nils-herrmann/clean_scirepeval huggingface dataset:
A new hybrid ensemble credit scoring model based on classifiers consensus system approach
Venue: Expert Syst. Appl.

A new hybrid ensemble model for credit scoring problem is proposed.An improved data filtering technique is developed based on GNG method.GNG with MARS combined proved to be better than applying them individually.Our model is validated on four performance measures over seven credit datasets.Classifiers decisions after consensus effectively improved prediction performance. During the last few years there has been marked attention towards hybrid and ensemble systems development, having proved their ability to be more accurate than single classifier models. However, among the hybrid and ensemble models developed in the literature there has been little consideration given to: 1) combining data filtering and feature selection methods 2) combining classifiers of different algorithms; and 3) exploring different classifier output combination techniques other than the traditional ones found in the literature. In this paper, the aim is to improve predictive performance by presenting a new hybrid ensemble credit scoring model through the combination of two data pre processing methods based on Gabriel Neighbourhood Graph editing (GNG) and Multivariate Adaptive Regression Splines (MARS) in the hybrid modelling phase. In addition, a new classifier combination rule based on the consensus approach (ConsA) of different classification algorithms during the ensemble modelling phase is proposed. Several comparisons will be carried out in this paper, as follows: 1) Comparison of individual base classifiers with the GNG and MARS methods applied separately and combined in order to choose the best results for the ensemble modelling phase; 2) Comparison of the proposed approach with all the base classifiers and ensemble classifiers with the traditional combination methods; and 3) Comparison of the proposed approach with recent related studies in the literature. Five of the well known base classifiers are used, namely, neural networks (NN) support vector machines (SVM) random forests (RF) decision trees (DT) and naive Bayes (NB) The experimental results, analysis and statistical tests prove the ability of the proposed approach to improve prediction performance against all the base classifiers, hybrid and the traditional combination methods in terms of average accuracy, the area under the curve (AUC) H measure and the Brier Score. The model was validated over seven real world credit datasets.
Beta boosted ensemble for big credit scoring data

In this work we present a novel ensemble model for a credit scoring problem. The main idea of the approach is to incorporate separate beta binomial distributions for each of the classes to generate balanced datasets that are further used to construct base learners that constitute the final ensemble model. The sampling procedure is performed on two separate ranking lists, each for one class, where the ranking is based on prepotency of observing positive class. Two strategies are considered: one assumes mining easy examples and the second one forces good classification of hard cases. The proposed solutions are tested on two big datasets on credit scoring.
Beta Boosted Ensemble for Big Credit Scoring Data

In this work we present a novel ensemble model for a credit scoring problem. The main idea of the approach is to incorporate separate beta binomial distributions for each of the classes to generate balanced datasets that are further used to construct base learners that constitute the final ensemble model. The sampling procedure is performed on two separate ranking lists, each for one class, where the ranking is based on prepotency of observing positive class. Two strategies are considered: one assumes mining easy examples and the second one forces good classification of hard cases. The proposed solutions are tested on two big datasets on credit scoring.
AnalyticalInk: An Interactive Learning Environment for Math Word Problem Solving
Venue: IUI

We present AnalyticalInk, a novel math learning environment prototype that uses a semantic graph as the knowledge representation of algebraic and geometric word problems. The system solves math problems by reasoning upon the semantic graph and automatically generates conceptual and procedural scaffoldings in sequence. We further introduces a step wise tutoring framework, which can check students' input steps and provide the adaptive scaffolding feedback. Based on the knowledge representation, AnalyticalInk highlights keywords that allow users to further drag them onto the workspace to gather insight into the problem's initial conditions. The system simulates a pen and paper environment to let users input both in algebraic and geometric workspaces. We conducted an usability evaluation to measure the effectiveness of AnalyticalInk. We found that keyword highlighting and dragging is useful and effective toward math problem solving. Answer checking in the tutoring component is useful. In general, our prototype shows the promise in helping users to understand geometrical concepts and master algebraic procedures under the problem solving.
TEORI KEPRIBADIAN SOCIAL COGNITIVE: KAJIAN PEMIKIRAN ALBERT BANDURA PERSONALITY THEORY SOCIAL COGNITIVE: ALBERT BANDURA

The various viewpoints of personality experts apparently caused Albert Bandura to develop his investigation focus in psychology. Through Bandura's theory developed, the social learning theory or social cognitive more refers to Skinner view about conditioning theory. Bandura looked at the existence of personality theories such as; psychoanalysis, emphasized on the strength of unconsciousness and the influence of childhood. The characteristic theory is too focused on consistency of behavior in various situations. Evolution theory believed that the biological factor would not change. As well as the basic principle of behaviorism theory which regards organism as a controlled side by the rewards and punishments from the environment, he still lacks detail compared by the approach theory he developed more subtle, even significant. It was not because the basic assumption that is owned by this theory that humans have flexibility to learn about different types of behavior in different situation, and humans also have the capacity to control their life. Because according to Bandura, human was the producer and as product of social system. That basic assumption then he developed with the view that the development of human personality by the social cognitive theory of learning acquired through observation and the existence of self efficacy
Gender Problems in the Practice of Professional Interpreters Assisting Migrant Women in Australia
Venue: Violence against women

