nils-herrmann/clean_scirepeval huggingface dataset:
Creative motivation: Creative achievement predicts cardiac autonomic markers of effort during divergent thinking

Executive approaches to creativity emphasize that generating creative ideas can be hard and requires mental effort. Few studies, however, have examined effort-related physiological activity during creativity tasks. Using motivational intensity theory as a framework, we examined predictors of effort-related cardiac activity during a creative challenge. A sample of 111 adults completed a divergent thinking task. Sympathetic (PEP and RZ) and parasympathetic (RSA and RMSSD) outcomes were assessed using impedance cardiography. As predicted, people with high creative achievement (measured with the Creative Achievement Questionnaire) showed significantly greater increases in sympathetic activity from baseline to task, reflecting higher effort. People with more creative achievements generated ideas that were significantly more creative, and creative performance correlated marginally with PEP and RZ. The results support the view that creative thought can be a mental challenge.
Automatic Poetry Generation with Mutual Reinforcement Learning

Poetry is one of the most beautiful forms of human language art. As a crucial step towards computer creativity, automatic poetry generation has drawn researchers’ attention for decades. In recent years, some neural models have made remarkable progress in this task. However, they are all based on maximum likelihood estimation, which only learns common patterns of the corpus and results in lossevaluation mismatch. Human experts evaluate poetry in terms of some specific criteria, instead of word-level likelihood. To handle this problem, we directly model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning, so as to motivate the model to pursue higher scores. Besides, inspired by writing theories, we propose a novel mutual reinforcement learning schema. We simultaneously train two learners (generators) which learn not only from the teacher (rewarder) but also from each other to further improve performance. We experiment on Chinese poetry. Based on a strong basic model, our method achieves better results and outperforms the current state-of-theart method.
BourGAN: Generative Networks with Metric Embeddings

This paper addresses the mode collapse for generative adversarial networks (GANs). We view modes as a geometric structure of data distribution in a metric space. Under this geometric lens, we embed subsamples of the dataset from an arbitrary metric space into the `2 space, while preserving their pairwise distance distribution. Not only does this metric embedding determine the dimensionality of the latent space automatically, it also enables us to construct a mixture of Gaussians to draw latent space random vectors. We use the Gaussian mixture model in tandem with a simple augmentation of the objective function to train GANs. Every major step of our method is supported by theoretical analysis, and our experiments on real and synthetic data confirm that the generator is able to produce samples spreading over most of the modes while avoiding unwanted samples, outperforming several recent GAN variants on a number of metrics and offering new features.
A very compact CPW-to-CPS BALUN for UWB antenna feeding

This paper considers an UWB balun optimized for a frequency band from 6 to 8.5 GHz. The balun provides a transition from unbalanced coplanar waveguide (CPW) to balanced coplanar stripline (CPS), which is suitable for feeding broadband coplanar antennas such as Vivaldi or bow-tie antennas. It is shown, that applying a solid ground plane under the CPS-to-CPS transition enables decreasing its area by a factor of 4.7. Such compact balun can be used for feeding uniplanar antennas, while significantly saving substrate area. Several transition configurations have been fabricated for single and double-layer configurations. They have been verified by comparison with results both from a full-wave electromagnetic (EM) simulation and experimental measurements.
Modeling Localness for Self-Attention Networks

Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies and enhance the ability of capturing shortrange dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese⇒English and English⇒German translation tasks demonstrate the effectiveness and universality of the proposed approach.
Piezoelectric and ferroelectric materials and structures for energy harvesting applications

This review provides a detailed overview of the energy harvesting technologies associated with piezoelectric materials along with the closely related sub-classes of pyroelectrics and ferroelectrics. These properties are, in many cases, present in the same material, providing the intriguing prospect of a material that can harvest energy from multiple sources including vibration, thermal fluctuations and light. Piezoelectric materials are initially discussed in the context of harvesting mechanical energy from vibrations using inertial energy harvesting, which relies on the resistance of a mass to acceleration, and kinematic energy harvesting which directly couples the energy harvester to the relative movement of different parts a source. Issues related to mode of operation, loss mechanisms and using non-linearity to enhance the operating frequency range are described along with the potential materials that could be employed for harvesting vibrations at elevated temperatures. Pyroelectric harvesting generates power from temperature fluctuations and this review covers the modes of pyroelectric harvesting such as simple resistive loading and Olsen cycles. Nano-scale pyroelectric systems and novel micro-electro-mechanical-systems designed to increase the operating frequency are discussed. The use of ferroelectric or multi-ferroic materials to convert light into chemical or electrical energy is then described in applications where the internal electric field can prevent electron-hole recombination or enhance chemical reactions at the ferroelectric surface. Finally, piezoelectric based energy harvesting devices are complex multi-physics systems requiring advanced methodologies to maximise their performance. The research effort to develop optimisation methods for complex piezoelectric energy harvesters is reviewed
ERIK ERIKSON ' S THEORY OF IDENTITY DEVELOPMENT

