nils-herrmann/clean_scirepeval huggingface dataset:
Is Active Impedance the Key to a Breakthrough for Legged Robots?

This work addresses the question whether active impedance control is key to a breakthrough for legged robots. In this paper, we will talk about controlling the mechanical impedance of joints and legs with a focus on stiffness and damping control. In contrast to passive elements like springs, active impedance is achieved by torque-controlled joints allowing real-time adjustment of stiffness and damping. We argue that legged robots require a high degree of versatility and flexibility to execute a wide range of assistive tasks to be truly useful to humans and thus to lead to a breakthrough. Adjustable stiffness and damping in realtime is a fundamental building block towards versatility. Experiments with our 80 kg hydraulic quadruped robot HyQ demonstrate that active impedance alone (thus no springs in the structure) can successfully emulate passively compliant elements during highly-dynamic locomotion tasks (running and hopping); and, that no springs are needed to protect the actuation system. Here we present results of a flying trot, also referred to as running trot. To the authors’ best knowledge this is the first time a flying trot was successfully implemented on a robot without passive elements such as springs. A critical discussion on the pros and cons of active impedance concludes the paper. Claudio Semini and Marco Frigerio Dept. of Advanced Robotics, Istituto Italiano di Tecnologia (IIT), via Morego, 30, 16163 Genova e-mail: claudio.semini@iit.it, claudio@semini.ch and marco.frigerio@iit.it Victor Barasuol PPGEAS Dept. of Automation and Systems, Federal University of Santa Catarina (UFSC), Florianpolis, SC, Brazil e-mail: victor.barasuol@posgrad.ufsc.br Thiago Boaventura and Jonas Buchli Agile & Dexterous Robotics Lab, ETH Zurich, Tannenstr. 3, 8092 Zürich e-mail: tboaventura@ethz.ch and buchlij@ethz.ch 1  2013', Springer STAR Series
Understanding User Requirements for Music Information Services

User studies in the music information retrieval and music digital library fields have been gradually increasing in recent years, but large-scale studies that can help detect common user behaviors are still lacking. We have conducted a large-scale user survey in which we asked numerous questions related to users’ music needs, uses, seeking, and management behaviors. In this paper, we present our preliminary findings, specifically focusing on the responses to questions of users’ favorite music related websites/applications and the reasons why they like them. We provide a list of popular music services, as well as an analysis of how these services are used, and what qualities are valued. Our findings suggest several trends in the types of music services people like: an increase in the popularity of music streaming and mobile music consumption, the emergence of new functionality, such as music identification and cloud music services, an appreciation of music videos, serendipitous discovery of music, and customizability, as well as users’ changing expectations of particular types of music information.
An Analytical Model for External Auditor Evaluation of the Internal Audit Function Using Belief Functions

The purpose of this paper is to advance research in internal audit (IA) evaluation by developing an IA assessment model that considers interrelationships among specific factors used by external auditors to evaluate the strength of the IA function. The model is based on three factors identified by auditing expression of the model using the belief function framework. By using this framework we overcome limitations of prior research regarding the modelling of interrelationships among factors and regarding difficulties in application. The results of our analysis revealed that modelling the " And " relationship is essential for assessing the strength of the IA function. As far as interrelationships are concerned, the analysis showed that when the three factors have a strong or a perfect relationship, the strength of the IA function remains high even if there is positive or negative evidence about one of the factors. This result holds as long as there are high levels of beliefs about the other two factors. Further, we demonstrate how the quality of corporate governance affects the evaluation of the IA function and how a cost benefit analysis can be applied to this framework to help determine the amount of external audit work to be carried out for compliance with the Sarbanes-Oxley Act of 2002 (SOX) and the Public Company Accounting Oversight Board (PCAOB) standards. Our analysis revealed that the extent of external audit work to be carried out by the external auditor depends on the strength of the IA function and the amount of litigation and regulatory costs likely to be faced by the external auditor.
A logical formulation of the Boolean model and of weighted Boolean models

In this paper the role of logic as a formal basis to exploit the query evaluation process of the boolean model and of weighted boolean models is analysed. The proposed approach is based on the expression of the constraint imposed by a query term on a document representation by means of the implication connective (by a fuzzy implication in the case of weighted terms). A logical formula corresponds to a query evaluation structure, and the degree of relevance of a document to a user query is obtained as the truth value of the formula expressing the evaluation structure of the considered query under the interpretation corresponding with a document and the query itself.
Content-Centric Energy Management of Mobile Displays

