nils-herrmann/clean_scirepeval huggingface dataset:
Luzzu A Methodology and Framework for Linked Data Quality Assessment
Venue: JDIQ

The increasing variety of Linked Data on the Web makes it challenging to determine the quality of this data and, subsequently, to make this information explicit to data consumers. Despite the availability of a number of tools and frameworks to assess Linked Data Quality, the output of such tools is not suitable for machine consumption, and thus consumers can hardly compare and rank datasets in the order of fitness for use. This article describes a conceptual methodology for assessing Linked Datasets, and Luzzu; a framework for Linked Data Quality Assessment. Luzzu is based on four major components: (1) an extensible interface for defining new quality metrics; (2) an interoperable, ontology driven back end for representing quality metadata and quality problems that can be re used within different semantic frameworks; (3) scalable dataset processors for data dumps, SPARQL endpoints, and big data infrastructures; and (4) a customisable ranking algorithm taking into account user defined weights. We show that Luzzu scales linearly against the number of triples in a dataset. We also demonstrate the applicability of the Luzzu framework by evaluating and analysing a number of statistical datasets against a variety of metrics. This article contributes towards the definition of a holistic data quality lifecycle, in terms of the co evolution of linked datasets, with the final aim of improving their quality.
Luzzu A Framework for Linked Data Quality Assessment
Venue: 2016 IEEE Tenth International Conference on Semantic Computing (ICSC)

The increasing variety of Linked Data on the Web makes it challenging to determine the quality of this data, and subsequently to make this information explicit to data consumers. Despite the availability of a number of tools and frameworks to assess Linked Data Quality, the output of such tools is not suitable for machine consumption, and thus consumers can hardly compare and rank datasets in the order of fitness for use. This paper describes Luzzu, a framework for Linked Data Quality Assessment. Luzzu is based on four major components: (1) an extensible interface for defining new quality metrics, (2) an interoperable, ontology driven back end for representing quality metadata and quality problems that can be reused within different semantic frameworks, (3) a scalable stream processor for data dumps and SPARQL endpoints, and (4) a customisable ranking algorithm taking into account user defined weights. We show that Luzzu scales linearly against the number of triples in a dataset. We also demonstrate the applicability of the Luzzu framework by evaluating and analysing a number of statistical datasets with regard to relevant metrics.
Towards An Objective Assessment Framework for Linked Data Quality: Enriching Dataset Profiles with Quality Indicators
Venue: Int. J. Semantic Web Inf. Syst.

Ensuring data quality in Linked Open Data is a complex process as it consists of structured information supported by models, ontologies and vocabularies and contains queryable endpoints and links. In this paper, the authors first propose an objective assessment framework for Linked Data quality. The authors build upon previous efforts that have identified potential quality issues but focus only on objective quality indicators that can measured regardless on the underlying use case. Secondly, the authors present an extensible quality measurement tool that helps on one hand data owners to rate the quality of their datasets, and on the other hand data consumers to choose their data sources from a ranked set. The authors evaluate this tool by measuring the quality of the LOD cloud. The results demonstrate that the general state of the datasets needs attention as they mostly have low completeness, provenance, licensing and comprehensibility quality scores.
Quality Work Life as predictor to Organisational Commitment under contrasting Leadership Styles: I.T Responses from Pakistan's private software houses

This study investigate the dimensions of organisational commitment under transformational and transactional leadership style while considering the attributes of Quality Work life prevailing in Pakistan's private software houses. Theoretical framework is developed by considering studies commenced in last three decades on similar topic. This study considers positivist paradigm with objective epistemology, realism ontology, and hyopthetico deductive quantitative method to explore the relationship between research variables. The sample size for study is 408 participants from 10 mega cities of Pakistan through combining probability and nonprobability sampling technique. Self administered survey questionnaire was circulated through HR department of targeted firms.
Influence of a Reverse Delta Type Add On Device on Wake Vortex Alleviation

Particle image velocimetry was used in a low speed wind tunnel to investigate the effect of interactions of vortices produced by a wingtip of a half span wing (NACA 23012 in landing configuration) and a slender reverse delta type add on device placed near the tip on the upper surface of the half span wing. This work investigates the characteristics of the vortex interactions generated downstream in planes perpendicular to the freestream direction and their dependence on angles of attack at a chord based Reynolds number of Rec=2.75x105. It was found that the add on device significantly reduced the tangential velocity, vorticity, and circulation magnitude by up to 82.9, 92.6, and 42.8% respectively, and increased the resultant vortex core radius by a factor of 6.57. A six component force balance was used to obtain the aerodynamic performance of the half span wing model with a reverse delta type add on device. It was found that the reverse delta type add on device marginally affected the aerodynamic perform.
Bidirectional RNN for Medical Event Detection in Electronic Health Records
Venue: HLT NAACL

Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks and show that they significantly out performed the CRF models.
The Roles of Racial Discrimination, Cultural Mistrust, and Gender in Latina/o Youth's School Attitudes and Academic Achievement.
Venue: Journal of research on adolescence the official journal of the Society for Research on Adolescence

Gender gaps in achievement are particularly pronounced among Latina/os, who are among the population most affected by the academic achievement gap. This study examined the roles of racial discrimination, cultural mistrust, and economic value of education in the academic achievement of urban, low income Latina/o adolescents. Participants were 346 high school students. Structural equation modeling revealed that the model of associations was a better fit for male students than for female students. Cultural mistrust mediated the relationship between racial discrimination and values of education, and perceived limitations of education predicted lower attendance rates for male students but not for female students. Implications for future research and interventions are discussed.
Recurrent Neural Network for Text Classification with Multi Task Learning
Venue: IJCAI

Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multi task learning framework to jointly learn across multiple related tasks. Based on recurrent neural network, we propose three different mechanisms of sharing information to model text with task specific and shared layers. The entire network is trained jointly on all these tasks. Experiments on four benchmark text classification tasks show that our proposed models can improve the performance of a task with the help of other related tasks.
ACTITUD HACIA LA MATEMATICA Y EL ROL DE LAS TIC EN LOS PROCESOS DE ENSENANZA APRENDIZAJE. UNA APROXIMACION PARA LA DEFINICION DE UN MODELO TEORICO i

The scope of this work focuses on the definition of the path of a study model, which integrates the variables that affect student attitudes toward mathematics and computerassisted instruction. After expert consultation, we identify a problem that is present in public and private institutions of higher education, this problem involves the attitude that the student presents toward mathematics. Once we have contextualized the problem, we analyze the theoretical foundations that explain this construct who refer the variables: attitude affectivity beliefs in the teaching and learning of mathematics, with the variables associated with teaching assisted mathematical computer and the role of ICT in this process. With this proposed model, we could carry out an empirical study and with the results; we could contribute to knowledge about teaching mathematics. In addition, the proposal of this study model and the empirical findings, it could encourage the orientation of the processes into new fields of application, example of that, the programming of financial simulators.
Relational Aggression, Victimization and Self Concept: Testing Pathways from Middle Childhood to Adolescence
Venue: Journal of youth and adolescence

When studying adolescent development, it is important to consider two key areas that are salient for teens, which are self concept and peer relations. A secondary analysis of the National Institute of Health and Human Development Study of Early Child Care and Youth Development was conducted to examine the prospective bidirectional associations between self concept and peer relations. To date, how social development broadly and peer relations in particular (e.g. relational aggression and victimization) affect self concept domains is not fully understood. Using a large sample (N 1063; 532 girls; M 11.14 years; SD .59) with multiple informants, the present study examined whether fifth grade relational aggression and sixth grade relational victimization was associated with adolescent self concept in three key domains (i.e. academic, sports, physical appearance) A significant direct effect emerged, such that relational aggression in middle childhood was associated with decreases in academic self concept and increases in sports self concept in adolescence. Analyses also revealed that having higher levels of domain specific self concept led to decreases in relational aggression across the transition to adolescence. The findings highlight the importance of examining bidirectional prospective associations between relational aggression, relational victimization, and domain specific self concept. Implications for future research and clinical intervention are discussed.
Positive attitude program's impact upon self concept across childhood and adolescence