Erik H. Erikson's (1902-1994) theory reflects in part bis psychoanalytic training, but , it embraces society's influence and the social aspects of development to a much larger exrefit than did Freud's. With little more than a German high school education, Erikson attended art schools and traveled in ltaly, apparently in search of 4is own identity. Erikson's later writing popularized the concept of "identity," and he applied it especially to the period of adolescence. After Erikson returned to Germany, where he studied art and prepared to teach art, he was offered a teaching position in a private school in Vienna that served the children of patients of Sigmund and Anna Freud. Peter Blos, a friend of Erikson from the rime they attended the Gymnasium together, also worked as a teacher in the same school and it was Blos's idea to offer Erikson the position. During bis tenure as a teacher, Erikson was invited to undergo psychoanalysis with Anna Freud, and during this process bis interest expanded from art and teaching to also include the study of psychoanalysis. While in Vienna, he also studied Montessori education, which later influenced bis psychoanalytic studies, such as the organization of abjects in space. Erikson graduated from the Vienna Psychoanalytic lnstitute in 1933 as a lay analyst since he held no medical or academic degrees. Later that year, he immigrated to the United States and became associated with the Harvard Psychological Clinic. Erikson bas published extensively, bis best known and most widely read book being Chitdhood and Society, published in 1950 and revised in 1963. Of particular significance to an understanding of adolescence is bis ldentity: Youth and Crisis (1968). Erikson's more recent book, The Life Cycle Compteted (1982), encompasses an integration of much of bis earlier work, but with the explicit purpose of exploring development by beginning with old age and to make sense of the "completed life cycle." He also explained that the new organization reflects bis view that, because aIl stages grow out of previous stages, tracing the antecedents backward would highlight these relationships. The idea of identity formation bas remained the focus of much of bis work and appears in other book titles, such as ldentity
Multi-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition

This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (<inline-formula> <tex-math notation="LaTeX">$\mathcal {M}$ </tex-math></inline-formula>-FFC), for face recognition. On the assumption that <inline-formula> <tex-math notation="LaTeX">$\mathcal {M}$ </tex-math></inline-formula>-FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by <inline-formula> <tex-math notation="LaTeX">$\mathcal {M}$ </tex-math></inline-formula>-fold to instantiate a filter offspring set. The <inline-formula> <tex-math notation="LaTeX">$\mathcal {M}$ </tex-math></inline-formula>-FFC flexibility also permits cross convolution amongst Gabor filters and other filter banks of profoundly dissimilar traits, e.g., principal component analysis (PCA) filters and independent component analysis (ICA) filters. The 2-FFC of Gabor, PCA, and ICA filters thus yields three offspring sets: 1) Gabor filters solely; 2) Gabor-PCA filters; and 3) Gabor-ICA filters, to render the learning-free and the learning-based 2-FFC descriptors. To facilitate a sensible Gabor filter selection for <inline-formula> <tex-math notation="LaTeX">$\mathcal {M}$ </tex-math></inline-formula>-FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into eight elementary filters. Aside from that, an average histogram pooling operator is employed to leverage the 2-FFC histogram features, prior to the final whitening PCA compression. The empirical results substantiate that the 2-FFC descriptors prevail over, or on par with, other face descriptors on both identification and verification tasks.
Ready for Robotics : Bringing Together the T and E of STEM in Early Childhood Teacher Education

Prior work has shown that early childhood educators demonstrate a lack of knowledge and understanding about technology and engineering, and about developmentally appropriate pedagogical approaches to bring those disciplines into the classrooms. This paper reports a study in which 32 early childhood educators participated in an intensive three-day professional development workshop with the goals of: increasing teachers’ knowledge about robotics, engineering and programming, and pedagogies for teaching them in the early childhood classroom. results show a statistically significant increase in the level of knowledge in all the three areas of technology in general, pedagogy, and robotics content knowledge after participation in the institute. additionally, results show significant increases in several aspects of technology self-efficacy and attitudes toward technology. implications for designing effective technology focused professional development are discussed.
Effect of Hydrogen and Carbamide Peroxide in Bleaching, Enamel Morphology, and Mineral Composition: In vitro Study.

