nils-herrmann/clean_scirepeval huggingface dataset:
From the lab to the dorm room : metacognitive awareness and use of spaced study

Laboratory studies have demonstrated the long-term memory benefits of studying material in multiple distributed sessions as opposed to one massed session, given an identical amount of overall study time (i.e., the spacing effect). The current study goes beyond the laboratory to investigate whether undergraduates know about the advantage of spaced study, to what extent they use it in their own studying, and what factors might influence its utilization. Results from a web-based survey indicated that participants (n = 285) were aware of the benefits of spaced study and would use a higher level of spacing under ideal compared to realistic circumstances. However, self-reported use of spacing was intermediate, similar to massing and several other study strategies, and ranked well below commonly used strategies such as rereading notes. Several factors were endorsed as important in the decision to distribute study time, including the perceived difficulty of an upcoming exam, the amount of material to learn, how heavily an exam is weighed in the course grade, and the value of the material. Further, level of metacognitive self-regulation and use of elaboration strategies were associated with higher rates of spaced study. Additional research is needed to examine student study habits in a naturalistic setting, and to explore effective ways to encourage behavior change through motivational and teaching techniques.
CapAuth: Identifying and Differentiating User Handprints on Commodity Capacitive Touchscreens

User identification and differentiation have implications in many application domains, including security, personalization, and co-located multiuser systems. In response, dozens of approaches have been developed, from fingerprint and retinal scans, to hand gestures and RFID tags. In this work, we propose CapAuth, a technique that uses existing, low-level touchscreen data, combined with machine learning classifiers, to provide real-time authentication and even identification of users. As a proof-of-concept, we ran our software on an off-the-shelf Nexus 5 smartphone. Our user study demonstrates twenty-participant authentication accuracies of 99.6%. For twenty-user identification, our software achieved 94.0% accuracy and 98.2% on groups of four, simulating family use.
Knowledge Transfer for Reducing Calibration Time in Brain-Computer Interfacing

Reducing calibration time while maintaining good classification accuracy has been one of the most challenging problems in electroencephalography (EEG) -based brain-computer interfaces (BCIs) research during the last years. Most of machine learning approaches that have been attempted to address this issue are based on knowledge transfer between different BCIs users. Assuming that there is a common underlying data generating process, they try to learn a subject-independent classification model from multiple users in order to classify data of future users. In this paper, we propose a novel approach that allows inter-subjects classification of EEG signals without relying on the strong assumptions considered in previous work. It consists of learning a prediction model of a new BCI user through an ensemble of classifiers where base classifiers are trained on data from other users separately and weighted according to the performance of the ensemble on few labeled data of the new user. Evaluation on real EEG data showed that our approach allows achieving good classification accuracy when the size of calibration set is small.
A global map of science based on the ISI subject categories

The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical transaction matrix (citing versus cited) which is much more densely populated than the underlying matrix at the journal level. Exploratory factor analysis leads us to opt for a fourteen-factor solution. This solution can easily be interpreted as the disciplinary structure of science. The nested maps of science (corresponding to 14 factors, 172 categories, and 6,164 journals) are brought online at http://www.leydesdorff.net/map06/index.htm. An analysis of interdisciplinary relations is pursued at three levels of aggregation using the newly added ISI subject category of “Nanoscience & nanotechnology.” The journal level provides the finer grained perspective. Errors in the attribution of journals to the ISI subject categories are averaged out so that the factor analysis can reveal the main structures. The mapping of science can, therefore, be comprehensive at the level of ISI subject categories.
Exploring Legal Patent Citations for Patent Valuation

