nils-herrmann/clean_scirepeval huggingface dataset:
Building Pre Service Teacher Interest in Computer Science Education through Mentoring Experiences (Abstract Only)
Venue: SIGCSE

K 12 teachers have an important role to play in introducing children to computer science. However, most teachers lack explicit training in computer science. This project explored questions related to the experiences of pre service teachers as they mentored K 12 children in computer science in an informal learning environment. Through a grant funded project at a comprehensive public university in the Southeastern United States, five pre education major undergraduate students in an introductory education course learned basic computer programming, interacted with a K 12 computer science teacher around strategies for introducing computer science to kids, and mentored children in exploring computer science in an informal setting. Quantitative and qualitative analyses of the data suggest these experiences were effective in increasing student awareness of critical issues related to computer science education, increasing their knowledge and skills related to Scratch, increasing their self perception as potential mentors in basic computer programming concepts, and increasing their attraction to STEM majors and endorsements.
Differences in Speech Recognition Between Children with Attention Deficits and Typically Developed Children Disappear When Exposed to 65 dB of Auditory Noise
Venue: Front. Psychol.

The most common neuropsychiatric condition in the in children is attention deficit hyperactivity disorder (ADHD) affecting ~6 9% of the population. ADHD is distinguished by inattention and hyperactive, impulsive behaviors as well as poor performance in various cognitive tasks often leading to failures at school. Sensory and perceptual dysfunctions have also been noticed. Prior research has mainly focused on limitations in executive functioning where differences are often explained by deficits in pre frontal cortex activation. Less notice has been given to sensory perception and subcortical functioning in ADHD. Recent research has shown that children with ADHD diagnosis have a deviant auditory brain stem response compared to healthy controls. The aim of the present study was to investigate if the speech recognition threshold differs between attentive and children with ADHD symptoms in two environmental sound conditions, with and without external noise. Previous research has namely shown that children with attention deficits can benefit from white noise exposure during cognitive tasks and here we investigate if noise benefit is present during an auditory perceptual task. For this purpose we used a modified Hagerman's speech recognition test where children with and without attention deficits performed a binaural speech recognition task to assess the speech recognition threshold in no noise and noise conditions (65 dB) Results showed that the inattentive group displayed a higher speech recognition threshold than typically developed children and that the difference in speech recognition threshold disappeared when exposed to noise at supra threshold level. From this we conclude that inattention can partly be explained by sensory perceptual limitations that can possibly be ameliorated through noise exposure.
Associations of Parenting Styles and Dimensions with Academic Achievement in Children and Adolescents: A Meta analysis

Parents and researchers alike are interested in how to promote children's academic competence. The present meta analysis integrates the results of 308 empirical studies on associations of general parenting dimensions and styles with academic achievement of children and adolescents assessed via grade point average or academic achievement tests. Parental responsiveness (warmth) behavioral control, autonomy granting, and an authoritative parenting style were associated with better academic performance both concurrently and in longitudinal studies, although these associations were small in a statistical sense. Parental harsh control, and psychological control, as well as neglectful, authoritarian, and permissive parenting styles were related to lower achievement with small to very small effect sizes. With three exceptions, parenting dimensions and styles also predicted change in academic achievement over time. Moderating effects of child age, ethnicity, reporter on parenting and academic achievement, quality of the parenting and achievement measure, and publication status were identified. It is concluded that associations of academic achievement with general parenting dimensions/styles tend to be smaller than associations of school specific parental involvement which have been addressed in previous meta analyses.
The impact of trial stage, developer involvement and international transferability on universal social and emotional learning programme outcomes: a meta analysis

This study expands upon the extant prior meta analytic literature by exploring previously theorised reasons for the failure of school based, universal social and emotional learning (SEL) programmes to produce expected results. Eighty nine studies reporting the effects of school based, universal SEL programmes were examined for differential effects on the basis of: (1) stage of evaluation (efficacy or effectiveness) (2) involvement from the programme developer in the evaluation (led, involved, independent) and (3) whether the programme was implemented in its country of origin (home or away) A range of outcomes were assessed including: social emotional competence, attitudes towards self, pro social behaviour, conduct problems, emotional distress, academic achievement and emotional competence. Differential gains across all three factors were shown, although not always in the direction hypothesised. The findings from the current study demonstrate a revised and more complex relationship between identified factors and dictate major new directions for the field.
Local Political Power and Housing Bubble in Spain

