nils-herrmann/clean_scirepeval huggingface dataset:
Immersive Interactive Virtual Reality and Informal Education

Research in conceptual learning and virtual reality is a relatively young field, but growing rapidly. The increasing development of virtual reality (VR) technologies has matured enough as to expand research from the military and scientific visualization realm into more multidisciplinary areas, such as education, art, and psychology. Consequently, virtual reality interfaces, interaction techniques, and devices have improved greatly in order to provide more natural and obvious modes of interaction and motivational elements. Nevertheless, the prohibitive costs and inaccessibility of VR technologies, coupled with issues of usability, educator training, operation, and maintenance, present important drawbacks for the educational use of VR making it difficult to incorporate in dwindling school budgets. In spite of these concerns and objections regarding the appropriateness and educational efficacy of virtual reality, there remain compelling reasons for believing that VR learning environments for children warrant serious investigation and can provide strong tools for learning. Institutions of informal education, such as museums, research, and cultural centers are in a better position to make use of such advanced systems and investigate their educational potential while effectively shaping how they deliver public education. This paper describes the interactive learning environments developed for learners of all ages in such an institution of informal education, and discusses the issues involved in using state-of-the-art interactive virtual environments for childhood learning.
Simple Learning and Compositional Application of Perceptually Grounded Word Meanings for Incremental Reference Resolution

An elementary way of using language is to refer to objects. Often, these objects are physically present in the shared environment and reference is done via mention of perceivable properties of the objects. This is a type of language use that is modelled well neither by logical semantics nor by distributional semantics, the former focusing on inferential relations between expressed propositions, the latter on similarity relations between words or phrases. We present an account of word and phrase meaning that is perceptually grounded, trainable, compositional, and ‘dialogueplausible’ in that it computes meanings word-by-word. We show that the approach performs well (with an accuracy of 65% on a 1-out-of-32 reference resolution task) on direct descriptions and target/landmark descriptions, even when trained with less than 800 training examples and automatically transcribed utterances.
Consensus group stable feature selection

Stability is an important yet under-addressed issue in feature selection from high-dimensional and small sample data. In this paper, we show that stability of feature selection has a strong dependency on sample size. We propose a novel framework for stable feature selection which first identifies consensus feature groups from subsampling of training samples, and then performs feature selection by treating each consensus feature group as a single entity. Experiments on both synthetic and real-world data sets show that an algorithm developed under this framework is effective at alleviating the problem of small sample size and leads to more stable feature selection results and comparable or better generalization performance than state-of-the-art feature selection algorithms. Synthetic data sets and algorithm source code are available at http://www.cs.binghamton.edu/~lyu/KDD09/.
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP

The transfer or share of knowledge between languages is a popular solution to resource scarcity in NLP. However, the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures. Frameworks such as Universal Dependencies (UD) are designed to be cross-lingually consistent, but even in carefully designed resources trees representing equivalent sentences may not always overlap. In this paper, we measure cross-lingual syntactic variation, or anisomorphism, in the UD treebank collection, considering both morphological and structural properties. We show that reducing the level of anisomorphism yields consistent gains in cross-lingual transfer tasks. We introduce a source language selection procedure that facilitates effective cross-lingual parser transfer, and propose a typologically driven method for syntactic tree processing which reduces anisomorphism. Our results show the effectiveness of this method for both machine translation and cross-lingual sentence similarity, demonstrating the importance of syntactic structure compatibility for boosting cross-lingual transfer in NLP.
When does testing enhance retention? A distribution-based interpretation of retrieval as a memory modifier.

Tests, as learning events, can enhance subsequent recall more than do additional study opportunities, even without feedback. Such advantages of testing tend to appear, however, only at long retention intervals and/or when criterion tests stress recall, rather than recognition, processes. We propose that the interaction of the benefits of testing versus restudying with final-test delay and format reflects not only that successful retrievals are more powerful learning events than are re-presentations but also that the distribution of memory strengths across items is shifted differentially by testing and restudying. The benefits of initial testing over restudying, in this view, should increase as the delay or format of the final test makes that test more difficult. Final-test difficulty, not the similarity of initial-test and final-test conditions, should determine the benefits of testing. In Experiments 1 and 2 we indeed found that initial cued-recall testing enhanced subsequent recall more than did restudying when the final test was a difficult (free-recall) test but not when it was an easier (cued-recall) test that matched the initial test. The results of Experiment 3 supported a new prediction of the distribution framework: namely, that the final cued-recall test that did not show a benefit of testing in Experiment 1 should show such a benefit when that test was made more difficult by introducing retroactive interference. Overall, our results suggest that the differential consequences of initial testing versus restudying reflect, in part, differences in how items distributions are shifted by testing and studying.
Comparison study of 12kV n-type SiC IGBT with 10kV SiC MOSFET and 6.5kV Si IGBT based on 3L-NPC VSC applications