The aim of the study was to evaluate the bleaching effect, morphological changes, and variations in calcium (Ca) and phosphate (P) in the enamel with hydrogen peroxide (HP) and carbamide peroxide (CP) after the use of different application regimens.



Four groups of five teeth were randomly assigned, according to the treatment protocol: HP 37.5% applied for 30 or 60 minutes (HP30, HP60), CP 16% applied for 14 or 28 hours (CP14, CP28). Changes in dental color were evaluated, according to the following formula: ΔE = [(La-Lb)2+(aa-ab)2 + (ba-bb)2]1/2. Enamel morphology and Ca and P compositions were evaluated by confocal laser scanning microscope and environmental scanning electron microscopy.



ΔE HP30 was significantly greater than CP14 (10.37 ± 2.65/8.56 ± 1.40), but not between HP60 and CP28. HP60 shows greater morphological changes than HP30. No morphological changes were observed in the groups treated with CP. The reduction in Ca and P was significantly greater in HP60 than in CP28 (p < 0.05).



Both formulations improved tooth color; HP produced morphological changes and Ca and P a gradual decrease, while CP produced no morphological changes, and the decrease in mineral component was smaller.



CP 16% applied during 2 weeks could be equally effective and safer for tooth whitening than to administer two treatment sessions with HP 37.5%.
Depth-First Search and Linear Graph Algorithms

The value of depth-first search or "bacltracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components of an undirect graph are presented. The space and time requirements of both algorithms are bounded by k1V + k2E dk for some constants kl, k2, and ka, where Vis the number of vertices and E is the number of edges of the graph being examined.
Sudoku Solutions Using Logic Equations

In several previous papers and particularly in  we presented the use of logic equations and their solution using ternary vectors and set-theoretic considerations as well as binary codings and bit-parallel vector operations. In this paper we introduce a new and elegant model for the game of Sudoku that uses the same approach and solves this problem without any search always finding all solutions (including no solutions or several solutions). It can also be extended to larger Sudokus and to a whole class of similar discrete problems, such as Queens’ problems on the chessboard, graph-coloring problems etc. Disadvantages of known SAT approaches for such problems were overcome by our new method.
Identifying Tweets with Fake News

The extensive use of social media has tremendous impact on culture, business, and politics on the world at large with potentially positive and negative effects. For example, social media coverage of crisis events may be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news for financial or political benefit. Considering the harmful consequences of fake news in social media, there is a profound need to detect false information, control and/or prevent it from spreading. In this paper, we propose an advanced framework to identify tweets with fake news contents using techniques including statistical analysis of Twitter user account, reverse image searching, cross verification of fake news sources, and data mining. Experimental results on a large miscellaneous events dataset demonstrate the effectiveness of our proposed approach in identifying fake tweets.
Occlusion-Aware Depth Estimation Using Light-Field Cameras

Consumer-level and high-end light-field cameras are now widely available. Recent work has demonstrated practical methods for passive depth estimation from light-field images. However, most previous approaches do not explicitly model occlusions, and therefore cannot capture sharp transitions around object boundaries. A common assumption is that a pixel exhibits photo-consistency when focused to its correct depth, i.e., all viewpoints converge to a single (Lambertian) point in the scene. This assumption does not hold in the presence of occlusions, making most current approaches unreliable precisely where accurate depth information is most important - at depth discontinuities. In this paper, we develop a depth estimation algorithm that treats occlusion explicitly, the method also enables identification of occlusion edges, which may be useful in other applications. We show that, although pixels at occlusions do not preserve photo-consistency in general, they are still consistent in approximately half the viewpoints. Moreover, the line separating the two view regions (correct depth vs. occluder) has the same orientation as the occlusion edge has in the spatial domain. By treating these two regions separately, depth estimation can be improved. Occlusion predictions can also be computed and used for regularization. Experimental results show that our method outperforms current state-of-the-art light-field depth estimation algorithms, especially near occlusion boundaries.
Contrasting the Spread of Misinformation in Online Social Networks

