nils-herrmann/clean_scirepeval huggingface dataset:
The Strategies for Translating Proper Names in Children ’ s Literature

The translation of proper names is one of the most challenging activities every translator faces. While working on children’s literature, the translation is especially complicated since proper names usually have various allusions indicating sex, age, geographical belonging, history, specific meaning, playfulness of language and cultural connotations. The goal of this article is to draw attention to strategic choices for the translation of proper names in children’s literature. First, the article presents the theoretical considerations that deal with different aspects of proper names in literary works and the issue of their translation. Second, the translation strategies provided by the translation theorist Eirlys E. Davies used for this research are explained. In addition, the principles of adaptation of proper names provided the State Commission of the Lithuanian Language are presented. Then, the discussion proceeds to the quantitative analysis of the translated proper names with an emphasis on providing and explaining numerous examples. The research has been carried out on four popular fantasy books translated from English and German by three Lithuanian translators. After analyzing the strategies of preservation, localization, transformation and creation, the strategy of localization has proved to be the most frequent one in all translations.
Single-Image Super-Resolution Based on Rational Fractal Interpolation

This paper presents a novel single-image super-resolution (SR) procedure, which upscales a given low-resolution (LR) input image to a high-resolution image while preserving the textural and structural information. First, we construct a new type of bivariate rational fractal interpolation model and investigate its analytical properties. This model has different forms of expression with various values of the scaling factors and shape parameters; thus, it can be employed to better describe image features than current interpolation schemes. Furthermore, this model combines the advantages of rational interpolation and fractal interpolation, and its effectiveness is validated through theoretical analysis. Second, we develop a single-image SR algorithm based on the proposed model. The LR input image is divided into texture and non-texture regions, and then, the image is interpolated according to the characteristics of the local structure. Specifically, in the texture region, the scaling factor calculation is the critical step. We present a method to accurately calculate scaling factors based on local fractal analysis. Extensive experiments and comparisons with the other state-of-the-art methods show that our algorithm achieves competitive performance, with finer details and sharper edges.
Loss-aware Binarization of Deep Networks

Deep neural network models, though very powerful and highly successful, are computationally expensive in terms of space and time. Recently, there have been a number of attempts on binarizing the network weights and activations. This greatly reduces the network size, and replaces the underlying multiplications to additions or even XNOR bit operations. However, existing binarization schemes are based on simple matrix approximations and ignore the effect of binarization on the loss. In this paper, we propose a proximal Newton algorithm with diagonal Hessian approximation that directly minimizes the loss w.r.t. the binarized weights. The underlying proximal step has an efficient closed-form solution, and the second-order information can be efficiently obtained from the second moments already computed by the Adam optimizer. Experiments on both feedforward and recurrent networks show that the proposed loss-aware binarization algorithm outperforms existing binarization schemes, and is also more robust for wide and deep networks.
Higher-order Graph Convolutional Networks

Following the success of deep convolutional networks in various vision and speech related tasks, researchers have started investigating generalizations of the well-known technique for graph-structured data. A recently-proposed method called Graph Convolutional Networks has been able to achieve state-of-the-art results in the task of node classification. However, since the proposed method relies on localized first-order approximations of spectral graph convolutions, it is unable to capture higher-order interactions between nodes in the graph. In this work, we propose a motif-based graph attention model, called Motif Convolutional Networks, which generalizes past approaches by using weighted multi-hop motif adjacency matrices to capture higher-order neighborhoods. A novel attention mechanism is used to allow each individual node to select the most relevant neighborhood to apply its filter. Experiments show that our proposed method is able to achieve state-of-the-art results on the semi-supervised node classification task.
Short Paper: Service-Oriented Sharding for Blockchains

