nils-herrmann/clean_scirepeval huggingface dataset:
Semi-supervised Question Retrieval with Gated Convolutions

Question answering forums are rapidly growing in size with no effective automated ability to refer to and reuse answers already available for previous posted questions. In this paper, we develop a methodology for finding semantically related questions. The task is difficult since 1) key pieces of information are often buried in extraneous details in the question body and 2) available annotations on similar questions are scarce and fragmented. We design a recurrent and convolutional model (gated convolution) to effectively map questions to their semantic representations. The models are pre-trained within an encoder-decoder framework (from body to title) on the basis of the entire raw corpus, and fine-tuned discriminatively from limited annotations. Our evaluation demonstrates that our model yields substantial gains over a standard IR baseline and various neural network architectures (including CNNs, LSTMs and GRUs).1
The Unreasonable Effectiveness of Word Representations for Twitter Named Entity Recognition

Named entity recognition (NER) systems trained on newswire perform very badly when tested on Twitter. Signals that were reliable in copy-edited text disappear almost entirely in Twitter’s informal chatter, requiring the construction of specialized models. Using wellunderstood techniques, we set out to improve Twitter NER performance when given a small set of annotated training tweets. To leverage unlabeled tweets, we build Brown clusters and word vectors, enabling generalizations across distributionally similar words. To leverage annotated newswire data, we employ an importance weighting scheme. Taken all together, we establish a new state-of-the-art on two common test sets. Though it is wellknown that word representations are useful for NER, supporting experiments have thus far focused on newswire data. We emphasize the effectiveness of representations on Twitter NER, and demonstrate that their inclusion can improve performance by up to 20 F1.
A Four-Terminal, Inline, Chalcogenide Phase-Change RF Switch Using an Independent Resistive Heater for Thermal Actuation

An inline chalcogenide phase-change radio-frequency (RF) switch using germanium telluride and driven by an integrated, electrically isolated thin-film heater for thermal actuation has been fabricated. A voltage pulse applied to the heater terminals was used to transition the phase-change material between the crystalline and amorphous states. An ON-state resistance of 4.5 Ω (0.08 Ω-mm) with an OFF-state capacitance and resistance of 35 fF and 0.5 MΩ, respectively, were measured resulting in an RF switch cutoff frequency (Fco) of 1.0 THz and an OFF/ON resistance ratio of 105. The output third-order intercept point measured , with zero power consumption during steady-state operation, making it a nonvolatile RF switch. To the best of our knowledge, this is the first reported implementation of an RF phase change switch in a four-terminal, inline configuration.
Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning

We study to what extend Chinese, Japanese and Korean faces can be classified and which facial attributes offer the most important cues. First, we propose a novel way of ob- taining large numbers of facial images with nationality la- bels. Then we train state-of-the-art neural networks with these labeled images. We are able to achieve an accuracy of 75.03% in the classification task, with chances being 33.33% and human accuracy 49% . Further, we train mul- tiple facial attribute classifiers to identify the most distinc- tive features for each group. We find that Chinese, Japanese and Koreans do exhibit substantial differences in certain at- tributes, such as bangs, smiling, and bushy eyebrows. Along the way, we uncover several gender-related cross-country patterns as well. Our work, which complements existing APIs such as Microsoft Cognitive Services and Face++, could find potential applications in tourism, e-commerce, social media marketing, criminal justice and even counter- terrorism.
Underwater Image Enhancement by Wavelength Compensation and Dehazing