The emergence of online social networks has revolutionized the way people seek and share information. Nowadays, popular online social sites as Twitter, Facebook and Google+ are among the major news sources as well as the most effective channels for viral marketing. However, these networks also became the most effective channel for spreading misinformation, accidentally or maliciously. The widespread diffusion of inaccurate information or fake news can lead to undesirable and severe consequences, such as widespread panic, libelous campaigns and conspiracies. In order to guarantee the trustworthiness of online social networks it is a crucial challenge to find effective strategies to contrast the spread of the misinformation in the network. In this paper we concentrate our attention on two problems related to the diffusion of misinformation in social networks: identify the misinformation sources and limit its diffusion in the network. We consider a social network where some nodes have already been infected from misinformation. We first provide an heuristics to recognize the set of most probable sources of the infection. Then, we provide an heuristics to place a few monitors in some network nodes in order to control information diffused by the suspected nodes and block misinformation they injected in the network before it reaches a large part of the network. To verify the quality and efficiency of our suggested solutions, we conduct experiments on several real-world networks. Empirical results indicate that our heuristics are among the most effective known in literature.
Hardening COTS Software with Generic Software Wrappers

Numerous techniques exist to augment the security functionality of Commercial O -The-Shelf (COTS) applications and operating systems, making them more suitable for use in mission-critical systems. Although individually useful, as a group these techniques present di culties to system developers because they are not based on a common framework which might simplify integration and promote portability and reuse. This paper presents techniques for developing Generic Software Wrappers { protected, non-bypassable kernel-resident software extensions for augmenting security without modi cation of COTS source. We describe the key elements of our work: our high-level Wrapper De nition Language (WDL), and our framework for con guring, activating, and managing wrappers. We also discuss code reuse, automatic management of extensions, a framework for system-building through composition, platform-independence, and our experiences with our Solaris and FreeBSD prototypes.
Ensuring data storage security in Cloud Computing

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, Cloud Computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. This unique attribute, however, poses many new security challenges which have not been well understood. In this article, we focus on cloud data storage security, which has always been an important aspect of quality of service. To ensure the correctness of users' data in the cloud, we propose an effective and flexible distributed scheme with two salient features, opposing to its predecessors. By utilizing the homomorphic token with distributed verification of erasure-coded data, our scheme achieves the integration of storage correctness insurance and data error localization, i.e., the identification of misbehaving server(s). Unlike most prior works, the new scheme further supports secure and efficient dynamic operations on data blocks, including: data update, delete and append. Extensive security and performance analysis shows that the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks.
A Multi-Functional In-Memory Inference Processor Using a Standard 6T SRAM Array

A multi-functional in-memory inference processor integrated circuit (IC) in a 65-nm CMOS process is presented. The prototype employs a deep in-memory architecture (DIMA), which enhances both energy efficiency and throughput over conventional digital architectures via simultaneous access of multiple rows of a standard 6T bitcell array (BCA) per precharge, and embedding column pitch-matched low-swing analog processing at the BCA periphery. In doing so, DIMA exploits the synergy between the dataflow of machine learning (ML) algorithms and the SRAM architecture to reduce the dominant energy cost due to data movement. The prototype IC incorporates a 16-kB SRAM array and supports four commonly used ML algorithms—the support vector machine, template matching, <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor, and the matched filter. Silicon measured results demonstrate simultaneous gains (dot product mode) in energy efficiency of 10<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> and in throughput of 5.3<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> leading to a 53<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> reduction in the energy-delay product with negligible (<inline-formula> <tex-math notation="LaTeX">$\le $ </tex-math></inline-formula>1%) degradation in the decision-making accuracy, compared with the conventional 8-b fixed-point single-function digital implementations.
Social Learning Analytics

We propose that the design and implementation of effective Social Learning Analytics (SLA) present significant challenges and opportunities for both research and enterprise, in three important respects. The first is that the learning landscape is extraordinarily turbulent at present, in no small part due to technological drivers. Online social learning is emerging as a significant phenomenon for a variety of reasons, which we review, in order to motivate the concept of social learning. The second challenge is to identify different types of SLA and their associated technologies and uses. We discuss five categories of analytic in relation to online social learning; these analytics are either inherently social or can be socialised. This sets the scene for a third challenge, that of implementing analytics that have pedagogical and ethical integrity in a context where power and control over data are now of primary importance. We consider some of the concerns that learning analytics provoke, and suggest that Social Learning Analytics may provide ways forward. We conclude by revisiting the drivers and trends, and consider future scenarios that we may see unfold as SLA tools and services mature.