Professional interpreters in Australia have little opportunity to undertake education on issues of violence against women. This article observes a lack of awareness of gender violence related issues among community interpreters. It then notes examples of gender related problems arising in the work of interpreters assisting migrant women. It repeats Maree Pardy's call for interpreter education, but diverges from her 1995 work by explicitly recommending this education be informed by feminist principles. It observes that, although community interpreters are key to migrant women accessing social services in Australia, the profession resists calls for specialized education. This resistance occurs in a country that is recognized internationally for its progressive public service interpreting policies. We question why this is the case, and suggest that adherence to a peculiarly superficial notion of "impartiality" dulls the profession's responsiveness to the reality of male violence. We draw on government and non governmental organization (NGO) reports, as well as interpreting scholarship using a gender lens, to theorize the need for feminist education of professional community interpreters.
Employee relations in SMEs an empirical approach using the Workplace Employment Relation Survey (WERS 2011)

This thesis is a paper based thesis. Using a large scale matched employee employer dataset, three empirical studies were undertaken to empirically examine: 1) the relationship between employee attitudes, human resource management practices and firm performance in SMEs; 2) the effect of the firm size on firms' and employees' experience during the recent financial crisis, particularly firm's employment related responses to the economic hardship; and 3) the impact of firm size on employee's experience of work stress during economic recession. The findings of the first study suggest a direct relationship between HRM practices and SME firm performance, but this relationship is moderated by high employee job satisfaction. The results suggest that HR policies and practices may improve small firm performance, especially within firms with low levels of commitment and satisfaction. The estimation results presented in the second study show that SMEs are more vulnerable during times of economic hardship than larger firms, but those with HR practices have shown more resilience to the downturn. There is a significant firm size effect on the choice of specific HRM measures in response to the recession, and having HR practices increases the likelihood of the firm to adopt organizational measures. Also, the results indicate that the differences in workers' job experience are moderated by high management formality. For the final empirical research, employees in SMEs experience lower level of overall job stress than those in large enterprise, although the effect disappears once the employee level and firm level characteristics are taken into consideration. Finally, the findings suggest that the association and magnitude of estimated effects of the work stressor presented in the Cooper and Marshall's work stress model differ significantly by firm size. Overall, the thesis has made significant contribution to the employee relations in SMEs literature and provide interesting academic and policy oriented findings.
Lidar detection of high concentrations of ozone and aerosol transported from northeastern Asia over Saga, Japan

To validate products of the Greenhouse gases Observing SATellite (GOSAT) we observed vertical profiles of aerosols, thin cirrus clouds, and tropospheric ozone with a mobile lidar system that consisted of a two wavelength (532 and 1064 nm) polarization lidar and a tropospheric ozone differential absorption lidar (DIAL) We used these lidars to make continuous measurements over Saga (33.24deg N, 130.29deg E) during 20 31 March 2015. High ozone and high aerosol concentrations were observed almost simultaneously in the altitude range 0.5 1.5 km from 03:00 to 20:00 Japan Standard Time (JST) on 22 March 2015. The maximum ozone volume mixing ratio was 110 ppbv. The maxima of the aerosol extinction coefficient and optical depth at 532 nm were 1.2 km 1 and 2.1, respectively. Backward trajectory analysis and the simulations by the Model of Aerosol Species IN the Global AtmospheRe (MASINGAR) mk 2 and the Meteorological Research Institute Chemistry Climate Model, version 2 (MRI CCM2) indicated that mineral dust particles from the Gobi Desert and an air mass with high ozone and aerosol (mainly sulfate) concentrations that originated from the North China Plain could have been transported over the measurement site within about 2 days. These high ozone and aerosol concentrations impacted surface air quality substantially in the afternoon of 22 March 2015. After some modifications of its physical and chemical parameters, MRI CCM2 approximately reproduced the high ozone volume mixing ratio. MASINGAR mk 2 successfully predicted high aerosol concentrations, but the predicted peak aerosol optical thickness was about one third of the observed value.
Improving the maximum torque per ampere in a BLDC motor accounting for transient and continuous operation using thermal boundary conditions
Venue: 2016 XXII International Conference on Electrical Machines (ICEM)