EnglishThis study aims to analyze the differential impact of social and emotional learning programs between Portuguese elementary and middle school students, and to clarify developmental and gender differences in children and adolescents self concept. The sample included 2682 students, 1237 elementary students (4th grade; Mage 9.24; SD 0.72) and 1445 middle school students (7th to 9th grade: Mage 13.30; SD 1.32) Self report questionnaires were administered before and after intervention. Multilevel linear modeling with a repeated measures design was used to evaluate the effects of the program on self concept. Results show significant intervention gains in social and emotional self concept, which differ by grade level (elementary students benefited more) There were also differences between genders, boys showed more benefits in social self concept and girls in emotional self concept as well as a smaller decrease in academic self concept. Participation in the programs led to more pronounced gains for elementary school students. portuguesEste estudo teve como objetivo analisar o impacto diferencial de dois programas de aprendizagem socioemocional de alunos portugueses do 1.o e 3.o ciclo, bem como clarificar as diferencas de genero e desenvolvimento no seu autoconceito. A amostra 2682 alunos, 1237 do 4.o ano; Midade 9.24; DP 0.72) e 1.445 de 7.o ao 9.o ano (Midade 13.30; DP 1.32) Foram aplicados questionarios de autorrelato antes e depois da intervencao. Para avaliar os efeitos dos programas foram utilizados modelos lineares multinivel. Os resultados mostram aumentos significativos no autoconceito social e emocional nos grupos de intervencao, com os alunos de 4.o ano a beneficiarem mais. Tambem foram encontradas diferencas entre generos, os rapazes apresentam maiores ganhos no autoconceito social, e as raparigas no autoconceito emocional, assim como uma diminuicao menor no autoconceito academico. A participacao nos programas conduziu a aumentos mais pronunciados nos alunos de 4.o ano.
Evaluation of an innovative approach based on prototype engineered wetland to control and manage boron (B) mine effluent pollution
Venue: Environmental Science and Pollution Research

AbstractA major environmental problem associated with boron (B) mining in many parts of the world is B pollution, which can become a point source of B mine effluent pollution to aquatic habitats. In this study, a cost effective, environment friendly, and sustainable prototype engineered wetland was evaluated and tested to prevent B mine effluent from spilling into adjoining waterways in the largest B reserve in the world. According to the results, average B concentrations in mine effluent significantly decreased from 17.5 to 5.7 mg l 1 after passing through the prototype with a hydraulic retention time of 14 days. The results of the present experiment, in which different doses of B had been introduced into the prototype, also demonstrated that Typha latifolia (selected as donor species in the prototype) showed a good resistance to alterations against B mine effluent loading rates. Moreover, we found that soil enzymes activities gradually decreased with increasing B dosages during the experiment. Boron mass balance model further showed that 60 of total B was stored in the filtration media, and only 7 of B was removed by plant uptake. Consequently, we suggested that application of the prototype in the vicinity of mining site may potentially become an innovative model and integral part of the overall landscape plan of B mine reserve areas worldwide.
Fifty Years On: The 'Great Proletarian Cultural Revolution' In the Age of the Internet Still Haunting China

This article takes as its starting point the current discussion in China about how to assess the Cultural Revolution CR of 1966 1976 and whether it might recur in one form or another. For readers unfamiliar with this movement, we first present an overview of its origins, course and consequences. Since this modern Chinese trauma is inseparable from the political views and objectives of its initiator, Mao Zedong, the latter's role will also be addressed. Second, we will show that while the CR is indeed officially a largely unaddressed and under researched issue in China (frequently described as a taboo) it nevertheless remains the subject of an intensive, ongfoing debate, particularly on the Internet. To illustrate this, we will examine recent public discourse in China to show that a societal reconstruction of collective memory about lessons learned and notions of a recurrence of the CR is actually taking place in a new social space in the form of 'connective action' Third, we will enlarge on whether there are indications of the emergence of a new CR under the leadership of Xi Jinping. We conclude that despite leftist voices there are no real indications that the Cultural Revolution will be repeated.
Pengujian efektivitas berbagai jenis dan konsentrasi sitokinin terhadap multiplikasi tunas mikro pisang (Musa paradisiaca L. secara In Vitro

Perbanyakan tanaman pisang secara konvensional dengan menggunakan bonggol atau anakan sucker secara terus menerus dapat menurunkan kualitas dan memerlukan waktu lama serta meningkatkan terjadinya serangan penyakit. Oleh karena itu, suatu upaya untuk menghasilkan bibit yang memiliki kualitas baik dalam waktu yang cepat dapat menggunakan kultur in vitro Percobaan ini bertujuan untuk memperoleh laju multiplikasi yang tinggi dengan penggunaan salah satu jenis dan konsentrasi sitokinin pada kultur in vitro tanaman pisang kepok kuning. Percobaan dilakukan pada bulan Oktober hingga Desember 2015 di Laboratorium Kultur Jaringan Teknologi Benih Fakultas Pertanian Universitas Padjadjaran. Rancangan percobaan menggunakan Rancangan Acak Lengkap (RAL) yang terdiri dari 8 perlakuan penggunaan jenis dan konsentrasi sitokinin yang berbeda (2 mg L 1 BAP, 4 mg L 1 BAP, 2 mg L 1 2 iP, 4 mg L 1 2 iP, 0,08 mg L 1 TDZ, 0,2 mg L 1 TDZ, 2 mg L 1 Kn, 4 mg L 1 Kn) Hasil percobaan menunjukkan bahwa 2 mg L 1 BAP merupakan jenis sitokinin yang mampu menghasilkan tingkat multiplikasi yang tinggi pada peubah persentase eksplan bertunas dan tinggi tunas (cm) Kata Kunci efektivitas, kepok kuning, multiplikasi, pisang, sitokinin, tunas
Deciphering a Meal through Open Source Standards: Soylent and the Rise of Diet Hackers
Venue: CHI Extended Abstracts

