Miracle-dz/UniFAR-FTUs huggingface dataset:
Affective variables and informal digital learning of English: Keys to willingness to communicate in a second language

This study examined the under-researched relationship between informal digital learning of English (IDLE) activities (receptive IDLE activities and productive IDLE activities), affective variables (grit, motivation, self-confidence and second language speaking anxiety) and willingness to communicate in a second language. Data (N = 183) were collected through a questionnaire from one state university in an English-as-a-foreign-language Indonesian context. The results showed that students’ willingness to communicate correlated significantly with all of the IDLE activities and affective variables. However, only productive IDLE activities, grit, self-confidence, and motivation were identified as the significant predictors of students’ willingness to communicate. Findings suggest that students’ IDLE engagement and affective states play a significant role in a second language communication. In particular, pedagogical benefits of affective variables (e.g., grit, self-confidence, and motivation) and productive IDLE activities should be emphasised to facilitate students’ willingness to communicate in a second language. These results will broaden current knowledge of IDLE and second language communication behaviour, which can contribute to bridging the interdisciplinary gap between computer assisted language learning, second language acquisition, and psychology.
Modeling soil organic carbon in corn (Zea mays L.)-based systems in Ohio under climate change

Soil organic carbon (SOC) is a key indicator of soil quality. Knowledge of the effects of land management and climate change on SOC stocks is of vital importance in creating future sustainable land use systems. This study presents both the promise and current challenges of modeling SOC in mineral soils under climate change. Soils data from two long-term agricultural research sites in Ohio under no-till (NT) and plow-till (PT) management, the RothC soil C model, and climate data from the Canadian Regional Climate Model were used to project future SOC content in agricultural soils using low-emissions (LE) and high-emissions (HE) climate change scenarios. It was hypothesized that from 2015 to 2070, SOC levels in soils under NT management in Ohio will show increasing trends under the LE scenario, decreasing trends in NT under the HE scenario, and decreasing trends in PT under both scenarios, with lower levels of SOC for both treatments under the HE scenario. The results of this study projected total SOC content in the topsoil layers (0 to 25 cm [0 to 10 in] at Wooster and 0 to 23 cm [0 to 9 in] at Hoytville) to decrease at all sites and under all management and climate projections, with the exception of NT at Wooster and Hoytville and PT at Wooster under the LE scenario. Starting at 32.4 Mg C ha−1 (14.5 tn C ac−1) in 1962 at Wooster, by 2070, soil under NT management is projected to have 45.4 and 32.1 Mg C ha−1 (20.3 and 14.3 tn C ac−1) for LE and HE scenarios, respectively, while PT management starting at 31.5 Mg C ha−1 (14.1 tn C ac−1) would have 29.4 and 21 Mg C ha−1 (13.1 and 9.4 tn C ac−1) for LE and HE scenarios, respectively. Starting at 65.2 Mg C ha−1 (29.1 tn C ac−1) in 1963 at Hoytville, by 2070, soil under NT management would have 65.9 and 51 Mg C ha−1 (29.4 and 22.8 tn C ac−1) for LE and HE scenarios, respectively, and PT starting at 63.5 Mg C ha−1 (28.3 tn C ac−1) would have 36.9 and 28.7 Mg C ha−1 (16.5 and 12.8 tn C ac−1) for LE and HE scenarios, respectively.
Usilng Machine Learning Technliques to Identify Botnet Traffic