Light scattering and color change are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. No existing underwater processing techniques can handle light scattering and color change distortions suffered by underwater images, and the possible presence of artificial lighting simultaneously. This paper proposes a novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration. Once the depth map, i.e., distances between the objects and the camera, is estimated, the foreground and background within a scene are segmented. The light intensities of foreground and background are compared to determine whether an artificial light source is employed during the image capturing process. After compensating the effect of artifical light, the haze phenomenon and discrepancy in wavelength attenuation along the underwater propagation path to camera are corrected. Next, the water depth in the image scene is estimated according to the residual energy ratios of different color channels existing in the background light. Based on the amount of attenuation corresponding to each light wavelength, color change compensation is conducted to restore color balance. The performance of the proposed algorithm for wavelength compensation and image dehazing (WCID) is evaluated both objectively and subjectively by utilizing ground-truth color patches and video downloaded from the Youtube website. Both results demonstrate that images with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.
Emotion recognition by speech signals

For emotion recognition, we selected pitch, log energy, formant, mel-band energies, and mel frequency cepstral coefficients (MFCCs) as the base features, and added velocity/acceleration of pitch and MFCCs to form feature streams. We extracted statistics used for discriminative classifiers, assuming that each stream is a one-dimensional signal. Extracted features were analyzed by using quadratic discriminant analysis (QDA) and support vector machine (SVM). Experimental results showed that pitch and energy were the most important factors. Using two different kinds of databases, we compared emotion recognition performance of various classifiers: SVM, linear discriminant analysis (LDA), QDA and hidden Markov model (HMM). With the text-independent SUSAS database, we achieved the best accuracy of 96.3% for stressed/neutral style classification and 70.1% for 4-class speaking style classification using Gaussian SVM, which is superior to the previous results. With the speaker-independent AIBO database, we achieved 42.3% accuracy for 5-class emotion recognition.
Crowdsourcing Multiple Choice Science Questions

Science Study Book Corpus Document Filter [...] enters a d orbital. The valence electrons (those added after the last noble gas configuration) in these elements include the ns and (n \u2013 1) d electrons. The official IUPAC definition of transition elements specifies those with partially filled d orbitals. Thus, the elements with completely filled orbitals (Zn, Cd, Hg, as well as Cu, Ag, and Au in Figure 6.30) are not technically transition elements. However, the term is frequently used to refer to the entire d block (colored yellow in Figure 6.30), and we will adopt this usage in this textbook. Inner transition elements are metallic elements in which the last electron added occupies an f orbital.
Database of iris printouts and its application: Development of liveness detection method for iris recognition

Liveness detection (often referred to as presentation attack detection) is the ability to detect artificial objects presented to a biometric device with an intention to subvert the recognition system. This paper presents the database of iris printout images with a controlled quality, and its fundamental application, namely development of liveness detection method for iris recognition. The database gathers images of only those printouts that were accepted by an example commercial camera, i.e. the iris template calculated for an artefact was matched to the corresponding iris reference of the living eye. This means that the quality of the employed imitations is not accidental and precisely controlled. The database consists of 729 printout images for 243 different eyes, and 1274 images of the authentic eyes, corresponding to imitations. It may thus serve as a good benchmark for at least two challenges: a) assessment of the liveness detection algorithms, and b) assessment of the eagerness of matching real and fake samples by iris recognition methods. To our best knowledge, the iris printout database of such properties is the first worldwide published as of today. In its second part, the paper presents an example application of this database, i.e. the development of liveness detection method based on iris image frequency analysis. We discuss how to select frequency windows and regions of interest to make the method sensitive to “alien frequencies” resulting from the printing process. The proposed method shows a very promising results, since it may be configured to achieve no false alarms when the rate of accepting the iris printouts is approximately 5% (i.e. 95% of presentation attack trials are correctly identified). This favorable compares to the results of commercial equipment used in the database development, as this device accepted all the printouts used. The method employs the same image as used in iris recognition process, hence no investments into the capture devices is required, and may be applied also to other carriers for printed iris patterns, e.g. contact lens.
Efficient word alignment with Markov Chain Monte Carlo