Effective patent valuation is important for patent holders. Forward patent citations, widely used in assessing patent value, have been considered as reflecting knowledge flows, just like paper citations. However, patent citations also carry legal implication, which is important for patent valuation. We argue that patent citations can either be technological citations that indicate knowledge transfer or be legal citations that delimit the legal scope of citing patents. In this paper, we first develop citation-network based methods to infer patent quality measures at either the legal or technological dimension. Then we propose a probabilistic mixture approach to incorporate both the legal and technological dimensions in patent citations, and an iterative learning process that integrates a temporal decay function on legal citations, a probabilistic citation network based algorithm and a prediction model for patent valuation. We learn all the parameters together and use them for patent valuation. We demonstrate the effectiveness of our approach by using patent maintenance status as an indicator of patent value and discuss the insights we learned from this study.
Wavelet turbulence for fluid simulation

We present a novel wavelet method for the simulation of fluids at high spatial resolution. The algorithm enables large- and small-scale detail to be edited separately, allowing high-resolution detail to be added as a post-processing step. Instead of solving the Navier-Stokes equations over a highly refined mesh, we use the wavelet decomposition of a low-resolution simulation to determine the location and energy characteristics of missing high-frequency components. We then synthesize these missing components using a novel incompressible turbulence function, and provide a method to maintain the temporal coherence of the resulting structures. There is no linear system to solve, so the method parallelizes trivially and requires only a few auxiliary arrays. The method guarantees that the new frequencies will not interfere with existing frequencies, allowing animators to set up a low resolution simulation quickly and later add details without changing the overall fluid motion.
Effect of a robot on user perceptions

Social robots are robots that help people as capable partners rather than as tools, are believed to be of greatest use for applications in entertainment, education, and healthcare because of their potential to be perceived as trusting, helpful, reliable, and engaging. This paper explores how the robot's physical presence influences a person's perception of these characteristics. The first study reported here demonstrates the differences between a robot and an animated character in terms a person's engagement and perceptions of the robot and character. The second study shows that this difference is a result of the physical presence of the robot and that a person's reactions would be similar even if the robot is not physically collocated. Implications to the design of socially communicative and interactive robots are discussed.
Lead-Free Piezoelectric Ceramics vs. PZT?

Investigations in the development of lead-free piezoelectric ceramics have recently claimed properties comparable to that of PZT-based materials. In this work, the dielectric and piezoelectric properties of the various systems were contrasted in relation to their respective Curie temperatures. Though comparable with respect to TC, enhanced properties reported in the K,NaNbO3 family are the result of increased polarizability associated with the Torthor-tetragonal polymorphic phase transition being compositionally shifted downward and not from a morphotropic phase boundary (MPB) as widely reported. As expected, the properties are strongly temperature dependent unlike that observed for MPB systems. Analogous to PZT, enhanced properties are noted for MPB compositions in the Na,BiTiO3-BaTiO3 and the ternary system with K,BiTiO3, but offer properties significantly lower than that of PZTs. The consequence of a ferroelectric to antiferroelectric transition well below TC further limits their usefulness.
An Extended Model of IT Governance: A Conceptual Proposal

Although a significant amount of work has be done on the subject of IT governance there still appears to be some disjoint and confusion about what IT governance really is and how it may be realized in practice. This research-in-progress paper draws on existing research related to IT governance to provide a more in-depth understanding of the concept. It describes the current understanding of IT governance and argues for the extension and further development of the various dimensions of governance. An extended model of IT governance is proposed. A research agenda is outlined.
A Primary-Side Control Scheme for High-Power-Factor LED Driver With TRIAC Dimming Capability

This paper proposes a primary-side control scheme for low-power light-emitting diode (LED) driver with Flyback topology. With average primary-side current signal and transformer auxiliary winding voltage signal, the output current can be derived and regulated precisely by the proposed control scheme without any isolated feedback circuit. Furthermore, the proposed primary-side control scheme can be implemented with power factor correction control to achieve high input power factor, which is usually required for LED drivers. Since the triode for alternating current (TRIAC) dimming is very popular in practical application, it is preferred that the LED driver can also be compatible with TRIAC dimming. With the proposed control scheme, the input characteristic of the LED driver is resistive like an incandescent bulb. No extra dummy load is needed to provide the TRIAC holding current. Both the output current signal and TRIAC dimming signal can be obtained at the primary side, the TRIAC dimming function can be easily implemented and linear dimming characteristic can be achieved. Detailed operation principles and design considerations of the proposed control scheme are presented in the paper. The experimental results from a 15-W prototype verify the theoretical analysis.
Motion capture from body-mounted cameras