This paper investigates the impact on power density of a BLDC motor using different control strategies. The simulations are carried out using 2D electromechanical FEM calculations, including the frequency converter supply. The maximum steady state output power is determined using thermal FEM calculations. The obtained results shows that an increase of 5.9% in output power can be obtained by running a BLDC motor with sinusoidal currents at rated operation instead of using BLDC currents. In addition, an increase of 18% peak torque can be obtained at rated speed operation when sinusoidal feeding is used instead of BLDC supply. The result is verified with measurements showing a close correlation to the calculation, were 6.5% increase of output power was obtained at rated operation.
Untrusted Business Process Monitoring and Execution Using Blockchain
Venue: BPM

The integration of business processes across organizations is typically beneficial for all involved parties. However, the lack of trust is often a roadblock. Blockchain is an emerging technology for decentralized and transactional data sharing across a network of untrusted participants. It can be used to find agreement about the shared state of collaborating parties without trusting a central authority or any particular participant. Some blockchain networks also provide a computational infrastructure to run autonomous programs called smart contracts. In this paper, we address the fundamental problem of trust in collaborative process execution using blockchain. We develop a technique to integrate blockchain into the choreography of processes in such a way that no central authority is needed, but trust maintained. Our solution comprises the combination of an intricate set of components, which allow monitoring or coordination of business processes. We implemented our solution and demonstrate its feasibility by applying it to three use case processes. Our evaluation includes the creation of more than 500 smart contracts and the execution over 8,000 blockchain transactions.
Non surgical Management of Skeletal Class III Malocclusion with Bilateral Posterior Crossbite: A Case Report.
Venue: Journal of clinical and diagnostic research JCDR

A 16 year old female patient with skeletal Class III malocclusion and bilateral posterior cross bite complaining of difficulty in chewing was treated orthodontically without surgery (camouflage treatment) The treatment comprised of fixed orthodontic treatment with MBT prescription (0.022"x0.028" slot) using quad helix appliance for bilateral expansion of maxillary arch and Class III elastics for occlusal correction. Post treatment records showed normal overbite and overjet with acceptable occlusion. So with this treatment strategy of expanding the maxillary arch using a quad helix appliance and use of Class III elastics, we achieved a good result with optimal occlusion.
Clinical Characteristics and Continued Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor Administration in EGFR mutated Non Small Cell Lung Cancer with Skeletal Metastasis
Venue: Cancer research and treatment official journal of Korean Cancer Association

The aim of this study was to analyze clinical characteristics of skeletal metastasis in epidermal growth factor receptor (EGFR) mutant non small cell lung cancer (NSCLC) and treatment outcomes of continued EGFR tyrosine kinase inhibitor (TKI) therapy in patients presenting with skeletal metastasis progression.  Of the 216 patients treated with EGFR TKI for management of stage III IV NSCLC between 2006 and 2012 in Seoul St. Mary's Hospital, 76 patients with confirmed EGFR mutated NSCLC with skeletal metastases during therapy were analyzed retrospectively.  Of 76 patients with EGFR mutant lung cancer with skeletal metastasis, 37 patients developed first progressive disease (PD) in skeletal regions. EGFR TKI was continued in these 37 patients after first PD in skeletal regions. Median time to first PD of skeletal regions was 8.9 months (95% confidence interval [CI] 4.8 to 13.0) Median time of continued EGFR TKI after first PD of skeletal regions was 8.0 months (95% CI, 2.9 to 13.0) in patients with disease progression of preexisting regions, 5.6 months (95% CI, 4.5 to 6.7) in patients showing new localized regions, and 3.3 months (95% CI, 1.1 to 5.5) in patients with multiple new metastatic regions (p=0.006) Median time of postskeletal metastasis progression survival was 23.0 months (95% CI, 13.5 to 32.5) 15.0 months (95% CI, 3 to 34.7) and 7.0 months (95% CI, 6.0 to 8.0) (p=0.004) in the above described patient groups, respectively. Overall, seven patients (18.9% had more than one episode of skeletal progression of disease without extraskeletal PD.  Continued EGFR TKI treatment with adequate local treatment after progression of skeletal metastasis may be considered for patients who show disease progression in preexisting regions or local progression.
Does Career Risk Deter Potential Entrepreneurs?

Do potential entrepreneurs remain in wage employment because of concerns that they will face worse job opportunities should their entrepreneurial ventures fail? Using a Canadian reform that extended job protected leave to one year for women giving birth after a cutoff date, we study whether the option to return to a previous job increases entrepreneurship. A regression discontinuity design reveals that longer job protected leave increases entrepreneurship by 1.9 percentage points. These entrepreneurs start incorporated businesses that hire employees in industries where experimentation before entry has low costs and high benefits. The effects are concentrated among those with more human and financial capital.
Spatiotemporal Residual Networks for Video Action Recognition
Venue: NIPS

