from a Conservative Emerging Economy

Does education make people more or less religious? The previous literature offers mixed findings on the relationship between education and religiosity. This may be due to endogeneity bias: education and religiosity can be caused by a third variable such as culture or upbringing. We instrument education by exposure to the 1997 education reform in Turkey which increased mandatory schooling from 5 to 8 years. The schooling reform increased the probability that young girls would complete 8 years of schooling and report lower religiosity later in life. The reform apparently did not influence such outcomes for boys. These effects are observed primarily in females growing up in strongly religious or poor areas.
The direct generation of High Power Microwaves with compact Marx generators

High Power Microwave energy may be directly generated with ultra-fast voltage pulses driving an antenna. Recent efforts with the wave erection Marx generator have seen the production of voltage pulses in excess of several hundred kV, with rise times as fast as 200 ps. This generator has been used to source Ultra Wideband antennas as well as Narrow Band antennas, each resulting in high electric field strengths. This paper describes the Marx generator and explores its use for generating UWB and NB energy. Experimental results are presented.
Identification of an Intravenous Injectable NK1 Receptor Antagonist for Use in Traumatic Brain Injury

Traumatic brain injuries represent a leading cause of death and disability in the paediatric and adult populations. Moderate-to-severe injuries are associated with blood–brain barrier dysfunction, the development of cerebral oedema, and neuroinflammation. Antagonists of the tachykinin NK1 receptor have been proposed as potential agents for the post-injury treatment of TBI. We report on the identification of EUC-001 as a potential clinical candidate for development as a novel TBI therapy. EUC-001 is a selective NK1 antagonist with a high affinity for the human NK1 receptor (Ki 5.75 × 10−10 M). It has sufficient aqueous solubility to enable intravenous administration, whilst still retaining good CNS penetration as evidenced by its ability to inhibit the gerbil foot-tapping response. Using an animal model of TBI, the post-injury administration of EUC-001 was shown to restore BBB function in a dose-dependent manner. EUC-001 was also able to ameliorate cerebral oedema. These effects were associated with a significant reduction in post-TBI mortality. In addition, EUC-001 was able to significantly reduce functional deficits, both motor and cognitive, that normally follow a severe injury. EUC-001 is proposed as an ideal candidate for clinical development for TBI.