Silicon Carbide (SiC) devices and modules have been developed with high blocking voltages for Medium Voltage power electronics applications. Silicon devices do not exhibit higher blocking voltage capability due to its relatively low band gap energy compared to SiC counterparts. For the first time, 12kV SiC IGBTs have been fabricated. These devices exhibit excellent switching and static characteristics. A Three-level Neutral Point Clamped Voltage Source Converter (3L-NPC VSC) has been simulated with newly developed SiC IGBTs. This 3L-NPC Converter is used as a 7.2kV grid interface for the solid state transformer and STATCOM operation. Also a comparative study is carried out with 3L-NPC VSC simulated with 10kV SiC MOSFET and 6.5kV Silicon IGBT device data.
Socioeconomic Status and Academic Achievement : A Meta-Analytic Review of Research

This meta-analysis reviewed the literature on socioeconomic status (SES) and academic achievement in journal articles published between 1990 and 2000. The sample included 101,157 students, 6,871 schools, and 128 school districts gathered from 74 independent samples. The results showed a medium to strong SES–achievement relation. This relation, however, is moderated by the unit, the source, the range of SES variable, and the type of SES–achievement measure. The relation is also contingent upon school level, minority status, and school location. The author conducted a replica of White’s (1982) meta-analysis to see whether the SES–achievement correlation had changed since White’s initial review was published. The results showed a slight decrease in the average correlation. Practical implications for future research and policy are discussed.
Ranking community answers by modeling question-answer relationships via analogical reasoning

The method of finding high-quality answers has significant impact on user satisfaction in community question answering systems. However, due to the lexical gap between questions and answers as well as spam typically existing in user-generated content, filtering and ranking answers is very challenging. Previous solutions mainly focus on generating redundant features, or finding textual clues using machine learning techniques; none of them ever consider questions and their answers as relational data but instead model them as independent information. Moreover, they only consider the answers of the current question, and ignore any previous knowledge that would be helpful to bridge the lexical and semantic gap. We assume that answers are connected to their questions with various types of latent links, i.e. positive indicating high-quality answers, negative links indicating incorrect answers or user-generated spam, and propose an analogical reasoning-based approach which measures the analogy between the new question-answer linkages and those of relevant knowledge which contains only positive links; the candidate answer which has the most analogous link is assumed to be the best answer. We conducted experiments based on 29.8 million Yahoo!Answer question-answer threads and showed the effectiveness of our approach.
Unsupervised transfer learning for target detection from hyperspectral images

Target detection has been of great interest in hyperspectral image analysis. Feature extraction from target samples and counterpart backgrounds consist the key to the problem. Traditional target detection methods depend on comparatively fixed feature for all the pixels under observation. For example, RX employs the same distance measurement for all the pixels. However, the best separation results usually come from certain targets and backgrounds. Theoretically, they are the purest targets and backgrounds pixels, or the constructive endmembers in the subspace model. So using those most representative pixels’ feature to train a concentrated subspace is expected to enhance the separability between targets and backgrounds. Meanwhile, applying the discriminative information from these training data to the large testing data which are not in the same feature space and with different data distributions is a challenge. Here, the idea of transfer learning from interactive annotation technique in video is employed. Based on the transfer learning frame, several points are taken into consideration and the proposed method is named as an unsupervised transfer learning based target detection (UTLD) method. Firstly, the extreme target and background pixels are generated from robust outlier detection, providing the input for target samples and background samples in transfer learning. Secondly, pixels are calculated from
An Acceleration-Based Robust Motion Controller Design for a Novel Series Elastic Actuator