This paper provides empirical evidence of the role of local politicians in the development of the housing bubble in Spain. It intends to determine empirically whether different political ideologies matter and whether municipalities run by different political parties show housing bubbles of different intensity. We work with all municipalities over 25,000 inhabitants. We measure political ideology according to the political party to which the mayor of a municipality belongs. The housing bubble is proxied by the stock of unsold new dwellings. The results suggest an association between the local political ideology and existing unsold new dwellings. Municipalities governed by right wing mayors have a higher number and share of unsold new dwellings than those governed by centre left and left wing mayors.
Local political power and housing bubble in Spain

espanolEste trabajo proporciona evidencia empirica sobre el papel de los politicos locales en el desarrollo de la burbuja inmobiliaria en Espana. Pretende estudiar, desde una perspectiva empirica, si la ideologia politica es relevante, de manera que los municipios dirigidos por diferentes partidos politicos presentan burbujas de distinta intensidad. En el trabajo se incluyen todos los municipios de mas de 25.000 habitantes. La ideologia politica es aproximada por el partido politico al que pertenece el alcalde de cada municipio. La burbuja inmobiliaria, por su parte, se aproxima por el stock de viviendas nuevas pendientes de venta. Los resultados sugieren que existe una asociacion entre la ideologia politica del municipio y las viviendas nuevas pendientes de venta existente. Los municipios gobernados por alcaldes de derecha tienen un mayor volumen (y proporcion) de viviendas nuevas en venta que los municipios gobernados por alcaldes de izquierda y centro izquierda. EnglishThis paper provides empirical evidence of the role of local politicians in the development of the housing bubble in Spain. It intends to determine empirically whether different political ideologies matter and whether municipalities run by different political parties show housing bubbles of different intensity. We work with all municipalities over 25,000 inhabitants. We measure political ideology according to the political party to which the mayor of a municipality belongs. The housing bubble is proxied by the stock of unsold new dwellings. The results suggest an association between the local political ideology and existing unsold new dwellings. Municipalities governed by right wing mayors have a higher number and share of unsold new dwellings than those governed by centre left and left wing mayors.
Economic and Social Power in Spain: corporate networks of banks, utilities and other large companies (1917 2009)

The evolution trend of the Spanish network has not differed essentially from the path of other Western European corporate networks, but the configuration and factors that explain it have a specific pattern in accordance with their economic and social characteristics. The exchange of directors among the largest banks and utilities in Spain was one of the instruments used to consolidate its hegemonic position and to limit competition in other sectors. Network analysis confirms the existence of a crony capitalism, created in a context of institutional weakness and dominance of undemocratic political systems. The extractive elite used the network boards to restrict competition in key economic sectors, at the expense of the economic development of the country, until the liberalisation of the domestic market and its integration into the global economy.
Fast and Furious: Podemos' Quest for Power in Multi level Spain

After a stunning breakthrough in the 2014 European election, Podemos became the third largest parliamentary party in Spain in the 2016 general election and achieved representation in all regions. This article examines how Podemos has adapted to the opportunities and dilemmas posed by Spain's multi level setting in several ways: its formation in early 2014 and its evolution since; its ideological principles and policy menu; its organisation; and its political strategies and electoral alliances. The hypotheses test the effects of adaptation to multi level competition on the party electorate. The findings indicate the positive effect of support for more decentralisation on the likelihood of voting for Podemos, but also how this effect is territorially differentiated and could affect the party's cohesion and internal debate.
CARTOGRAPHY AND POWER IN THE CONQUEST AND CREATION OF NEW SPAIN

With the so called linguistic turn, historians have begun to study the ways in which a multitude of cultural forms are imbricated in the colonial and imperial project. In analyzing the infinite ways in which power is exercised and manifested, historians are turning a critical eye toward a myriad of cultural pro ductions for a better understanding of how culture, politics, and power work in concert. One example is the increasing scrutiny given to geographical concep tions and representations. In Latin American colonial studies, a number of recent works have analyzed the ways in which deep, culturally rooted structures of spa tial perception and representation have influenced the colonial process. This essay attempts to bring a number of those works into meaningful dialogue with one another with respect to the cultural and political facets of cartography. It also introduces work by scholars studying other regions of the world that may push the field farther and the work of the "new cultural cartographers" who have prob lematized traditional notions about the mimetic quality of maps and their pre sumed objectivity. In sum, this essay surveys recent literature pertaining to colo nial cartography in Latin America, analyzes a number of comparative and theoretical studies that may broaden future research, and suggests that cartogra phy and maps offer a fruitful avenue for further study and analysis of colonial ism, imperialism, and state formation.
An Efficient Buffer Management Policy for DTN