Despite numerous studies to reduce the power consumption of the display-related components of mobile devices, previous works have led to a deterioration in user experience due to compromised graphic quality. In this paper, we propose an effective scheme to reduce the energy consumption of the display subsystems of mobile devices without compromising user experience. In preliminary experiments, we noticed that mobile devices typically perform redundant display updates even if the display content does not change. Based on this observation, we first propose a metric called the content rate, which is defined as the number of meaningful frame changes in a second. Our scheme then estimates an optimal refresh rate based on the content rate in order to eliminate redundant display updates. Also proposed is the flicker compensation technique, which prevents the flickering problem caused by the reduced refresh rate. Extensive experiments conducted on the latest smartphones demonstrated that our system effectively reduces the overall power consumption of mobile devices by 35 percent while simultaneously maintaining satisfactory display quality.
Manifold Mixup : Learning Better Representations by Interpolating Hidden States

Deep networks often perform well on the data distribution on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift. Ideally, a model would assign lower confidence to points unlike those from the training distribution. We propose a regularizer which addresses this issue by training with interpolated hidden states and encouraging the classifier to be less confident at these points. Because the hidden states are learned, this has an important effect of encouraging the hidden states for a class to be concentrated in such a way so that interpolations within the same class or between two different classes do not intersect with the real data points from other classes. This has a major advantage in that it avoids the underfitting which can result from interpolating in the input space. We prove that the exact condition for this problem of underfitting to be avoided by Manifold Mixup is that the dimensionality of the hidden states exceeds the number of classes, which is often the case in practice. Additionally, this concentration can be seen as making the features in earlier layers more discriminative. We show that despite requiring no significant additional computation, Manifold Mixup achieves large improvements over strong baselines in supervised learning, robustness to single-step adversarial attacks, semi-supervised learning, and Negative Log-Likelihood on held out samples.  Equal contribution. †Work done while author was visiting Montreal Institute for Learning Algorithms. Code available at https://github.com/vikasverma1077/manifold_mixup ar X iv :1 80 6. 05 23 6v 3 [ st at .M L ] 4 O ct 2 01 8
Building a Conversational Agent Overnight with Dialogue Self-Play

We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap endto-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, but it is also customizable to cater to task-specific interactions. Compared to the Wizard-of-Oz approach for data collection, M2M achieves greater diversity and coverage of salient dialogue flows while maintaining the naturalness of individual utterances. In the first phase, a simulated user bot and a domain-agnostic system bot converse to exhaustively generate dialogue “outlines”, i.e. sequences of template utterances and their semantic parses. In the second phase, crowd workers provide contextual rewrites of the dialogues to make the utterances more natural while preserving their meaning. The entire process can finish within a few hours. We propose a new corpus of 3,000 dialogues spanning 2 domains collected with M2M, and present comparisons with popular dialogue datasets on the quality and diversity of the surface forms and dialogue flows.
A Meta-Analysis of the Effects of Calculators on Students ’ Achievement and Attitude Levels in Precollege Mathematics Classes

The findings of 54 research studies were integrated through meta-analysis to determine the effects of calculators on student achievement and attitude levels. Effect sizes were generated through Glassian techniques of meta-analysis, and Hedges and Olkin’s (1985) inferential statistical methods were used to test the significance of effect size data. Results revealed that students’ operational skills and problem-solving skills improved when calculators were an integral part of testing and instruction. The results for both skill types were mixed when calculators were not part of assessment, but in all cases, calculator use did not hinder the development of mathematical skills. Students using calculators had better attitudes toward mathematics than their noncalculator counterparts. Further research is needed in the retention of mathematics skills after instruction and transfer of skills to other mathematics-related subjects.
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling

We study parameter inference in large-scale latent variable models. We first propose a unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed frequentist or Bayesian methods. We then propose a novel inference method for the frequentist estimation of parameters, that adapts MCMC methods to online inference of latent variable models with the proper use of local Gibbs sampling. Then, for latent Dirichlet allocation,we provide an extensive set of experiments and comparisons with existing work, where our new approach outperforms all previously proposed methods. This work is currently under review for JMLR  (submitted on July, 27 2016).
Do barriers to pediatric pain management as perceived by nurses change over time?

