nils-herrmann/clean_scirepeval huggingface dataset:
Teacher Enthusiasm: Reviewing and Redefining a Complex Construct

The last review on teacher enthusiasm was 45 years ago, and teacher enthusiasm remains a compelling yet complex variable in the educational context. Since Rosenshine's (School Review 78:499 514, 1970) review, the conceptualizations, definitions, methodology, and results have only become more scattered, and several related constructs have emerged that may or may not be synonymous with teacher enthusiasm. In this review, we delve into the past four decades of teacher enthusiasm research to provide a potential starting point for a new, consolidated direction in teacher enthusiasm research based on a proposed, holistic definition of enthusiasm which brings together research from the past and can fuel research for the future. We begin by reviewing definitions of teacher enthusiasm and related constructs and, thereafter, put forward a new and integrative definition of teacher enthusiasm that combines the two most prevalent conceptualizations of the construct, namely experienced enjoyment and expressive behavior. Bearing our proposed definition in mind, we go on to present numerous measures that assess teacher enthusiasm, detail research evidence related to its correlates, and finally derive several research implications that, when considered in future research, promise to advance the field.
Redefining the "carrier" state for foot and mouth disease from the dynamics of virus persistence in endemically affected cattle populations
Venue: Scientific reports

The foot and mouth disease virus (FMDV) "carrier" state was defined by van Bekkum in 1959. It was based on the recovery of infectious virus 28 days or more post infection and has been a useful construct for experimental studies. Using historic data from 1,107 cattle, collected as part of a population based study of endemic FMD in 2000, we developed a mixed effects logistic regression model to predict the probability of recovering viable FMDV by probang and culture, conditional on the animal's age and time since last reported outbreak. We constructed a second set of models to predict the probability of an animal being probang positive given its antibody response in three common non structural protein (NSP) ELISAs and its age. We argue that, in natural ecological settings, the current definition of a "carrier" fails to capture the dynamics of either persistence of the virus (as measured by recovery using probangs) or the uncertainty in transmission from such animals that the term implies. In these respects it is not particularly useful. We therefore propose the first predictive statistical models for identifying persistently infected cattle in an endemic setting that captures some of the dynamics of the probability of persistence. Furthermore, we provide a set of predictive tools to use alongside NSP ELISAs to help target persistently infected cattle.
Human emotion detection through speech and facial expressions
Venue: 2016 3rd International Conference on Computer and Information Sciences (ICCOINS)

Estimation of human emotions using a computer has been difficult since when human engaged in a conversational secession. In this research work, a proposed hybrid system through facial expressions and speech is used to estimate basic emotions (angry, sad, happy, boredom, disgust and surprise) of a person when he is engaged in a conversational secession. Relative Bin Frequency Coefficients and Relative Sub Image Based features are used for acoustic and visual data respectively. Support Vector Machine with radial basis kernel is used for classification. This research work revealed that the proposed feature extraction through speech and facial expression is the most prominent aspect affecting the emotion detection system accompanied by proposed fusion technique. Although, some aspects considered affecting the emotion detection system, this affect is relatively minor. It was observed that the performance of the bimodal emotion detection system is low than the unimodal emotion detection system through deliberate facial expressions. In order to deal with the issue, a suitable database is used. The results indicated that the proposed emotion detection system showed better performance with respect to basic emotional classes than the rest.
Human Emotion Detection via Brain Waves Study by Using Electroencephalogram (EEG)

Human emotion is very difficult to determine just by looking at the face and also the behavior of a person. This research was conducted to detect or identify human emotion via the study of brain waves. In addition, the research aims to develop computer software that can detect human emotions quickly and easily. This study aims at EEG signals of relationship and human emotions. The main objective of this recognition is to develop "mind implementation of Robots" While the research methodology is divided into four; (i) both visibility and EEG data were used to extract the date at the same time from the respondent, (ii) the process of complete data record includes the capture of images using the camera and EEG, (iii) pre processing, classification and feature extraction is done at the same time, (iv) the features extracted is classified using artificial intelligence techniques to emotional faces. Researchers expect the following results; (i) studies brain waves for the purpose of emotions, (ii) the study of human emotion with facial emotions and to relate the brain waves, (iii) In conclusion, this study is very useful for doctors in hospitals and police departments for criminal investigation. As a result of this study, it also helps to develop a software package.
Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection
Venue: IEEE Transactions on Affective Computing