We present efmaral, a new system for efficient and accurate word alignment using a Bayesian model with Markov Chain Monte Carlo (MCMC) inference. Through careful selection of data structures and model architecture we are able to surpass the fast_align system, commonly used for performance-critical word alignment, both in computational efficiency and alignment accuracy. Our evaluation shows that a phrase-based statistical machine translation (SMT) system produces translations of higher quality when using word alignments from efmaral than from fast_align, and that translation quality is on par with what is obtained using giza++, a tool requiring orders of magnitude more processing time. More generally we hope to convince the reader that Monte Carlo sampling, rather than being viewed as a slow method of last resort, should actually be the method of choice for the SMT practitioner and others interested in word alignment.
Incorporating demand uncertainty and forecast error in supply chain planning models

This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.  (2011) 62, 483–500. doi:10.1057/jors.2010.40 Published online 16 June 2010
Autonomous, Vision-based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle

The use of mobile robots in search-and-rescue and disaster-response missions has increased significantly in recent years. However, they are still remotely controlled by expert professionals on an actuator set-point level, and they would benefit, therefore, from any bit of autonomy added. This would allow them to execute highlevel commands, such as “execute this trajectory” or “map this area.” In this paper, we describe a vision-based quadrotor micro aerial vehicle that can autonomously execute a given trajectory and provide a live, dense three-dimensional (3D) map of an area. This map is presented to the operator while the quadrotor is mapping, so that there are no unnecessary delays in the mission. Our system does not rely on any external positioning system (e.g., GPS or motion capture systems) as sensing, computation, and control are performed fully onboard a smartphone processor. Since we use standard, off-the-shelf components from the hobbyist and smartphone markets, the total cost of our system is very low. Due to its low weight (below 450 g), it is also passively safe and can be deployed close to humans. We describe both the hardware and the software architecture of our system. We detail our visual odometry pipeline, the state estimation and control, and our live dense 3D mapping, with an overview of how all the modules work and how they have been integrated into the final system. We report the results of our experiments both indoors and outdoors. Our quadrotor was demonstrated over 100 times at multiple trade fairs, at public events, and to rescue professionals. We discuss the practical challenges and lessons learned. Code, datasets, and videos are publicly available to the robotics community.
Classical fear conditioning in functional neuroimaging

Classical conditioning, the simplest form of associative learning, is one of the most studied paradigms in behavioural psychology. Since the formal description of classical conditioning by Pavlov, lesion studies in animals have identified a number of anatomical structures involved in, and necessary for, classical conditioning. In the 1980s, with the advent of functional brain imaging techniques, particularly positron emission tomography (PET), it has been possible to study the functional anatomy of classical conditioning in humans. The development of functional magnetic resonance imaging (fMRI)--in particular single-trial or event-related fMRI--has now considerably advanced the potential of neuroimaging for the study of this form of learning. Recent event-related fMRI and PET studies are adding crucial data to the current discussion about the putative role of the amygdala in classical fear conditioning in humans.
A Global/Local Affinity Graph for Image Segmentation

Construction of a reliable graph capturing perceptual grouping cues of an image is fundamental for graph-cut based image segmentation methods. In this paper, we propose a novel sparse global/local affinity graph over superpixels of an input image to capture both short- and long-range grouping cues, and thereby enabling perceptual grouping laws, including proximity, similarity, continuity, and to enter in action through a suitable graph-cut algorithm. Moreover, we also evaluate three major visual features, namely, color, texture, and shape, for their effectiveness in perceptual segmentation and propose a simple graph fusion scheme to implement some recent findings from psychophysics, which suggest combining these visual features with different emphases for perceptual grouping. In particular, an input image is first oversegmented into superpixels at different scales. We postulate a gravitation law based on empirical observations and divide superpixels adaptively into small-, medium-, and large-sized sets. Global grouping is achieved using medium-sized superpixels through a sparse representation of superpixels' features by solving a ℓ0-minimization problem, and thereby enabling continuity or propagation of local smoothness over long-range connections. Small- and large-sized superpixels are then used to achieve local smoothness through an adjacent graph in a given feature space, and thus implementing perceptual laws, for example, similarity and proximity. Finally, a bipartite graph is also introduced to enable propagation of grouping cues between superpixels of different scales. Extensive experiments are carried out on the Berkeley segmentation database in comparison with several state-of-the-art graph constructions. The results show the effectiveness of the proposed approach, which outperforms state-of-the-art graphs using four different objective criteria, namely, the probabilistic rand index, the variation of information, the global consistency error, and the boundary displacement error.
A Preliminary Evaluation of Machine Learning in Algorithm Selection for Search Problems