Motion capture technology generally requires that recordings be performed in a laboratory or closed stage setting with controlled lighting. This restriction precludes the capture of motions that require an outdoor setting or the traversal of large areas. In this paper, we present the theory and practice of using body-mounted cameras to reconstruct the motion of a subject. Outward-looking cameras are attached to the limbs of the subject, and the joint angles and root pose are estimated through non-linear optimization. The optimization objective function incorporates terms for image matching error and temporal continuity of motion. Structure-from-motion is used to estimate the skeleton structure and to provide initialization for the non-linear optimization procedure. Global motion is estimated and drift is controlled by matching the captured set of videos to reference imagery. We show results in settings where capture would be difficult or impossible with traditional motion capture systems, including walking outside and swinging on monkey bars. The quality of the motion reconstruction is evaluated by comparing our results against motion capture data produced by a commercially available optical system.
Component-Based Recognition of Facesand Facial Expressions

Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. In this paper, we propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm which benefits from the idea of sparsity and morphological diversity. This entails building data-driven dictionaries for neutral and expressive components. The DCS algorithm then uses these dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition. Experiments on publicly available expression and face data sets show the effectiveness of our method.
A Communication-Assisted Protection Strategy for Inverter-Based Medium-Voltage Microgrids

This paper proposes a communication-assisted protection strategy implementable by commercially available microprocessor-based relays for the protection of medium-voltage microgrids. Even though the developed protection strategy benefits from communications, it offers a backup protection strategy to manage communication network failures. The paper also introduces the structure of a relay that enables the proposed protection strategy. Comprehensive simulation studies are carried out to verify the effectiveness of the proposed protection strategy under different fault scenarios, in the PSCAD/EMTDC software environment.
Annotation of Computer Science Papers for Semantic Relation Extrac-tion

We designed a new annotation scheme for formalising relation structures in research papers, through the investigation of computer science papers. The annotation scheme is based on the hypothesis that identifying the role of entities and events that are described in a paper is useful for intelligent information retrieval in academic literature, and the role can be determined by the relationship between the author and the described entities or events, and relationships among them. Using the scheme, we have annotated research abstracts from the IPSJ Journal published in Japanese by the Information Processing Society of Japan. On the basis of the annotated corpus, we have developed a prototype information extraction system which has the facility to classify sentences according to the relationship between entities mentioned, to help find the role of the entity in which the searcher is interested.
One Big Net For Everything

I apply recent work on"learning to think"(2015) and on PowerPlay (2011) to the incremental training of an increasingly general problem solver, continually learning to solve new tasks without forgetting previous skills. The problem solver is a single recurrent neural network (or similar general purpose computer) called ONE. ONE is unusual in the sense that it is trained in various ways, e.g., by black box optimization / reinforcement learning / artificial evolution as well as supervised / unsupervised learning. For example, ONE may learn through neuroevolution to control a robot through environment-changing actions, and learn through unsupervised gradient descent to predict future inputs and vector-valued reward signals as suggested in 1990. User-given tasks can be defined through extra goal-defining input patterns, also proposed in 1990. Suppose ONE has already learned many skills. Now a copy of ONE can be re-trained to learn a new skill, e.g., through neuroevolution without a teacher. Here it may profit from re-using previously learned subroutines, but it may also forget previous skills. Then ONE is retrained in PowerPlay style (2011) on stored input/output traces of (a) ONE's copy executing the new skill and (b) previous instances of ONE whose skills are still considered worth memorizing. Simultaneously, ONE is retrained on old traces (even those of unsuccessful trials) to become a better predictor, without additional expensive interaction with the enviroment. More and more control and prediction skills are thus collapsed into ONE, like in the chunker-automatizer system of the neural history compressor (1991). This forces ONE to relate partially analogous skills (with shared algorithmic information) to each other, creating common subroutines in form of shared subnetworks of ONE, to greatly speed up subsequent learning of additional, novel but algorithmically related skills.
Sketching piecewise clothoid curves