A life without food a leitmotiv of science fiction dystopias is becoming a voluntary choice for thousands of people consuming the powdered food replacement soylent. The DIY soylent powders are designed by a group of nutrition hobbyists who distrust existing healthy eating standards and crowdsource experimental soylent recipes in their online user forums. This DIY food tech rebellion offers an opportunity to look at the issues around present food standards and policies from the HCI perspective. This paper reports findings from in depth interviews with 21 soylent dieters, with the aim of identifying the risks and opportunities of the expert amateur DIY food practices.
Distributed joint flow radio and channel assignment using partially overlapping channels in multi radio wireless mesh networks
Venue: Wirel. Networks

Equipping mesh nodes with multiple radios that support multiple wireless channels is considered a promising solution to overcome the capacity limitation of single radio wireless mesh networks. However, careful and intelligent radio resource management is needed to take full advantage of the extra radios on the mesh nodes. Flow radio assignment and channel assignment procedures should obey the physical constraints imposed by the radios as well as the topological constraints imposed by routing. Varying numbers of wireless channels are available for the channel assignment procedure for different wireless communication standards. To further complicate the problem, the wireless communication standard implemented by the radios of the wireless mesh network may define overlapping as well as orthogonal channels, as in the case of the IEEE 802.11b/g family of standards. This paper presents Distributed Flow Radio Channel Assignment, a distributed joint flow radio and channel assignment scheme and the accompanying distributed protocol in the context of multi channel multi radio wireless mesh networks. The scheme's performance is evaluated on small networks for which the optimal flow radio and channel configuration can be computed, as well as on large random topologies.
Deep Machine Learning and Neural Networks: An Overview

Accepted May 23, 2017 Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine learning or to design intelligent machines. In this paper brief introduction to all machine learning paradigm and application area of deep machine learning and different types of neural networks with applications is discussed.
Identity Mappings in Deep Residual Networks
Venue: ECCV

Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001 layer ResNet on CIFAR 10 (4.62 error) and CIFAR 100, and a 200 layer ResNet on ImageNet. Code is available at: https:/github.com/KaimingHe/resnet 1k layers.
Control of ghost plating by antireflection coating layer stacking on crystalline silicon solar cells
Venue: 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)

During the plating process, ghost plating could appear when the quality of the passivation layer is poor, and it causes an increase in the recombination rate. In this paper, light induced plating was applied, and various passivation layers were investigated to remove ghost plating in crystalline silicon solar cells. These included, (1) SiN<sub>x</sub> (2) double SiN<sub>x</sub> layer, (3) double layer with thermal silicon oxide and SiN<sub>x</sub> and (4) double layer of SiN<sub>x</sub> and SiO<sub>x</sub> by PECVD. For the plated solar cells, a laser was used to remove various ARC layers and phosphoric acid was spin coated onto the doped silicon wafer prior to laser ablation. As a result, a thermal SiO<sub>2</sub>/PECVD SiN<sub>x</sub> layer showed the lowest pinhole density and its wet vapor transmission rate was characterized. The solar cell with the thermal SiO2/PECVD SiNx layer showed the lowest J<sub>02</sub> value, as well as improved V<sub>oc</sub> and J<sub>sc</sub>
Exponential expressivity in deep neural networks through transient chaos
Venue: NIPS

We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights. Our results reveal an order to chaos expressivity phase transition, with networks in the chaotic phase computing nonlinear functions whose global curvature grows exponentially with depth but not width. We prove this generic class of deep random functions cannot be efficiently computed by any shallow network, going beyond prior work restricted to the analysis of single functions. Moreover, we formalize and quantitatively demonstrate the long conjectured idea that deep networks can disentangle highly curved manifolds in input space into flat manifolds in hidden space. Our theoretical analysis of the expressive power of deep networks broadly applies to arbitrary nonlinearities, and provides a quantitative underpinning for previously abstract notions about the geometry of deep functions.