For decades, nurses (RNs) have identified barriers to providing the optimal pain management that children deserve; yet no studies were found in the literature that assessed these barriers over time or across multiple pediatric hospitals. The purpose of this study was to reassess barriers that pediatric RNs perceive, and how they describe optimal pain management, 3 years after our initial assessment, collect quantitative data regarding barriers identified through comments during our initial assessment, and describe any changes over time. The Modified Barriers to Optimal Pain Management survey was used to measure barriers in both studies. RNs were invited via e-mail to complete an electronic survey. Descriptive and inferential statistics were used to compare results over time. Four hundred forty-two RNs responded, representing a 38% response rate. RNs continue to describe optimal pain management most often in terms of patient comfort and level of functioning. While small changes were seen for several of the barriers, the most significant barriers continued to involve delays in the availability of medications, insufficient physician medication orders, and insufficient orders and time allowed to pre-medicate patients before procedures. To our knowledge, this is the first study to reassess RNs' perceptions of barriers to pediatric pain management over time. While little change was seen in RNs' descriptions of optimal pain management or in RNs' perceptions of barriers, no single item was rated as more than a moderate barrier to pain management. The implications of these findings are discussed in the context of improvement strategies.
The association of maternal ABO blood group with gestational diabetes mellitus in Japanese pregnant women.

To investigated the association between the ABO blood group and gestational diabetes mellitus (GDM).



A retrospective case-control study was conducted using data from 5424 Japanese pregnancies. GDM screening was performed in the first trimester using a casual blood glucose test and in the second trimester using a 50-g glucose challenge test. If the screening was positive, a 75-g oral glucose tolerance test was performed for a GDM diagnosis, which was defined according to the International Association of Diabetes and Pregnancy Study Groups. Logistic regression was used to obtain the odds ratio (OR) and 95% confidence interval (CI) adjusted for traditional risk factors.



Women with the A blood group (adjusted OR: 0.34, 95% CI: 0.19-0.63), B (adjusted OR: 0.35, 95% CI: 0.18-0.68), or O (adjusted OR: 0.39, 95% CI: 0.21-0.74) were at decreased risk of GDM compared with those with group AB. Women with the AB group were associated with increased risk of GDM as compared with those with A, B, or O (adjusted OR: 2.73, 95% CI: 1.64-4.57).



ABO blood groups are associated with GDM, and group AB was a risk factor for GDM in Japanese population.
Ubiquitous Personal Learning Environment (UPLE)

Web 2.0 technologies opened up new perspectives in learning and teaching activities. Collaboration, communication and sharing between learners contribute to the self-regulated learning, a bottom-up approach. The market for smartphones and tablets are growing rapidly. They are being used more often in everyday life. This allows us to support self-regulated learning in a way that learning resources and applications are accessible any time and at any place. This publication focuses on the Personal Learning Environment (PLE) that was launched at Graz University of Technology in 2010. After a first prototype a complete redesign was carried out to fulfill a change towards learner-centered framework. Statistical data show a high increase of attractiveness of the whole system in general. As the next step a mobile version is integrated. A converter for browser-based learning apps within PLE to native smartphone apps leads to the Ubiquitous PLE, which is discussed in this paper in detail.
Toxicity of Moringa oleifera seed extract on some hematological and biochemical profiles in a freshwater fish, Cyprinus carpio.

The study was carried out to investigate the acute and sublethal toxicity of Moringa oleifera seed extract on hematological and biochemical variables of a freshwater fish Cyprinus carpio under laboratory conditions. The 96 h LC50 value of M. oleifera seed extract to the fish C. carpio was estimated by probit analysis method and was found to be 124.0 mg/L (with 95% confidence limits). For sublethal studies a non lethal dose of 1/10th of 96 h LC50 value (12.40 mg/L) was taken. During acute treatment (96 h), hematological variables like red blood cell count (RBC), hemoglobin (Hb), hematocrit (Hct), and mean corpuscular hemoglobin concentration (MCHC) were significantly (P<0.05) decreased in fish exposed to seed extract. However a significant (P<0.05) increase in white blood cell count (WBC), mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) value was observed in the exposed fish during above treatment period when compared to that of the control groups. Biochemical parameters such as plasma protein and glucose levels significantly declined in fish exposed to seed extract while a significant (P<0.05) increase in aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) activity was observed. During sublethal treatment (12.40 mg/L), WBC count, MCV, MCH, plasma glucose, AST, ALT and ALP activities were gradually elevated (P<0.05) at the end of 7, 14, 21, 28 and 35th days in seed extract exposed fish, whereas plasma protein level declined. However, a biphasic trend was noticed in Hb, Hct, RBC and MCHC levels. This study may provide baseline information about the toxicity of M. oleifera seed extract to C. carpio and to establish safer limit in water purification.
Self-Report Dietary Assessment Tools Used in Canadian Research: A Scoping Review.

