nils-herrmann/clean_scirepeval huggingface dataset:
Using bidirectional lstm recurrent neural networks to learn high-level abstractions of sequential features for automated scoring of non-native spontaneous speech

We introduce a new method to grade non-native spoken language tests automatically. Traditional automated response grading approaches use manually engineered time-aggregated features (such as mean length of pauses). We propose to incorporate general time-sequence features (such as pitch) which preserve more information than time-aggregated features and do not require human effort to design. We use a type of recurrent neural network to jointly optimize the learning of high level abstractions from time-sequence features with the time-aggregated features. We first automatically learn high level abstractions from time-sequence features with a Bidirectional Long Short Term Memory (BLSTM) and then combine the high level abstractions with time-aggregated features in a Multilayer Perceptron (MLP)/Linear Regression (LR). We optimize the BLSTM and the MLP/LR jointly. We find such models reach the best performance in terms of correlation with human raters. We also find that when there are limited time-aggregated features available, our model that incorporates time-sequence features improves performance drastically.
Classification : Decision Forests and SVM on Interval and DTW Features

This paper describes the methods used for our submission to the KDD 2007 Challenge on Time Series Classification. For each dataset we selected from a pool of methods (individual classifiers or classifier ensembles) using cross validation (CV). Three types of classifiers were considered: nearest neighbour (using DTW), support vector machines (linear and perceptron kernel) and decision forests (boosting, random forest, rotation forest and random oracles). SVM and decision forests used extracted features of two types: similarity-based and interval-based. Two feature selection approaches were applied: FCBF or SVM-RFE. Where the minimum CV errors of several classifiers tied, the labels were assigned through majority vote. We report results with both the practice and the contest data.
Large eddy simulations of the HyShot II scramjet

The present work is part of a broad effort toward predictive simulations of complex flows at high Reynolds numbers. The main objective of the Stanford PSAAP Center (Pre-dictive Science Academic Alliance Program) is to predict the reacting flow in the HyShot II scramjet experiment carried out in the HEG facility of the German Aerospace Agency DLR (cf. Laurence et al. 2011). Specifically, the objective is to predict the best-estimate of the flow, to estimate the uncertainties from a range of sources in this prediction, and, finally , to quantify the margin to engine unstart. Unstart occurs in supersonic internal flows when the flow becomes choked (e.g., due to excessive friction or heat addition), which initiates an unsteady unstart process that eventually leads to subsonic flow throughout. The unstart process involves a propagation of shock waves upstream in the combustor, the speed of which depends strongly on the amount of heat addition. The experiments by Laurence et al. (2011) indicate propagation velocities of 31 m/s at equivalence ratio (ER) of 0.5, 93 m/s at ER=0.7, and above 200 m/s at ER=1.1. For this problem with fixed mass flux of incoming air, the equivalence ratio is proportional to the fuel mass flux. The length of the HyShot combustor is about 300 mm; thus the unstart process takes about 10 ms at ER=0.5. The experiments were carried out in a shock tube with a test time of about 2.5 ms; thus the unstart process is significantly longer than the test time near the unstart boundary. For this reason, it is clear that shock tube experiments can not give a precise unstart bound. An alternative approach is to use a validated large eddy simulation (LES) method to explore and determine the unstart bound. This is the approach taken here, and the motivation of the work described in this brief. The complexity of the flow (turbulence, shocks, mixing, combustion) implies that a careful multi-stage validation plan is needed. Moreover, the available quantitative experimental data for the HyShot case is limited to measurements of pressure (mean and rms) and heat transfer (although these are highly uncertain) along two lines in the combustor. Therefore, validation on the HyShot problem alone is not sufficient. The first step in the validation is to compute a supersonic flat plate boundary layer as a basic check on the LES method and, specifically, the modeling of the friction and heat transfer at …
Optimization, Approximation, and Complexity Classes

W e define a natural variant of NP, MAX NP, and also a subclass called MAX SNP. These are classes of optimization problems, and in fact contain several natural, well-studied ones. W e show that problems in these classes can be approximated with some bounded error. Furthermore, we show that a number of common optimization problems are complete for MAXSNP under a kind of careful transformation (called L-redudon) that preserves approximability. It follows that such a complete problem has a polynomial-t ime approximation scheme iff the whole class does. These results may help explain the lack of progress on the approximabil ity of a host of optimization problems.
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II