To date, techniques to counter cyber-attacks have predominantly been reactive; they focus on monitoring network traffic, detecting anomalies and cyber-attack traffic patterns, and, a posteriori, combating the cyber-attacks and mitigating their effects. Contrary to such approaches, we advocate proactively detecting and identifying botnets prior to their being used as part of a cyber-attack (Strayer et al., 2006). In this paper, we present our work on using machine learning-based classification techniques to identify the command and control (C2) traffic of IRC-based botnets - compromised hosts that are collectively commanded using Internet relay chat (IRC). We split this task into two stages: (I) distinguishing between IRC and non-IRC traffic, and (II) distinguishing between botnet and real IRC traffic. For stage I, we compare the performance of J48, naive Bayes, and Bayesian network classifiers, identify the features that achieve good overall classification accuracy, and determine the classification sensitivity to the training set size. While sensitive to the training data and the attributes used to characterize communication flows, machine learning-based classifiers show promise in identifying IRC traffic. Using classification in stage II is trickier, since accurately labeling IRC traffic as botnet and non-botnet is challenging. We are currently exploring labeling flows as suspicious and non-suspicious based on telltales of hosts being compromised
Outsourcing and Offshoring : Problems for Price and Productivity Measurement

1 The lowering of communications costs, transportation costs, and trade barriers coupled with economic reform and rapid development in many countries such as China have stimulated tremendous growth in world trade. Imports and exports expressed as a percent of U.S. GDP grew from 20 percent in 1989 to 29 percent in 2007. Most of the increase in the relative importance of trade in the U.S. economy is accounted for by the expansion of imports, and most of the import growth, in turn, is accounted for by imports from developing countries. Imports from China alone made up 39 percent of the growth in imports since 2000. Most imports, whether purchased for further processing or for distribution to final consumers, may be broadly classified as intermediate inputs, and it is in this broad sense that I use the term intermediate input in this paper. As communications costs, transportation costs, and trade barriers have fallen and as developing countries have instituted reforms and improved economic capabilities, the optimal sourcing of intermediate inputs has rapidly changed. The shift of the sourcing of intermediate inputs from domestic to foreign producers—often referred to as offshore outsourcing or offshoring in the literature—is driven by factor price arbitrage. The substitution of lower-cost auto parts from Mexico and China for domestically produced parts is an example of offshore outsourcing or offshoring of intermediate inputs in manufacturing (auto assembly) as well as services (auto repair). The move from suppliers with domestic production to suppliers with production in lower-cost countries such as China illustrates offshore outsourcing of " inputs " in the retail or wholesale trade sectors in which imported goods do not undergo further processing. The growth of offshore outsourcing and offshoring and associated growth in imports from low-wage countries has spurred numerous academics and policy analysts to study its implications for U.S. economy, most notably for productivity, employment, wage levels, and inequality. The purpose of this paper is not to weigh in on the costs, benefits, or distributional impacts of the trade. Rather, it is to argue that the data are not suitable for making such assessments. 2 Input price declines, which are the salient feature of offshore outsourcing and offshoring, are not generally captured in import price statistics. As a consequence, the value of real imports is understated and GDP, industry value-added output measures, and productivity will be overstated. This mismeasurement will tend to bias studies against finding …
Stephanorhinus (Mammalia: Rhinocerotidae) of the western European Pleistocene, with a revision of S. etruscus (Falconer, 1868)

Les rhinoceros du Plio-Pleistocene europeen traditionellement attribues au genre Dicerorhinus Gloger sont provisoirement assignes au genre Stephanorhinus Kretzoi. Le S. hundsheimensis est reconnu comme une espece separee du S. etruscus. Ce dernier est caracteristique de la faune du Pliocene recent et du Pleistocene ancien, alors que le S. hundsheimensis apparait au Pleistocene moyen, et surtout au debut de cette periode. Ces especes sont comparees entre elles, puis avec le S. hemitoechus et le S. kirchbergensis, a partir des traits morphologiques et des dimensions du crâne, de la dentition et des os des membres
Undiscovered Public Knowledge

Knowledge can be public, yet undiscovered, if independently created fragments are logically related but never retrieved, brought together, and interpreted. Information retrieval, although essential for assembling such fragments, is always problematic. The search process, like a scientific theory, can be criticized and improved, but can never be verified as capable of retrieving all information relevant to a problem or theory. This essential incompleteness of search and retrieval therefore makes possible, and plausible, the existence of undiscovered public knowledge. Three examples intended to throw light on the logic of undiscovered knowledge are constructed and analyzed. The argument is developed within the framework of a Popperian or critical approach within science and on Popper's distinction between subjective and objective knowledge--the distinction between World 2 and World 3.
International Journal of Affective Engineering