Emotions are time varying affective phenomena that are elicited as a result of stimuli. Videos and movies in particular are made to elicit emotions in their audiences. Detecting the viewers' emotions instantaneously can be used to find the emotional traces of videos. In this paper, we present our approach in instantaneously detecting the emotions of video viewers' emotions from electroencephalogram (EEG) signals and facial expressions. A set of emotion inducing videos were shown to participants while their facial expressions and physiological responses were recorded. The expressed valence (negative to positive emotions) in the videos of participants' faces were annotated by five annotators. The stimuli videos were also continuously annotated on valence and arousal dimensions. Long short term memory recurrent neural networks (LSTM RNN) and continuous conditional random fields (CCRF) were utilized in detecting emotions automatically and continuously. We found the results from facial expressions to be superior to the results from EEG signals. We analyzed the effect of the contamination of facial muscle activities on EEG signals and found that most of the emotionally valuable content in EEG features are as a result of this contamination. However, our statistical analysis showed that EEG signals still carry complementary information in presence of facial expressions.
Multiple Instance Hyperspectral Target Characterization
Venue: ArXiv

In this paper, two methods for multiple instance target characterization, MI SMF and MI ACE, are presented. MI SMF and MI ACE estimate a discriminative target signature from imprecisely labeled and mixed training data. In many applications, such as sub pixel target detection in remotely sensed hyperspectral imagery, accurate pixel level labels on training data is often unavailable and infeasible to obtain. Furthermore, since sub pixel targets are smaller in size than the resolution of a single pixel, training data is comprised only of mixed data points (in which target training points are mixtures of responses from both target and non target classes) Results show improved, consistent performance over existing multiple instance concept learning methods on several hyperspectral sub pixel target detection problems.
Instance influence estimation for hyperspectral target signature characterization using extended functions of multiple instances
Venue: SPIE Defense Security

The Extended Functions of Multiple Instances (eFUMI) algorithm1 is a generalization of Multiple Instance Learning (MIL) In eFUMI, only bag level (i.e. set level) labels are needed to estimate target signatures from mixed data. The training bags in eFUMI are labeled positive if any data point in a bag contains or represents any proportion of the target signature and are labeled as a negative bag if all data points in the bag do not represent any target. From these imprecise labels, eFUMI has been shown to be effective at estimating target signatures in hyperspectral subpixel target detection problems. One motivating scenario for the use of eFUMI is where an analyst circles objects/regions of interest in a hyperspectral scene such that the target signatures of these objects can be estimated and be used to determine whether other instances of the object appear elsewhere in the image collection. The regions highlighted by the analyst serve as the imprecise labels for eFUMI. Often, an analyst may want to iteratively refine their imprecise labels. In this paper, we present an approach for estimating the influence on the estimated target signature if the label for a particular input data point is modified. This instance influence estimation guides an analyst to focus on (re )labeling the data points that provide the largest change in the resulting estimated target signature and, thus, reduce the amount of time an analyst needs to spend refining the labels for a hyperspectral scene. Results are shown on real hyperspectral sub pixel target detection data sets.
Research Support Services in South African Academic Libraries

Changing pedagogy and rapid growth of commensurate technologies have triggered demand from academic libraries for new research support services such as bibliometrics, data management, digital preservation and curation, open access (OA) and open journal publishing. South African academic libraries have responded, to an extent, to the changing research support needs of their research communities. Some of the mainstream research support services provided by South African academic libraries include bibliometrics, open scholarship services and research data management. There are also some non typical research support services and/or activities that are provided these include research landscape analysis, research week and research engagement provision.
Cesium containing triple cation perovskite solar cells: improved stability, reproducibility and high efficiency+ +Electronic supplementary information (ESI) available. See DOI: 10.1039/c5ee03874j Click here for additional data file.
Venue: Energy environmental science

Today's best perovskite solar cells use a mixture of formamidinium and methylammonium as the monovalent cations. Adding cesium improves the compositions greatly.
On line relative clause attachment strategy in heritage speakers of Spanish

Previous research on relative clause attachment has found that late Spanish English bilinguals tend to parse both languages with a single strategy that is consistent with that of the language in which they are currently immersed. One recent investigation using an off line measure has suggested that the same may not be true of early Spanish English bilinguals, who did not show effects of greater exposure to English in the comprehension of ambiguous relative clauses (RCs) The present study sought to determine whether on line RC attachment behavior in heritage Spanish was similarly unaffected by extensive exposure to English.  Forty six monolinguals and 28 heritage speakers of Mexican Spanish participated in a self paced reading experiment in which stimuli contained temporarily ambiguous relative clauses that were biased toward either high or low syntactic attachment.  Analysis of variance by subject and by item was performed on reading time data and on responses to post stimulus comprehension questions.  Statistical analyses revealed similar preferences for high attachment in Spanish among both monolinguals and heritage speakers.  These results suggest that Spanish heritage speakers, as early bilinguals, may be less affected by ongoing exposure to English than the late bilinguals tested in prior studies.  The outcome also suggests that, within Spanish as a heritage language, relative clause attachment may be unique compared to other aspects of sentence processing in that it shows little influence from English.
Stabilization of a silty soil: Effects of disruptive salts