Two stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos. Recently, Residual Networks (ResNets) have arisen as a new technique to train extremely deep architectures. In this paper, we introduce spatiotemporal ResNets as a combination of these two approaches. Our novel architecture generalizes ResNets for the spatiotemporal domain by introducing residual connections in two ways. First, we inject residual connections between the appearance and motion pathways of a two stream architecture to allow spatiotemporal interaction between the two streams. Second, we transform pretrained image ConvNets into spatiotemporal networks by equipping these with learnable convolutional filters that are initialized as temporal residual connections and operate on adjacent feature maps in time. This approach slowly increases the spatiotemporal receptive field as the depth of the model increases and naturally integrates image ConvNet design principles. The whole model is trained end to end to allow hierarchical learning of complex spatiotemporal features. We evaluate our novel spatiotemporal ResNet using two widely used action recognition benchmarks where it exceeds the previous state of the art.
ANALYZING RCD30 OBLIQUE PERFORMANCE IN A PRODUCTION ENVIRONMENT

In 2014 the Institut Cartografic i Geologic de Catalunya (ICGC) decided to incorporate digital oblique imagery in its portfolio in response to the growing demand for this product. The reason can be attributed to its useful applications in a wide variety of fields and, most recently, to an increasing interest in 3d modeling. The selection phase for a digital oblique camera led to the purchase of the Leica RCD30 Oblique system, an 80MPixel multispectral medium format camera which consists of one Nadir camera and four oblique viewing cameras acquiring images at an off Nadir angle of 35o. The system also has a multi directional motion compensation on board system to deliver the highest image quality. The emergence of airborne oblique cameras has run in parallel to the inclusion of computer vision algorithms into the traditional photogrammetric workflows. Such algorithms rely on having multiple views of the same area of interest and take advantage of the image redundancy for automatic feature extraction. The multiview capability is highly fostered by the use of oblique systems which capture simultaneously different points of view for each camera shot. Different companies and NMAs have started pilot projects to assess the capabilities of the 3D mesh that can be obtained using correlation techniques. Beyond a software prototyping phase, and taking into account the currently immature state of several components of the oblique imagery workflow, the ICGC has focused on deploying a real production environment with special interest on matching the performance and quality of the existing production lines based on classical Nadir images. This paper introduces different test scenarios and layouts to analyze the impact of different variables on the geometric and radiometric performance. Different variables such as flight altitude, side and forward overlap and ground control point measurements and location have been considered for the evaluation of aerial triangulation and stereo plotting. Furthermore, two different flight configurations have been designed to measure the quality of the absolute radiometric calibration and the resolving power of the system. To quantify the effective resolution power of RCD30 Oblique images, a tool based on the computation of the Line Spread Function has been developed. The tool processes a region of interest that contains a single contour in order to extract a numerical measure of edge smoothness for a same flight session. The ICGC is highly devoted to derive information from satellite and airborne multispectral remote sensing imagery. A seamless Normalized Difference Vegetation Index (NDVI) retrieved from Digital Metric Camera (DMC) reflectance imagery is one of the products of ICGC's portfolio. As an evolution of this well defined product, this paper presents an evaluation of the absolute radiometric calibration of the RCD30 Oblique sensor. To assess the quality of the measure, the ICGC has developed a procedure based on simultaneous acquisition of RCD30 Oblique imagery and radiometric calibrated AISA (Airborne Hyperspectral Imaging System) imagery.
Extreme multi millennial slip rate variations on the Garlock fault, California: Strain super cycles, potentially time variable fault strength, and implications for system level earthquake occurrence

Pronounced variations in fault slip rate revealed by new measurements along the Garlock fault have basic implications for understanding how faults store and release strain energy in large earthquakes. Specifically, dating of a series of 26.0 3.5 2.5 m fault offsets with a newly developed infrared stimulated luminescence method shows that the fault was slipping at 14.0 2.2 1.8 mm yr approximately twice as fast as the long term average rate, during a previously documented cluster of four earthquakes 0.5 2.0 ka. This elevated late Holocene rate must be balanced by periods of slow or no slip such as that during the ca. 3300 yr long seismic lull preceding the cluster. Moreover, whereas a comparison of paleoseismic data and stress modeling results suggests that individual Garlock earthquakes may be triggered by periods of rapid San Andreas fault slip or very large slip events, the "on off" behavior of the Garlock suggests a longer term mechanism that may involve changes in the rate of elastic strain accumulation on the fault over millennial time scales. This inference is consistent with most models of the geodetic velocity field, which yield slip deficit rates that are much slower than the average latest Pleistocene early Holocene (post 8 13 ka) Garlock slip rate of 6.5 1.5 mm yr These observations indicate the occurrence of millennia long strain "super cycles" on the Garlock fault that may be associated with temporal changes in elastic strain accumulation rate, which may in turn be controlled by variations in relative strength of the various faults in the Garlock San Andreas Eastern California Shear Zone fault system and/or changes in relative plate motion rates.