Most of the previous cross-cultural Kansei design research has been limited to Asia, Europe and America. However, the interaction of the product design market between Africa and Asia also deserves much attention. Having observed rich aesthetic traditions among the Asian cultures such as Japan, and also having noted aesthetics as an emotional socio-cultural factor in African design culture, a point of convergence is being sought. In this study, we focused on investigating the aesthetic sensibility in contemporary, culturally-inspired African product design. Firstly, we drew case studies from a previous product design research conducted in Botswana and the Hippo water roller design from South Africa. Secondly, we set up an online questionnaire survey to elicit the current perceptions of African designers about African product characteristics, aesthetic dimensions and possible affinity with the Asian design cultures. Our findings thus attempt to reveal the underlying concept of African design and the product aesthetic sensibility in a post-colonial Africa. Hence, we propose a cross-cultural product evaluation based on Kansei method. We strongly anticipated that the application of the Kansei studies to the African design domain will expand the platform for understanding the cross-cultural dynamics of Kansei and this knowledge, in turn, can translate into improving the design process for products that are cross-culturally sensitive, instinctively connective, emotionally rewarding and widely acceptable.
A Comparison of Deception Behavior in Dyad and Triadic Group Decision Making in Synchronous

can be found at: Small Group Research Additional services and information for http://sgr.sagepub. com/cgi/alerts Email Alerts: http://sgr.sagepub. com/subscriptions Subscriptions: http://www.sagepub. com/journalsReprints.nav Reprints: http://www.sagepub. com/journalsPermissions.nav Permissions: http://sgr.sagepub. com/cgi/content/refs/37/2/140 SAGE Journals Online and HighWire Press platforms): (this article cites 39 articles hosted on the Citations
Estimation the Number of Mortality Due to Cardiovascular and Respiratory disease, Attributed to pollutants O3, and NO2 in the Air of Tehran.

Introduction and Purpose: Nowadays, the air quality in many major cities is one of the most important environmental problems that seriously compromises the health of residents and causes a range of acute and chronic health effects. Therefore, this study was aimed to quantify and estimate the health effects related to the pollutants NO 2 and O 3 in metropolis of Tehran in 2014 was done by AirQ model. Methods: Data acquired were collected from the environmental organization and the center of air quality control in Tehran. This data were analyzed by Microsoft Excel software, and the processed data was entered AirQ model. The relative risk, baseline incidence and the attributed proportion was estimated and the overall results in case of mortality and morbidity were presented in tables and graphs. Results: The results showed that the average annual concentration of NO 2 and O 3 in Tehran, was respectively 130 and 64 μg/m 3 . According to the results with baseline incidence of 543.5 per 100,000, the total cumulative number of mortalities attributed to nitrogen dioxide and ozone, were estimated respectively to be 1593 and 946, that included about 2.66% and 1.58% of total mortalities except traffic accidents in Tehran. Conclusion: Our results showed that the level of air pollution has increased in Tehran, which requires more attention of authorities and policy makers to control the air pollution.
Effect of Soil Surface Mulching on Microorganisms and Nutrients of Rhizosphere Soil of Greenhouse Cucumber

The cucumber was selected to study different surface mulching of combination of plastic film and maize straw on soil surface in greenhouse.Data of rhizosphere soil microorganisms and nutrients were measured to explore the best treatment and internal mechanism.The results showed that different treatments improved cell number of soil bacterial,increased content of soil organic matter,total K,and alkali-hydrolysable nitrogen in fruiting period.The treatments of straw added plastic film were better than the treatments of only straw or plastic film,and the treatment of straw was better than the treatment of plastic film,but increased the number of fungi which caused plant diseases.In the experiment,treatments of straw added plastic film promoted colony count of microorganisms,straw decomposition,and nutrient releasing,so that increased content of soil nutrients.In a word,DJ2treatment was more than treatments of DJ3and DJ1, compared with the contrast in later fruiting period,it increased cell number of soil bacteria and cellulosedecomposing,and content of soil organic matter,total P,total K,and alkali-hydrolysable nitrogen,which increased 82.78%,10.98%,20.83%,22.83%,5.16%and 5.53%,respectively.
Multihop Ad Hoc Networking: The Reality