We present a novel approach to sketching 2D curves with minimally varying curvature as piecewise clothoids. A stable and efficient algorithm fits a sketched piecewise linear curve using a number of clothoid segments with G2 continuity based on a specified error tolerance. Further, adjacent clothoid segments can be locally blended to result in a G3 curve with curvature that predominantly varies linearly with arc length. We also handle intended sharp corners or G1 discontinuities, as independent rotations of clothoid pieces. Our formulation is ideally suited to conceptual design applications where aesthetic fairness of the sketched curve takes precedence over the precise interpolation of geometric constraints. We show the effectiveness of our results within a system for sketch-based road and robot-vehicle path design, where clothoids are already widely used.
Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines the most essential data mining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend. OntoDM-core is available at http://www.ontodm.com .
Online Sequence-to-Sequence Reinforcement Learning for Open-Domain Conversational Agents

We propose an online, end-to-end, deep reinforcement learning technique to develop generative conversational agents for open-domain dialogue. We use a unique combination of offline two-phase supervised learning and online reinforcement learning with human users to train our agent. While most existing research proposes hand-crafted and developer-defined reward functions for reinforcement, we devise a novel reward mechanism based on a variant of Beam Search and one-character user-feedback at each step. Experiments show that our model, when trained on a small and shallow Seq2Seq network, successfully promotes the generation of meaningful, diverse and interesting responses, and can be used to train agents with customized personas and conversational styles.
Exact Tensor Completion Using t-SVD

In this paper, we focus on the problem of completion of multidimensional arrays (also referred to as tensors), in particular three-dimensional (3-D) arrays, from limited sampling. Our approach is based on a recently proposed tensor algebraic framework where 3-D tensors are treated as linear operators over the set of 2-D tensors. In this framework, one can obtain a factorization for 3-D data, referred to as the tensor singular value decomposition (t-SVD), which is similar to the SVD for matrices. t-SVD results in a notion of rank referred to as the tubal-rank. Using this approach we consider the problem of sampling and recovery of 3-D arrays with low tubal-rank. We show that by solving a convex optimization problem, which minimizes a convex surrogate to the tubal-rank, one can guarantee exact recovery with high probability as long as number of samples is of the order <inline-formula><tex-math notation="LaTeX">$O(rnk \log (nk))$ </tex-math></inline-formula>, given a tensor of size <inline-formula><tex-math notation="LaTeX">$n\times n\times k$ </tex-math></inline-formula> with tubal-rank <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> . The conditions under which this result holds are similar to the incoherence conditions for low-rank matrix completion under random sampling. The difference is that we define incoherence under the algebraic setup of t-SVD, which is different from the standard matrix incoherence conditions. We also compare the numerical performance of the proposed algorithm with some state-of-the-art approaches on real-world datasets.
Geometry-Aware Learning of Maps for Camera Localization

Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking. The exact definitions of maps, however, are often application-specific and hand-crafted for different scenarios (e.g. 3D landmarks, lines, planes, bags of visual words). We propose to represent maps as a deep neural net called MapNet, which enables learning a data-driven map representation. Unlike prior work on learning maps, MapNet exploits cheap and ubiquitous sensory inputs like visual odometry and GPS in addition to images and fuses them together for camera localization. Geometric constraints expressed by these inputs, which have traditionally been used in bundle adjustment or pose-graph optimization, are formulated as loss terms in MapNet training and also used during inference. In addition to directly improving localization accuracy, this allows us to update the MapNet (i.e., maps) in a self-supervised manner using additional unlabeled video sequences from the scene. We also propose a novel parameterization for camera rotation which is better suited for deep-learning based camera pose regression. Experimental results on both the indoor 7-Scenes dataset and the outdoor Oxford RobotCar dataset show significant performance improvement over prior work. The MapNet project webpage is .