This paper proposes an acceleration-based robust controller for the motion control problem, i.e., position and force control problems, of a novel series elastic actuator (SEA). A variable stiffness SEA is designed by using soft and hard springs in series so as to relax the fundamental performance limitation of conventional SEAs. Although the proposed SEA intrinsically has several superiorities in force control, its motion control problem, especially position control problem, is harder than conventional stiff and SEAs due to its special mechanical structure. It is shown that the performance of the novel SEA is limited when conventional motion control methods are used. The performance of the steady-state response is significantly improved by using disturbance observer (DOb), i.e., improving the robustness; however, it degrades the transient response by increasing the vibration at tip point. The vibration of the novel SEA and external disturbances are suppressed by using resonance ratio control (RRC) and arm DOb, respectively. The proposed method can be used in the motion control problem of conventional SEAs as well. The intrinsically safe mechanical structure and high-performance motion control system provide several benefits in industrial applications, e.g., robots can perform dexterous and versatile industrial tasks alongside people in a factory setting. The experimental results show viability of the proposals.
Reliability challenges for electric vehicles: From devices to architecture and systems software

Today, modern high-end cars have close to 100 electronic control units (ECUs) that are used to implement a variety of applications ranging from safety-critical control to driver assistance and comfort-related functionalities. The total sum of these applications is several million lines of software code. The ECUs are connected to different sensors and actuators and communicate via a variety of communication buses like CAN, FlexRay and now also Ethernet. In the case of electric vehicles, both the amount and the importance of such electronics and software are even higher. Here, a number of hydraulic or pneumatic controls are replaced by corresponding software-implemented controllers in order to reduce the overall weight of the car and hence to improve its driving range. Until recently, most of the software and system design in the automotive domain -- as in many other domains -- relied on an always correctly functioning or a zero-defect hardware implementation platform. However, as the device geometries of integrated circuits continue to shrink, this assumption is increasingly not true. Incorporating large safety margins in the design process results in very pessimistic design and expensive processors. Further, the processors in cars -- in contrast to those in many consumer electronics devices like mobile phones -- are exposed to harsh environments, extreme temperature variations, and often, strong electromagnetic fields. Hence, their reliability is even more questionable and must be explicitly accounted for in all layers of design abstraction -- starting from circuit design to architecture design, to software design and runtime management and monitoring. In this paper we outline some of these issues, currently followed practices, and the challenges that lie ahead of us in the automotive and electric vehicles domain.
Fuzzy sets and probability : Misunderstandings , bridges and gaps

One of the most controversial issues in uncertainty modelling and information sciences is the relationship between probability theory and fuzzy sets. This paper is meant to survey the literature pertaining to this debate, and to try to overcome misunderstandings and to supply access to many basic references that have addressed the "probability versus fuzzy set" challenge. This problem has not a single facet, as will be claimed here. Moreover it seems that a lot of controversies might have been avoided if protagonists had been patient enough to build a common language and to share their scientific backgrounds. The main points made here are as follows. i) Fuzzy set theory is a consistent body of mathematical tools. ii) Although fuzzy sets and probability measures are distinct, several bridges relating them have been proposed that should reconcile opposite points of view ; especially possibility theory stands at the cross-roads between fuzzy sets and probability theory. iii) Mathematical objects that behave like fuzzy sets exist in probability theory. It does not mean that fuzziness is reducible to randomness. Indeed iv) there are ways of approaching fuzzy sets and possibility theory that owe nothing to probability theory. Interpretations of probability theory are multiple especially frequentist versus subjectivist views (Fine ) ; several interpretations of fuzzy sets also exist. Some interpretations of fuzzy sets are in agreement with probability calculus and some are not. The paper is structured as follows : first we address some classical misunderstandings between fuzzy sets and probabilities. They must be solved before any discussion can take place. Then we consider probabilistic interpretations of membership functions, that may help in membership function assessment. We also point out nonprobabilistic interpretations of fuzzy sets. The next section examines the literature on possibility-probability transformations and tries to clarify some lurking controversies on that topic. In conclusion, we briefly mention several subfields of fuzzy set research where fuzzy sets and probability are conjointly used.
On reverse-engineering the KUKA Robot Language

Most commercial manufacturers of industrial robots require their robots to be programmed in a proprietary language tailored to the domain – a typical domain-specific language (DSL). However, these languages oftentimes suffer from shortcomings such as controller-specific design, limited expressiveness and a lack of extensibility. For that reason, we developed the extensible Robotics API for programming industrial robots on top of a general-purpose language. Although being a very flexible approach to programming industrial robots, a fully-fledged language can be too complex for simple tasks. Additionally, legacy support for code written in the original DSL has to be maintained. For these reasons, we present a lightweight implementation of a typical robotic DSL, the KUKA Robot Language (KRL), on top of our Robotics API. This work deals with the challenges in reverse-engineering the language and mapping its specifics to the Robotics API. We introduce two different approaches of interpreting and executing KRL programs: tree-based and bytecode-based interpretation.
Fall Detection System Based on Kinect Sensor Using Novel Detection and Posture Recognition Algorithm