In this article we show that, although pure general-purpose MANET (mobile ad hoc networks) does not yet exist in the real world, the multihop ad hoc networking paradigm was successfully applied in several classes of networks that are penetrating the mass market. We present as examples mesh, opportunistic, vehicular, and sensor networks, where the multi-hop ad hoc paradigm is applied in a pragmatic way to extend the Internet and/or to support well-defined application requirements. We contrast these successful areas of ad hoc networking to the lack of impact of pure general-purpose MANET, demonstrating how a more pragmatic approach is a winner
AOGNets: Compositional Grammatical Architectures for Deep Learning

Neural architectures are the foundation for improving performance of deep neural networks (DNNs). This paper presents deep compositional grammatical architectures which harness the best of two worlds: grammar models and DNNs. The proposed architectures integrate compositionality and reconfigurability of the former and the capability of learning rich features of the latter in a principled way. We utilize AND-OR Grammar (AOG) as network generator in this paper and call the resulting networks AOGNets. An AOGNet consists of a number of stages each of which is composed of a number of AOG building blocks. An AOG building block splits its input feature map into N groups along feature channels and then treat it as a sentence of N words. It then jointly realizes a phrase structure grammar and a dependency grammar in bottom-up parsing the “sentence” for better feature exploration and reuse. It provides a unified framework for the best practices developed in state-of-the-art DNNs. In experiments, AOGNet is tested in the ImageNet-1K classification benchmark and the MS-COCO object detection and segmentation benchmark. In ImageNet-1K, AOGNet obtains better performance than ResNet and most of its variants, ResNeXt and its attention based variants such as SENet, DenseNet and DualPathNet. AOGNet also obtains the best model interpretability score using network dissection. AOGNet further shows better potential in adversarial defense. In MS-COCO, AOGNet obtains better performance than the ResNet and ResNeXt backbones in Mask R-CNN.
Paravalvular regurgitation one year after transcatheter aortic valve implantation

Objectives: The aim of this study was to assess the natural history of paravalvular regurgitation at 1 year in patients undergoing TAVI. Background: The immediate incidence of paravalvular regurgitation is estimated to be between 65 and 85% following transcatheter aortic valve implantation (TAVI). There is limited data as to whether this deteriorates during follow‐up. Methods: Forty‐six patients were recruited from a TAVI programme at our institute. All patients underwent an assessment of prosthetic valve function periprocedurally with aortography and immediately postprocedurally with transthoracic echocardiography. Twenty‐one patients with a median age was 83 (66–91) years of whom 14 were male reached 1 year follow‐up, 13 of whom were available for repeat transthoracic echocardiography. Results: The incidence of paravalvular regurgitation immediately following TAVI was 86%. Of them 57% had ≤ mild regurgitation and 29% had > mild regurgitation. At 1 year the incidence of paravalvular regurgitation was 77%. 54% had ≤ mild regurgitation and 34% > mild regurgitation. No patient had severe regurgitation. The degree of regurgitation reduced in 6 (46%), stayed the same in 3 (23%), and increased in 4 (31%) of patients. Conclusions: Patients undergoing TAVI have an immediate postprocedural risk of regurgitation of 86%. In the majority of cases the degree of paravalvular CoreValve® regurgitation is mild, and remains stable in 70% of patients during medium term follow‐up. © 2009 Wiley‐Liss, Inc.
Proceedings of the first ADVISES Young Researchers Workshop