Machine learning is an established method of selecting algorithms to solve hard search problems. Despite this, to date no systematic comparison and evaluation of the different techniques has been performed and the performance of existing systems has not been critically compared to other approaches. We compare machine learning techniques for algorithm selection on real-world data sets of hard search problems. In addition to well-established approaches, for the first time we also apply statistical relational learning to this problem. We demonstrate that most machine learning techniques and existing systems perform less well than one might expect. To guide practitioners, we close by giving clear recommendations as to which machine learning techniques are likely to perform well based on our experiments.
Multi-objective Cross-Project Defect Prediction

Cross-project defect prediction is very appealing because (i) it allows predicting defects in projects for which the availability of data is limited, and (ii) it allows producing generalizable prediction models. However, existing research suggests that cross-project prediction is particularly challenging and, due to heterogeneity of projects, prediction accuracy is not always very good. This paper proposes a novel, multi-objective approach for cross-project defect prediction, based on a multi-objective logistic regression model built using a genetic algorithm. Instead of providing the software engineer with a single predictive model, the multi-objective approach allows software engineers to choose predictors achieving a compromise between number of likely defect-prone artifacts (effectiveness) and LOC to be analyzed/tested (which can be considered as a proxy of the cost of code inspection). Results of an empirical evaluation on 10 datasets from the Promise repository indicate the superiority and the usefulness of the multi-objective approach with respect to single-objective predictors. Also, the proposed approach outperforms an alternative approach for cross-project prediction, based on local prediction upon clusters of similar classes.
Online banking: a field study of drivers, development challenges, and expectations

Online banking is the newest and least understood delivery channel for retail banking services. Yet, few, if any, studies were reported quantifying the issues relevant to this cutting-edge technology. This paper reports the results of a quantitative study of the perceptions of banks’ executive and IT managers and potential customers with regard to the drivers, development challenges, and expectations of online banking. The findings will be useful for both researchers and practitioners who seek to understand the issues relevant to online banking.
A passive exoskeleton with artificial tendons: Design and experimental evaluation

We developed a passive exoskeleton that was designed to minimize joint work during walking. The exoskeleton makes use of passive structures, called artificial tendons, acting in parallel with the leg. Artificial tendons are elastic elements that are able to store and redistribute energy over the human leg joints. The elastic characteristics of the tendons have been optimized to minimize the mechanical work of the human leg joints. In simulation the maximal reduction was 40 percent. The performance of the exoskeleton was evaluated in an experiment in which nine subjects participated. Energy expenditure and muscle activation were measured during three conditions: Normal walking, walking with the exoskeleton without artificial tendons, and walking with the exoskeleton with the artificial tendons. Normal walking was the most energy efficient. While walking with the exoskeleton, the artificial tendons only resulted in a negligibly small decrease in energy expenditure.
Integrated magnetic design of small planar transformers for LLC resonant converters

Integrating magnetic parasitics in the design of LCC resonant converters provides a solution to reduce parts count and increase power density. This paper provides an efficient design procedure for small planar transformers which integrate transformer leakage inductance and magnetizing inductance into the resonant tank by employing an accurate parasitic prediction model. Finite element simulations were used to create the models using Design of Experiment (DoE) methodology. A planar transformer prototype was designed and tested within a 2.5W LLC resonant converter and results under different operating modes are included to illustrate the resonant behaviour and to validate the presented design procedure.