Choosing the most appropriate dietary assessment tool for a study can be a challenge. Through a scoping review, we characterized self-report tools used to assess diet in Canada to identify patterns in tool use and to inform strategies to strengthen nutrition research. The research databases Medline, PubMed, PsycINFO, and CINAHL were used to identify Canadian studies published from 2009 to 2014 that included a self-report assessment of dietary intake. The search elicited 2358 records that were screened to identify those that reported on self-report dietary intake among nonclinical, non-Aboriginal adult populations. A pool of 189 articles (reflecting 92 studies) was examined in-depth to assess the dietary assessment tools used. Food-frequency questionnaires (FFQs) and screeners were used in 64% of studies, whereas food records and 24-h recalls were used in 18% and 14% of studies, respectively. Three studies (3%) used a single question to assess diet, and for 3 studies the tool used was not clear. A variety of distinct FFQs and screeners, including those developed and/or adapted for use in Canada and those developed elsewhere, were used. Some tools were reported to have been evaluated previously in terms of validity or reliability, but details of psychometric testing were often lacking. Energy and fat were the most commonly studied, reported by 42% and 39% of studies, respectively. For ∼20% of studies, dietary data were used to assess dietary quality or patterns, whereas close to half assessed ≤5 dietary components. A variety of dietary assessment tools are used in Canadian research. Strategies to improve the application of current evidence on best practices in dietary assessment have the potential to support a stronger and more cohesive literature on diet and health. Such strategies could benefit from national and global collaboration.
Scalable Active Learning by Approximated Error Reduction

We study the problem of active learning for multi-class classification on large-scale datasets. In this setting, the existing active learning approaches built upon uncertainty measures are ineffective for discovering unknown regions, and those based on expected error reduction are inefficient owing to their huge time costs. To overcome the above issues, this paper proposes a novel query selection criterion called approximated error reduction (AER). In AER, the error reduction of each candidate is estimated based on an expected impact over all datapoints and an approximated ratio between the error reduction and the impact over its nearby datapoints. In particular, we utilize hierarchical anchor graphs to construct the candidate set as well as the nearby datapoint sets of these candidates. The benefit of this strategy is that it enables a hierarchical expansion of candidates with the increase of labels, and allows us to further accelerate the AER estimation. We finally introduce AER into an efficient semi-supervised classifier for scalable active learning. Experiments on publicly available datasets with the sizes varying from thousands to millions demonstrate the effectiveness of our approach.
An Approach for SQL Injection Vulnerability Detection

Our research objective is to facilitate the identification of true input manipulation vulnerabilities via the combination of static analysis, runtime detection, and automatic testing. We propose an approach for SQL injection vulnerability detection, automated by a prototype tool SQLInjectionGen. We performed case studies on two small web applications for the evaluation of our approach compared to static analysis for identifying true SQL injection vulnerabilities. In our case study, SQLInjectionGen had no false positives, but had a small number of false negatives while the static analysis tool had a false positive for every vulnerability that was actually protected by a white or black list.
Soccer Player Characteristics in English Lower-League Development Programmes: The Relationships between Relative Age, Maturation, Anthropometry and Physical Fitness

The relative age effect (RAE) and its relationships with maturation, anthropometry, and physical performance characteristics were examined across a representative sample of English youth soccer development programmes. Birth dates of 1,212 players, chronologically age-grouped (i.e., U9's-U18's), representing 17 professional clubs (i.e., playing in Leagues 1 & 2) were obtained and categorised into relative age quartiles from the start of the selection year (Q1 = Sep-Nov; Q2 = Dec-Feb; Q3 = Mar-May; Q4 = Jun-Aug). Players were measured for somatic maturation and performed a battery of physical tests to determine aerobic fitness (Multi-Stage Fitness Test [MSFT]), Maximal Vertical Jump (MVJ), sprint (10 & 20m), and agility (T-Test) performance capabilities. Odds ratio's (OR) revealed Q1 players were 5.3 times (95% confidence intervals [CI]: 4.08-6.83) more likely to be selected than Q4's, with a particularly strong RAE bias observed in U9 (OR: 5.56) and U13-U16 squads (OR: 5.45-6.13). Multivariate statistical models identified few between quartile differences in anthropometric and fitness characteristics, and confirmed chronological age-group and estimated age at peak height velocity (APHV) as covariates. Assessment of practical significance using magnitude-based inferences demonstrated body size advantages in relatively older players (Q1 vs. Q4) that were very-likely small (Effect Size [ES]: 0.53-0.57), and likely to very-likely moderate (ES: 0.62-0.72) in U12 and U14 squads, respectively. Relatively older U12-U14 players also demonstrated small advantages in 10m (ES: 0.31-0.45) and 20m sprint performance (ES: 0.36-0.46). The data identify a strong RAE bias at the entry-point to English soccer developmental programmes. RAE was also stronger circa-PHV, and relatively older players demonstrated anaerobic performance advantages during the pubescent period. Talent selectors should consider motor function and maturation status assessments to avoid premature and unwarranted drop-out of soccer players within youth development programmes.