Elderly suffers from injuries or disabilities through falls every year. With a high likelihood of falls causing serious injury or death, falling can be extremely dangerous, especially when the victim is home-alone and is unable to seek timely medical assistance. Our fall detection systems aims to solve this problem by automatically detecting falls and notify healthcare services or the victim’s caregivers so as to provide help. In this paper, development of a fall detection system based on Kinect sensor is introduced. Current fall detection algorithms were surveyed and we developed a novel posture recognition algorithm to improve the specificity of the system. Data obtained through trial testing with human subjects showed a 26.5% increase in fall detection compared to control algorithms. With our novel detection algorithm, the system conducted in a simulated ward scenario can achieve up to 90% fall detection rate.
Buck-Boost PWM Converters Having Two Independently Controlled Switches-LQJTXDQ & KHQ ' UDJDQ 0 DNVLPRYLü DQG 5 REHUW ( ULFNVRQ

1 This work is supported by Philips Research, Briarcliff Manor, NY, through Colorado Power Electronics Center Abstract – Single-switch step-up/step-down converters, such as the buck-boost, SEPIC and Cuk, have relatively high voltage and current stresses on components compared to the buck or the boost converter. A buck-boost converter with two independently controlled switches can work as a boost or as a buck converter depending on input-output conditions, and thus achieves lower stresses on components. Using the converter synthesis method from [1], families of two-switch buck-boost converters are generated, including several new converter topologies. The two-switch buck-boost converters are evaluated and compared in terms of component stresses in universal-input power-factor-corrector applications. Among them, one new two-switch converter is identified that has low inductor conduction losses (50% of the boost converter), low inductor volt-seconds (72% of the boost converter), and about the same switch conduction losses and voltage stresses as the boost converter.
Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement

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Altering the Intestinal Microbiota during a Critical Developmental Window Has Lasting Metabolic Consequences

Acquisition of the intestinal microbiota begins at birth, and a stable microbial community develops from a succession of key organisms. Disruption of the microbiota during maturation by low-dose antibiotic exposure can alter host metabolism and adiposity. We now show that low-dose penicillin (LDP), delivered from birth, induces metabolic alterations and affects ileal expression of genes involved in immunity. LDP that is limited to early life transiently perturbs the microbiota, which is sufficient to induce sustained effects on body composition, indicating that microbiota interactions in infancy may be critical determinants of long-term host metabolic effects. In addition, LDP enhances the effect of high-fat diet induced obesity. The growth promotion phenotype is transferrable to germ-free hosts by LDP-selected microbiota, showing that the altered microbiota, not antibiotics per se, play a causal role. These studies characterize important variables in early-life microbe-host metabolic interaction and identify several taxa consistently linked with metabolic alterations.
A High Dynamic-Range Neural Recording Chopper Amplifier for Simultaneous Neural Recording and Stimulation

Closed-loop neuromodulation is essential for the advance of neuroscience and for administering therapy in patients suffering from drug-resistant neurological conditions. Neural stimulation generates large artifacts at the recording sites, which easily saturate traditional recording front ends. This paper presents a neural recording chopper amplifier capable of handling in-band artifacts up to 40 mV<sub>pp</sub> while preserving the accompanying small neural signals. New techniques have been proposed that solve the issues of low input impedance and electrode-offset rejection, which enable a DC input impedance of 300 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> and a dynamic range of 69 dB (200 Hz–5 kHz) and 78 dB (1–200 Hz). Implemented in a 40-nm CMOS process, the prototype occupies an area of 0.071 mm<sup>2</sup>/channel, and consumes <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{W}$ </tex-math></inline-formula> from a 1.2 V supply. The input-referred noise is <inline-formula> <tex-math notation="LaTeX">$7~\mu \text{V}_{\mathrm {rms}}$ </tex-math></inline-formula> (200 Hz–20 kHz) and <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{V}_{\mathrm {rms}}$ </tex-math></inline-formula> (1–200 Hz). The total harmonic distortion for a 20-mV<sub>p</sub> input at 1 kHz is −74 dB. This paper improves the linearity by 14–26 dB, dynamic range by 11–28 dB, and input-impedance for chopped front ends by a factor of 11 as compared with the current state of the art, while achieving similar power and noise performance.