Multi-objectiveevolutionaryalgorithmswhichusenon-dominatedsorting andsharinghave beenmainly criticizedfor their (i) computational complexity (where is thenumberof objectivesand is thepopulationsize), (ii) non-elitismapproach, and(iii) theneedfor specifyingasharingparameter . In this paper , we suggesta non-dominatedsortingbasedmulti-objective evolutionaryalgorithm(wecalledit theNon-dominatedSortingGA-II or NSGA-II) which alleviatesall theabove threedifficulties.Specifically, a fastnon-dominatedsorting approachwith computationalcomplexity is presented. Second,a selectionoperatoris presentedwhich createsa mating pool by combiningthe parentandchild populationsandselectingthe best(with respectto fitnessand spread) solutions.Simulationresultson five difficult testproblemsshow that theproposedNSGA-II is ableto find muchbetterspreadof solutionsin all problemscomparedto PAES—anotherelitist multi-objective EA which paysspecial attentiontowardscreatinga diversePareto-optimalfront. Becauseof NSGA-II’s low computational requirements, elitist approach, andparameter -lesssharingapproach,NSGA-II shouldfind increasingapplicationsin theyearsto come.
Mimic: visual analytics of online micro-interactions

We present Mimic, an input capture and visual analytics system that records online user behavior to facilitate the discovery of micro-interactions that may affect problem understanding and decision making. As aggregate statistics and visualizations can mask important behaviors, Mimic can help interaction designers to improve the usability of their designs by going beyond aggregates to examine many individual user sessions in detail. To test Mimic, we replicate a recent crowd-sourcing experiment to better understand why participants consistently perform poorly in answering a canonical conditional probability question called the Mammography Problem. To analyze the micro-interactions, the Mimic web application is used to play back user sessions collected through remote logging of client-side events. We use Mimic to demonstrate the value of using advanced visual interfaces to interactively study interaction data. In the Mammography Problem, issues like user confusion, low confidence, and divided-attention were found based on participants changing their answers, doing repeated scrolling, and overestimating a base rate. Mimic shows how helpful detailed observational data can be and how important the careful design of micro-interactions is in helping users to successfully understand a problem, find a solution, and achieve their goals.
Study of the therapeutic effects of a hippotherapy simulator in children with cerebral palsy: a stratified single-blind randomized controlled trial.

To investigate whether hippotherapy (when applied by a simulator) improves postural control and balance in children with cerebral palsy.



Stratified single-blind randomized controlled trial with an independent assessor. Stratification was made by gross motor function classification system levels, and allocation was concealed.



Children between 4 and 18 years old with cerebral palsy.



Participants were randomized to an intervention (simulator ON) or control (simulator OFF) group after getting informed consent. Treatment was provided once a week (15 minutes) for 10 weeks.



Gross Motor Function Measure (dimension B for balance and the Total Score) and Sitting Assessment Scale were carried out at baseline (prior to randomization), end of intervention and 12 weeks after completing the intervention.



Thirty-eight children participated. The groups were balanced at baseline. Sitting balance (measured by dimension B of the Gross Motor Function Measure) improved significantly in the treatment group (effect size = 0.36; 95% CI 0.01-0.71) and the effect size was greater in the severely disabled group (effect size = 0.80; 95% CI 0.13-1.47). The improvements in sitting balance were not maintained over the follow-up period. Changes in the total score of the Gross Motor Function Measure and the Sitting Assessment Scale were not significant.



Hippotherapy with a simulator can improve sitting balance in cerebral palsy children who have higher levels of disability. However, this did not lead to a change in the overall function of these children (Gross Motor Function Classification System level V).
Bidirectional Expansion For Keyword Search on Graph Databases

Relational, XML and HTML data can be represented as graphs with entities as nodes and relationships as edges. Text is associated with nodes and possibly edges. Keyword search on such graphs has received much attention lately. A central problem in this scenario is to efficiently extract from the data graph a small number of the “best” answer trees. A Backward Expanding search, starting at nodes matching keywords and working up toward confluent roots, is commonly used for predominantly text-driven queries. But it can perform poorly if some keywords match many nodes, or some node has very large degree. In this paper we propose a new search algorithm, Bidirectional Search, which improves on Backward Expanding search by allowing forward search from potential roots towards leaves. To exploit this flexibility, we devise a novel search frontier prioritization technique based on spreading activation. We present a performance study on real data, establishing that Bidirectional Search significantly outperforms Backward Expanding search.
The psychology of transcending the here and now.