Soil stabilization with lime and hydraulic binders is a widely used technique to improve the stability and the mechanical properties of soils for use in road construction. However, the presence of substances such as sulphates can induce the swelling, cracking and loss of some mechanical properties defined as disruptive behaviours. This paper analyses the disturbances on the treatment of one silty soil contaminated with sulphates, chlorides, phosphates and nitrates salts, separately then by combinations of two salts. Tests in accelerated curing conditions are used as they are the first realised by the embankments companies, before undertaking further study. The impact of each soluble salt depends on the associated anion and cation. It is not possible to establish a degree of disturbance by cation or anion type. The deleterious effects of sulphates can change if other salts coexist in the soil (i.e. Vs (Volumetric swelling) equal 7% in case of potassium sulphate contamination, whereas Vs reaches 18 and 23% with a crossed contamination of potassium sodium sulphates and potassium sulphate chloride respectively) The influence of complete salts, instead of anions only, should be integrated in technical guides to improve the reliability of the suitability tests they propose.
Stabilization of Soil: A Review

If good earth is not available at the construction site, it becomes imperative to opt for soil stabilization. Soil stabilization is a process to treat a soil to maintain or improve the performance of the soil as a construction material. The stabilizing agent improves the strength parameters of sub grade of road pavement and leads to strengthening of embankment. The objective of this paper is to review the applications of different stabilizing agents such as lime, fly ash, cement, rice husk, expanded polystyrene geofoam and waste paper sludge for different type of soil.
Deep clustering: Discriminative embeddings for segmentation and separation
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

We address the problem of "cocktail party" source separation in a deep learning framework called deep clustering. Previous deep network approaches to separation have shown promising performance in scenarios with a fixed number of sources, each belonging to a distinct signal class, such as speech and noise. However, for arbitrary source classes and number, "class based" methods are not suitable. Instead, we train a deep network to assign contrastive embedding vectors to each time frequency region of the spectrogram in order to implicitly predict the segmentation labels of the target spectrogram from the input mixtures. This yields a deep network based analogue to spectral clustering, in that the embeddings form a low rank pair wise affinity matrix that approximates the ideal affinity matrix, while enabling much faster performance. At test time, the clustering step "decodes" the segmentation implicit in the embeddings by optimizing K means with respect to the unknown assignments. Preliminary experiments on single channel mixtures from multiple speakers show that a speaker independent model trained on two speaker mixtures can improve signal quality for mixtures of held out speakers by an average of 6dB. More dramatically, the same model does surprisingly well with three speaker mixtures.
Label Embedding for Zero shot Fine grained Named Entity Typing
Venue: COLING

Named entity typing is the task of detecting the types of a named entity in context. For instance, given "Eric is giving a presentation" our goal is to infer that 'Eric' is a speaker or a presenter and a person. Existing approaches to named entity typing cannot work with a growing type set and fails to recognize entity mentions of unseen types. In this paper, we present a label embedding method that incorporates prototypical and hierarchical information to learn pre trained label embeddings. In addition, we adapt a zero shot learning framework that can predict both seen and previously unseen entity types. We perform evaluation on three benchmark datasets with two settings: 1) few shots recognition where all types are covered by the training set; and 2) zero shot recognition where fine grained types are assumed absent from training set. Results show that prior knowledge encoded using our label embedding methods can significantly boost the performance of classification for both cases.
Fine Grained Named Entity Classification with Wikipedia Article Vectors
Venue: 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

This paper addresses the task of assigning multiple labels of fine grained named entity (NE) types to Wikipedia articles. To address the sparseness of the input feature space, which is salient particularly in fine grained type classification, we propose to learn article vectors (i.e. entity embeddings) from hypertext structure of Wikipedia using a Skip gram model and incorporate them into the input feature set. To conduct large scale practical experiments, we created a new dataset containing over 22,000 manually labeled instances. The results of our experiments show that our idea gained statistically significant improvements in classification results.
Neural Joint Learning for Classifying Wikipedia Articles into Fine grained Named Entity Types
Venue: PACLIC

This paper addresses the task of assigning finegrained NE type labels to Wikipedia articles. To address the data sparseness problem, which is salient particularly in fine grained type classification, we introduce a multi task learning framework where type classifiers are all jointly learned by a neural network with a hidden layer. In addition, we also propose to learn article vectors (i.e. entity embeddings) from Wikipedia's hypertext structure using a Skipgram model and incorporate them into the input feature set. To conduct large scale practical experiments, we created a new dataset containing over 22,000 manually labeled instances. The dataset is available. The results of our experiments show that both ideas gained their own statistically significant improvement separately in classification accuracy.
Integrated Approach for Liming and Fertilizer Application on Yield of Cabbage and Cauliflower in Acidic Soil of Sylhet