(max. 2000 char.) : Abstract: The research training network Analysis Design and Validation of Interactive Safety-critical and Error-tolerant Systems (ADVISES) focus on coaching and supervising Post Docs and Ph.D. students. The main objective is to provide a multi-disciplinary research training that can combat the impact of human error during the design, operation and management of safety-critical, interactive systems. Additionally, the exchange of knowledge, practices, tools and experience between adjacent (but still too distinct) disciplines can lead to the efficient integration of complementary research methods. Ultimately, it is hoped that this will contribute to a new and more unified research agenda for the development of safety-critical, interactive systems. reduce the occurrence of erroneous events design safety-critical interactive systems to apply Abstract : Human factors contribute to risk in safety critical systems. However, current approaches to integrating human factors issues in the development of safety critical systems appear not fully sufficient. In this paper a we argue for creating a safety architecture. We show how such a architecture can be created using a Safety Modelling Language (SML), which uses barriers to prevent of stop undesired effects. Subsequently we discuss how these barriers can be implemented via various approaches. One of these approaches, the use of activity diagrams is illustrated using a case from the Air Traffic Control domain.
Learning Limited Dependence Bayesian Classi ers

We present a framework for characterizing Bayesian classi cation methods. This framework can be thought of as a spectrum of allowable dependence in a given probabilistic model with the Naive Bayes algorithm at the most restrictive end and the learning of full Bayesian networks at the most general extreme. While much work has been carried out along the two ends of this spectrum, there has been surprising little done along the middle. We analyze the assumptions made as one moves along this spectrum and show the tradeo s between model accuracy and learning speed which become critical to consider in a variety of data mining domains. We then present a general induction algorithm that allows for traversal of this spectrum depending on the available computational power for carrying out induction and show its application in a number of domains with di erent properties.
Predicting Trends in Cereal Production in the Czech Republic by Means of Neural Networks

This paper deals with problems of processing agricultural production data into the form of time series and analysing consequent results by means of two completely different methods. The first method for calculating cereals production figures uses the MS-Excel spreadsheet using conventional mathematical and statistical functions while the second one uses the ELKI software providing users with development environment including algorithms of neural networks. The obtained results are similar to a certain extent which shows new possibilities of progressive use of neural networks in future and enables modern approach to analysing time series not only in agricultural sector.
Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension

—Multi-hop machine reading comprehension is a chal- lenging task in natural language processing, which requires more reasoning ability and explainability. Spectral models based on graph convolutional networks grant the inferring abilities and lead to competitive results, however, part of them still face the challenge of analyzing the reasoning in a human-understandable way. Inspired by the concept of the Grandmother Cells in cognitive neuroscience, a spatial graph attention framework named ClueReader , imitating the procedure was proposed. This model is designed to assemble the semantic features in multi-angle representations and automatically concentrate or alleviate the information for reasoning. The name “ ClueReader ” is a metaphor for the pattern of the model: regard the subjects of queries as the start points of clues, take the reasoning entities as bridge points, and consider the latent candidate entities as the grandmother cells, and the clues end up in candidate entities. The proposed model allows us to visualize the reasoning graph and analyze the importance of edges connecting two entities and the selectivity in the mention and candidate nodes, which can be easier to be comprehended empirically. The ofﬁcial evaluations in open-domain multi-hop reading dataset W IKI H OP and Drug- drug Interactions dataset M ED H OP prove the validity of our approach and show the probability of the application of the model in the molecular biology domain.
GLOBAL JOURNAL OF ADVANCED ENGINEERING TECHNOLOGIES AND SCIENCES

Magnetic adaptive testing is a nondestructive method, based on systematic measurement and evaluation of magnetic hysteresis loops, to characterize the degradation of ferromagnetic materials. Magnetic parameters in general depend on the size of the samples to be measured, and results of measurements, performed on different size samples cannot be compared with each other. In this work it is shown, that by proper choice of the size of magnetizing yoke, the degradation of the material can be correctly determined even in that case if different size of samples are measured.