People directly experience only themselves here and now but often consider, evaluate, and plan situations that are removed in time or space, that pertain to others' experiences, and that are hypothetical rather than real. People thus transcend the present and mentally traverse temporal distance, spatial distance, social distance, and hypotheticality. We argue that this is made possible by the human capacity for abstract processing of information. We review research showing that there is considerable similarity in the way people mentally traverse different distances, that the process of abstraction underlies traversing different distances, and that this process guides the way people predict, evaluate, and plan near and distant situations.
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a per-pixel reconstruction error based on an `-distance. This procedure, however, leads to large residuals whenever the reconstruction includes slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that they cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity that examines inter-dependencies between local image regions, taking into account luminance, contrast, and structural information, instead of simply comparing single pixel values. It achieves significant performance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over state-of-the-art approaches for unsupervised defect segmentation that use per-pixel reconstruction
Extending the CSG Tree - Warping, Blending and Boolean Operations in an Implicit Surface Modeling System

Automatic blending has characterized the major advantage of implicit surface modeling systems. Recently, the introduction of deformations based on space warping and Boolean operations between primitives has increased the usefulness of such systems. We propose a further enhancement which will extend the range of models that can be easily and intuitively defined with a skeletal implicit surface system. We describe a hierarchical method which allows arbitrary compositions of models that make use of blending, warping and Boolean operations. We call this structure the BlobTree. Blending and space warping are treated in the same way as union, difference and intersection, i.e. as nodes in the BlobTree. The traversal of the BlobTree is described along with two rendering algorithms ; a polygonizer and a ray tracer. We present some examples of interesting models which can be made easily using our approach that would be very difficult to represent with conventional systems.
Robust Non-Intrusive Load Monitoring (NILM) with unknown loads

A Non-Intrusive Load Monitoring (NILM) method, robust even in the presence of unlearned or unknown appliances (UUAs) is presented in this paper. In the absence of such UUAs, this NILM algorithm is capable of accurately identifying each of the turned-ON appliances as well as their energy levels. However, when there is an UUA or set of UUAs are turned-ON during a particular time window, proposed NILM method detects their presence. This enables the operator to detect presence of anomalies or unlearned appliances in a household. This quality increases the reliability of the NILM strategy and makes it more robust compared to existing NILM methods. The proposed Robust NILM strategy (RNILM) works accurately with a single active power measurement taken at a low sampling rate as low as one sample per second. Here first, a unique set of features for each appliance was extracted through decomposing their active power signal traces into uncorrelated subspace components (SCs) via a high-resolution implementation of the Karhunen-Loeve (KLE). Next, in the appliance identification stage, through considering power levels of the SCs, the number of possible appliance combinations were rapidly reduced. Finally, through a Maximum a Posteriori (MAP) estimation, the turned-ON appliance combination and/or the presence of UUA was determined. The proposed RNILM method was validated using real data from two public databases: Reference Energy Disaggregation Dataset (REDD) and Tracebase. The presented results demonstrate the capability of the proposed RNILM method to identify, the turned-ON appliance combinations, their energy level disaggregation as well as the presence of UUAs accurately in real households.
Research Paper: Automatic Resolution of Ambiguous Terms Based on Machine Learning and Conceptual Relations in the UMLS

Motivation. The UMLS has been used in natural language processing applications such as information retrieval and information extraction systems. The mapping of free-text to UMLS concepts is important for these applications. To improve the mapping, we need a method to disambiguate terms that possess multiple UMLS concepts. In the general English domain, machine-learning techniques have been applied to sense-tagged corpora, in which senses (or concepts) of ambiguous terms have been annotated (mostly manually). Sense disambiguation classifiers are then derived to determine senses (or concepts) of those ambiguous terms automatically. However, manual annotation of a corpus is an expensive task. We propose an automatic method that constructs sense-tagged corpora for ambiguous terms in the UMLS using MEDLINE abstracts.



For a term W that represents multiple UMLS concepts, a collection of MEDLINE abstracts that contain W is extracted. For each abstract in the collection, occurrences of concepts that have relations with W as defined in the UMLS are automatically identified. A sense-tagged corpus, in which senses of W are annotated, is then derived based on those identified concepts. The method was evaluated on a set of 35 frequently occurring ambiguous biomedical abbreviations using a gold standard set that was automatically derived. The quality of the derived sense-tagged corpus was measured using precision and recall.



The derived sense-tagged corpus had an overall precision of 92.9% and an overall recall of 47.4%. After removing rare senses and ignoring abbreviations with closely related senses, the overall precision was 96.8% and the overall recall was 50.6%.