An experiment was conducted at farming system research and development (FSRD) site, Jalalpur and multi location testing (MLT) sites, Moulvibazar and Zokigonj during 2013 2015 to study the effect of liming and fertilizers levels on the yield of cabbage and cauliflower in acidic soil of Sylhet. Trials were laid out in a factorial randomized block design with six dispersed replications. The treatments of fertilizer managements were M 1 soil test based (STB) recommended fertilizer and M 2 fertilizer dose practiced by the farmers (FP) and the level of limes were L 1 :0 t ha 1; L 2 1 t ha 1 and L 3 2 t ha 1 Results revealed that yields of cabbage and cauliflower responded significantly to fertilizer doses, levels of lime, and their combinations in all the locations. Soil test based fertilizer rate (M 1 N 201 P 63 K 65 S 23 Zn 0.5 for cabbage and M 1 N 157 P 57 K 116 S 20 Zn 0.5 Kg ha 1 for cauliflower) gave the maximum head and curd yields of cabbage and cauliflower, respectively. The levels of lime at the rate 2 t ha 1 (L 3 provided the highest yields of cabbage and cauliflower in all locations. Dolomite lime 2 t ha 1 in combination with recommended fertilizer (M 1 L 3 gave the highest yields and maximum economic returns of cabbage and cauliflower in all locations. However, it was identical with 1 t ha 1 of dolomite lime of similar dose of fertilizer nutrients. Therefore, application of lime at the rate 1 t ha 1 with STB recommended fertilizer management (M 1 could be enough for profitable economic returns for cultivating cabbage and cauliflower in strongly acidic soil in Sylhet. Bangladesh Agron. J. 2016, 19(1) 49 57
Optimizing skyline queries over incomplete data
Venue: Inf. Sci.

Skyline queries have been widely used as an attractive operator in multi criteria decision making applications. Because of the intuitive notion of skyline queries, many skyline algorithms have been developed in various data settings. However, most of the skyline algorithms rely on the assumption of completeness, i.e. all values of points are known. In many cases, because this assumption does not hold, conventional skyline algorithms cannot be applied. To handle incomplete data, existing work redefines the dominance notion by using the common subspace between points. However, it can incur too many pairwise comparisons over incomplete data. To address this problem, we first propose a new sorting based bucket skyline algorithm using two optimization techniques: bucket and point level orders. In case that too few or no skyline points exist over incomplete data, we develop a novel skyline ranking method that adjusts two user specific parameters for retrieving meaningful skyline points. Lastly, we empirically evaluate the efficiency and effectiveness of our proposed algorithms over both synthetic and real life datasets.
Force Field Development from Periodic Density Functional Theory Calculations for Gas Separation Applications Using Metal Organic Frameworks

We present accurate force fields developed from density functional theory (DFT) calculations with periodic boundary conditions for use in molecular simulations involving M2(dobdc) (M MOF 74; dobdc4 2,5 dioxidobenzenedicarboxylate; M Mg, Mn, Fe, Co, Ni, Zn) and frameworks of similar topology. In these systems, conventional force fields fail to accurately model gas adsorption due to the strongly binding open metal sites. The DFT derived force fields predict the adsorption of CO2, H2O, and CH4 inside these frameworks much more accurately than other common force fields. We show that these force fields can also be used for M2(dobpdc) (dobpdc4 4,4' dioxidobiphenyl 3,3' dicarboxylate) an extended version of MOF 74, and thus are a promising alternative to common force fields for studying materials similar to MOF 74 for carbon capture applications. Furthermore, it is anticipated that the approach can be applied to other metal organic framework topologies to obtain force fields for different systems. We ha.
Research Methods in the itAIS Community: Building a Classification Framework for Management and Information Systems Studies

In this paper, we present a framework for the systematic classification of management research methods. We propose a scalable model based on six classification criteria: Type of Contribution (e.g. theory testing, context specific description, etc. Research Paradigm (e.g. positivist, interpretivist, etc. Research Design (e.g. case study, survey, etc. Time and Distance Lag Strategies (e.g. longitudinal, cross cultural, etc. Information Elaboration Methods (e.g. statistical analysis, text analysis, etc. and Information Gathering and Preparation Methods (e.g. interviews, secondary data collection, etc. We suggest that the six level framework we present here has the potential to allow the systematic and granular monitoring of methodological trends in scholarly communities. In the final part of the paper, the framework is utilized to classify the 62 papers of the itAIS 2011 selected papers collection, and the methodological attitudes of the itAIS community are briefly commented.