Delay or Disruption Tolerant Networks (DTNs) are challenged networks where an end to end path may not always exist, due to which it is necessary to use a store carry forward paradigm for routing messages from source to destination. DTNs have emerged from MANETs, inheriting their typical properties like mobility, network partitioning, sparse network structure, etc. differing in the inability to use IP. Due to the high mobility of nodes, and limited radio transmission range, two nodes may not always be able to communicate with each other. Thus, communication is established with the help of encounter opportunities between nodes. So intelligent relay selection plays an important role in routing performance. But apart from relay selection, effective buffer management policies also have an impact on routing performance. In this paper we have discussed existing buffer management methods in literature and proposed a novel buffer management scheme based on hop count and TTL, which uses partial network knowledge. Experimental results show that the proposed buffer management scheme outperforms existing buffer management policies in terms of higher delivery rate and lower overhead ratio.
Detecting informal settlements from high resolution imagery using an object based image approach

The aim of this study was twofold: evaluate different approaches to deriving normalised digital surface models (nDSM) and develop a robust and transferable methodology for mapping informal dwellings. In the first component, three approaches to extract nDSMs were investigated: (i) light detection and ranging (LiDAR) data, (ii) high resolution aerial photographs in a process of image matching, and (iii) a series of aerial images captured using a hand held camera using structure from motion (SfM) techniques. SfM is a relatively new technique that has not been widely used for nDSM extraction. This study represented a first attempt at evaluating the three approaches, particularly for mapping informal dwellings. The accuracy of the respective nDSMs was evaluated using vertical profiles, area based, as well as positional based accuracy assessment metrics. This provided a clear indication of the robustness of each of the models. Results showed that an nDSM can be successfully extracted in an informal settlement for informal dwelling mapping. Overall LiDAR achieved the highest accuracy in all three accuracy assessments, showing its ability to handle the undefined and complex morphology of informal settlements. To further test the robustness of the nDSMs, each model was applied to an independent test site with varying dwelling density and achieved improved accuracies. In the second component, the utility of high resolution WorldView 2 imagery and object based image analysis (OBIA) techniques to develop a robust and transferable methodology for mapping individual informal dwellings in the City of Cape Town was tested. A systematic approach was used to objectively identify segmentation and classification parameters. The supervised segmentation parameter tuner (SPT) tool was used to derive optimal segmentation parameters, and was evaluated using an area based accuracy assessment which resulted in high compactness 86% and correctness >88% To reduce data dimensionality and optimize the classification process, the RF algorithm reduced the original WV 2 feature set (n=40) and aerial imagery (n=60) feature sets by 23% and 53% whereas the CART algorithm reduced the same feature set by 95% and 91% respectively. For classification, a supervised approach was adopted using the random forest (RF) algorithm, and a rule based classification using a rule set in eCognition software. Although different feature subsets were selected by the RF and CART algorithm for the WV 2 and aerial imagery, similar classification accuracies were achieved in all the test sites.
Phase II monitoring of generalized linear profiles using weighted likelihood ratio charts
Venue: Comput. Ind. Eng.