UMLS conceptual relations and MEDLINE abstracts can be used to automatically acquire knowledge needed for resolving ambiguity when mapping free-text to UMLS concepts.
A multidisciplinary approach to 3D survey and reconstruction of historical buildings

The aim of this research is to suggest a methodology based on 3D survey and reconstructive modeling, suitable to increase the actual knowledge of an historical building and supporting its historical interpretation. The case study used for testing the proposed methodology is the huge Chartreuse of Pavia, with a special focus on a relatively unexplored portion of the monument. The survey, based on 3D laser scanning and orthoimages, integrated by historical studies and other complementary information (thermoluminescence dating, IR imaging, hystorical analysis), allowed to read all the architectural aspects hidden in this highly architecturally stratified monument, improving in this way the comprehension of the building's transformations in time. A 3D reconstruction approach was then suggested, merging several information of different nature, from the actual geometry of the building to the interpretation of historical documents, suggesting a sequence of diachronic models as virtual narration of the historical evolution. On other hand the 3D models were used to obtain a cross-validation of the historical evolution hypotheses developed by experts in the various disciplines involved in the project. The data collected were exploited through a web portal in order to enhance the readability of tangible and intangible heritage associated to that Chartreuse portion, nowadays not accessible to common public.
The association of VDR polymorphisms and type 2 diabetes in older people living in community in Santiago de Chile

Several polymorphisms have been associated with obesity and type 2 diabetes in different populations. To investigate the frequencies of a genetic polymorphism of vitamin D receptor (FokI and BsmI) in patients with T2D. The case–control study was conducted in 138 patients with T2D and 172 control subjects, men and women (60–79 years old). The genotype and allele frequency determination of VDR polymorphisms were determined in these subjects. The frequency of the C allele of the FokI polymorphism was significantly higher in the T2D group than in healthy subjects (p = 0.025). The frequencies of the BsmI variant were similar in subjects with and without T2D (p = 0.747). Consistent with these data, there was an association of the C allele with T2D (OR = 1.74, 95% CI = 1.003–3.084, p = 0.036), but not the AG + GG variants for BsmI (OR = 1.02, 95% CI = 0.635–1.649, p = 0.916). We can observe a significant association between carrier of the T > C variant of FokI and type 2 diabetes, adjusted for vitamin D, age, obesity (overweight and obesity), seasonality, sex and Homa-IR. Here, we show a significant association between the FokI polymorphisms (TC + CC) and T2D with an odds ratio of 1.9001 (95% CI (1.0970–3.6838), p = 0.041). Our study suggests that the C allele (TC + CC) of the VDR-FokI gene is a possible risk factor for T2D in older people living in a community in Santiago de Chile.
Comparing information graphics: a critical look at eye tracking

Effective graphics are essential for understanding complex information and completing tasks. To assess graphic effectiveness, eye tracking methods can help provide a deeper understanding of scanning strategies that underlie more traditional, high-level accuracy and task completion time results. Eye tracking methods entail many challenges, such as defining fixations, assigning fixations to areas of interest, choosing appropriate metrics, addressing potential errors in gaze location, and handling scanning interruptions. Special considerations are also required designing, preparing, and conducting eye tracking studies. An illustrative eye tracking study was conducted to assess the differences in scanning within and between bar, line, and spider graphs, to determine which graphs best support relative comparisons along several dimensions. There was excessive scanning to locate the correct bar graph in easier tasks. Scanning across bar and line graph dimensions before comparing across graphs was evident in harder tasks. There was repeated scanning between the same dimension of two spider graphs, implying a greater cognitive demand from scanning in a circle that contains multiple linear dimensions, than from scanning the linear axes of bar and line graphs. With appropriate task design and targeted analysis metrics, eye tracking techniques can illuminate visual scanning patterns hidden by more traditional time and accuracy results.
A Survey on Clustering Algorithms for Wireless Sensor Networks

A wireless sensor network (WSN)consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.
Quasi-Resonant (QR) Controller With Adaptive Switching Frequency Reduction Scheme for Flyback Converter

A quasi-resonant (QR) controller with an adaptive frequency reduction scheme has been developed for a flyback converter. While maintaining the valley switching, the QR controller reduces the switching frequency for lighter load by skipping some valleys to reduce the power loss and thereby achieve better light-load efficiency. If the QR controller cannot detect any valley due to the damped oscillation of switch voltage, then the valley switching is given up and the nonvalley switching is employed to keep reducing the switching frequency for lighter load. The proposed QR controller has been implemented in a 0.35-μm 700-V BCDMOS process and applied to a 40-W flyback converter. The power efficiency of the flyback converter is improved by up to 3.0% when the proposed QR controller is employed compared to the one employing the conventional QR controller.