We focus on effective profile monitoring for discrete response.We provide a unified framework for generalized linear profile monitoring.A new control chart is developed based on the weighted likelihood ratio test.Our approach can be readily extended to other general profiles. In recent years, effective profile monitoring for discrete response variables, such as binary, multinomial, ordinal or Poisson variables, has increasingly attracted interest of researchers in the area of statistical process control. Such quality characteristics are often modeled as special cases of generalized linear models. The objective of this paper is to try to provide a unified framework for Phase II monitoring of generalized linear profiles of which the explanatory variables can be fixed design or random arbitrary design. To this end, a new control chart is developed based on the weighted likelihood ratio test, and it can be readily extended to other generalized profiles or profiles with random predictors if the likelihood function can be obtained. Numerical results and illustrative example show that the proposed control chart has satisfactory in control run length distribution and stands out at early detection.
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Recent approaches in depth based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes. Currently available depth based and RGB+Dbased action recognition benchmarks have a number of limitations, including the lack of training samples, distinct class labels, camera views and variety of subjects. In this paper we introduce a large scale dataset for RGB+D human action recognition with more than 56 thousand video samples and 4 million frames, collected from 40 distinct subjects. Our dataset contains 60 different action classes including daily, mutual, and health related actions. In addition, we propose a new recurrent neural network structure to model the long term temporal correlation of the features for each body part, and utilize them for better action classification. Experimental results show the advantages of applying deep learning methods over state of the art handcrafted features on the suggested cross subject and cross view evaluation criteria for our dataset. The introduction of this large scale dataset will enable the community to apply, develop and adapt various data hungry learning techniques for the task of depth based and RGB+D based human activity analysis.
Conditional Random Fields based approach for real time building occupancy estimation with multi sensory networks

Automated real time occupancy monitoring in buildings plays an important role in increasing energy efficiency and provides facility managers with useful information about the usage of different spaces. In this paper, a novel approach is proposed for estimating real time occupancy in buildings, based on Conditional Random Field probabilistic models, utilizing data from different sensor types. Three different types of occupancy information are considered: presence/absence, actual number of occupants and occupancy density. The proposed occupancy estimation method has been applied to four spaces with different characteristics in a real life testbed environment. Experimental results revealed that the proposed method yielded high accuracy for different sensor combinations in all tested configurations regarding the occupancy granularity and the space type, and outperformed the Hidden Markov Model based method.
Chinese word segmentation based on conditional random fields with character clustering
Venue: 2016 International Conference on Asian Language Processing (IALP)

Chinese word segmentation plays an important role in Chinese text mining. It is the foundation of automatic relation extraction and identification in Chinese information processing. In this paper, we propose a method for Chinese word segmentation based on conditional random fields (CRF) with character clustering. For the character clustering, we firstly use the Skip Gram model to obtain character embedding from a raw corpus (without word delimiters) We then apply two different clustering algorithms, K means and Brown clustering algorithm, to get the clusters of character embedding. The effect of different numbers of dimensions of character embedding, the number of clusters, and different clustering algorithms have been studied. We verify our method using the 4th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC2015) Weibo text segmentation task. Our system achieves an F score of 95.67% and an out of vocabulary (OOV) rate of 94.78% The result shows that clustering character embedding based on character representation can improve the performance of Chinese word segmentation on short text.
An Improved Community Detection Algorithm Based on the Distance Dynamics
Venue: 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS)

To solve the slow convergence or non convergence problem of traditional distance dynamics method, an improved community detection algorithm based on distance dynamics is proposed in this paper for speeding up the time efficient and accuracy of algorithm. Our improved algorithm firstly analyzes the node cohesiveness and slow convergence problems. Secondly, the neighbor cohesion is defined and two new interaction patterns are designed to enhance the accuracy of community. Finally, the convergence coefficient is defined to judge whether the dynamic interaction process is in the slow convergence or non convergence status. In the dynamic interaction process, when the percentage of non converged distance is less than convergence coefficient, the final value of the distances are pre judged and the whole algorithm is over. The experimental results show our improved algorithm has a fast convergent speed and accurate detection results.
A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection

In this paper, we proposed a link density clustering (LDC) method for overlapping community detection based on density peaks. We firstly use an extended cosine link distance metric to reflect the relationship of links. Then we introduce a clustering algorithm with fast search for solving the link clustering (LC) problem by density peaks with box plot strategy to determine the cluster centers automatically. Finally, we acquire both the link communities and the node communities. Our algorithm is compared with other representative algorithms through substantial experiments on real world networks. The experimental results show that our algorithm consistently outperforms other algorithms in terms of modularity and coverage.
Hierarchical Question Image Co Attention for Visual Question Answering
Venue: NIPS

A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling "where to look" or visual attention, it is equally important to model "what words to listen to" or question attention. We present a novel co attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question (and consequently the image via the co attention mechanism) in a hierarchical fashion via a novel 1 dimensional convolution neural networks (CNN) Our model improves the state of the art on the VQA dataset from 60.3% to 60.5% and from 61.6% to 63.3% on the COCO QA dataset. By using ResNet, the performance is further improved to 62.1% for VQA and 65.4% for COCO QA.