The rise of blockchain-based cryptocurrencies has led to an explosion of services using distributed ledgers as their underlying infrastructure. However, due to inherently single-service oriented blockchain protocols, such services can bloat the existing ledgers, fail to provide sufficient security, or completely forego the property of trustless auditability. Security concerns, trust restrictions, and scalability limits regarding the resource requirements of users hamper the sustainable development of loosely-coupled services on blockchains. This paper introduces Aspen, a sharded blockchain protocol designed to securely scale with increasing number of services. Aspen shares the same trust model as Bitcoin in a peer-to-peer network that is prone to extreme churn containing Byzantine participants. It enables introduction of new services without compromising the security, leveraging the trust assumptions, or flooding users with irrelevant messages.
Efficient RF energy harvesting by using multiband microstrip antenna arrays with multistage rectifiers

This paper covers the design, implementation and usage of a matched multiband microstrip antenna intended to collect RF energy efficiently in commonly used portions of the selected cellular frequency spectrum including the ISM bands. Multistage Schottky rectifier antenna (rectenna) array circuit is proposed for high energy conversion efficiency to improve battery life and portability. Design details for the multiband microstrip antenna prototype is presented.
How Linguistic and Cultural Forces Shape Conceptions of Time: English and Mandarin Time in 3D

In this paper we examine how English and Mandarin speakers think about time, and we test how the patterns of thinking in the two groups relate to patterns in linguistic and cultural experience. In Mandarin, vertical spatial metaphors are used more frequently to talk about time than they are in English; English relies primarily on horizontal terms. We present results from two tasks comparing English and Mandarin speakers' temporal reasoning. The tasks measure how people spatialize time in three-dimensional space, including the sagittal (front/back), transverse (left/right), and vertical (up/down) axes. Results of Experiment 1 show that people automatically create spatial representations in the course of temporal reasoning, and these implicit spatializations differ in accordance with patterns in language, even in a non-linguistic task. Both groups showed evidence of a left-to-right representation of time, in accordance with writing direction, but only Mandarin speakers showed a vertical top-to-bottom pattern for time (congruent with vertical spatiotemporal metaphors in Mandarin). Results of Experiment 2 confirm and extend these findings, showing that bilinguals' representations of time depend on both long-term and proximal aspects of language experience. Participants who were more proficient in Mandarin were more likely to arrange time vertically (an effect of previous language experience). Further, bilinguals were more likely to arrange time vertically when they were tested in Mandarin than when they were tested in English (an effect of immediate linguistic context).
STYLES AND STRATEGIES OF LEARNING

Previous results are reviewed and two series of experiments on learning are described, one carried out in the laboratory, and the other in educational institutions. Both series use • conversational' systems which allow mental activities to be described in terms of dialogue and behaviour. Several types of result are reported: (a) the significance of understanding ; (b) the existence of, and variations in, learning strategies; (c) the effect of matching and mismatching a teaching strategy to an individual's learning strategy; (d) the nature and classification of cognitive style; and (e) transfer effects and 'learning to learn.' A theoretical basis is developed for a classification of learning styles.
Dynamic MultiTask Learning with Convolutional Neural Network

Multi-task learning and deep convolutional neural network (CNN) have been successfully used in various fields. This paper considers the integration of CNN and multi-task learning in a novel way to further improve the performance of multiple related tasks. Existing multi-task CNN models usually empirically combine different tasks into a group which is then trained jointly with a strong assumption of model commonality. Furthermore, traditional approaches usually only consider small number of tasks with rigid structure, which is not suitable for large-scale applications. In light of this, we propose a dynamic multi-task CNN model to handle these problems. The proposed model directly learns the task relations from data instead of subjective task grouping. Due to its flexible structure, it supports task-wise incremental training, which is useful for efficient training of massive tasks. Specifically, we add a new task transfer connection (TTC) between the layers of each task. The learned TTC is able to reflect the correlation among different tasks guiding the model dynamically adjusting the multiplexing of the information among different tasks. With the help of TTC, multiple related tasks can further boost the whole performance for each other. Experiments demonstrate that the proposed dynamic multi-task CNN model outperforms traditional approaches.
Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping

Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is an ill-posed problem, so appropriate constraints are required to obtain reasonable solutions. One of the most prominent constraints is cycle consistency, which enforces the translated image by GXY to be translated back to the input image by an inverse mapping GY X . While cycle consistency requires the simultaneous training of GXY and GY X , recent studies have shown that one-sided domain mapping can be achieved by preserving pairwise distances between images. Although cycle consistency and distance preservation successfully constrain the solution space, they overlook the special properties of images that simple geometric transformations do not change the image’s semantic structure. Based on this special property, we develop a geometry-consistent generative adversarial network (GcGAN), which enables one-sided unsupervised domain mapping. GcGAN takes the original image and its counterpart image transformed by a predefined geometric transformation as inputs and generates two images in the new domain coupled with the corresponding geometry-consistency constraint. The geometryconsistency constraint reduces the space of possible solutions while keep the correct solutions in the search space. Quantitative and qualitative comparisons with the baseline (GAN alone) and the state-of-the-art methods including CycleGAN [62] and DistanceGAN  demonstrate the effectiveness of our method.
An empirical assessment of the EFQM Excellence Model : Evaluation as a TQM framework relative to the MBNQA Model

Total quality management (TQM) is an approach to management embracing both social and technical dimensions aimed at achieving excellent results, which needs to be put into practice through a specific framework. Nowadays, quality award models, such as the Malcolm Baldrige National Quality Award (MBNQA) and the European Foundation for Quality Management (EFQM) Excellence Model, are used as a guide to TQM implementation by a large number of organizations. Nevertheless, there is a paucity of empirical research confirming whether these models clearly reflect the main premises of TQM. The purpose of this paper is to analyze the extent to which the EFQM Excellence Model captures the main assumptions involved in the TQM concept, that is, the distinction between technical and social TQM issues, the holistic interpretation of TQM in the firm, and the causal linkage between TQM procedures and organizational performance. Based on responses collected from managers of 446 Spanish companies by means of a structured questionnaire, we find that: (a) social and technical dimensions are embedded in the model; (b) both dimensions are intercorrelated; (c) they jointly enhance results. These findings support the EFQM Excellence Model as an operational framework for TQM, and also reinforce the results obtained in previous studies for the MBNQA, suggesting that quality award models really are TQM frameworks.
Survey on Taxonomy Learning from Text Corpora : Issues , Resources and Recent Advances

A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations. While a large number of taxonomies have been constructed from human-compiled resources (e.g., Wikipedia), learning taxonomies from text corpora has received a growing interest and is essential for longtailed and domain-specific knowledge acquisition. In this paper, we overview recent advances on taxonomy construction from free texts, reorganizing relevant subtasks into a complete framework. We also overview resources for evaluation and discuss challenges for future research.
Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction

Maintaining accurate world knowledge in a complex and changing environment is a perennial problem for robots and other artificial intelligence systems. Our architecture for addressing this problem, called Horde, consists of a large number of independent reinforcement learning sub-agents, or demons. Each demon is responsible for answering a single predictive or goal-oriented question about the world, thereby contributing in a factored, modular way to the system’s overall knowledge. The questions are in the form of a value function, but each demon has its own policy, reward function, termination function, and terminal-reward function unrelated to those of the base problem. Learning proceeds in parallel by all demons simultaneously so as to extract the maximal training information from whatever actions are taken by the system as a whole. Gradient-based temporal-difference learning methods are used to learn efficiently and reliably with function approximation in this off-policy setting. Horde runs in constant time and memory per time step, and is thus suitable for learning online in realtime applications such as robotics. We present results using Horde on a multi-sensored mobile robot to successfully learn goal-oriented behaviors and long-term predictions from offpolicy experience. Horde is a significant incremental step towards a real-time architecture for efficient learning of general knowledge from unsupervised sensorimotor interaction.
Joint Learning of Local and Global Features for Entity Linking via Neural Networks

Previous studies have highlighted the necessity for entity linking systems to capture the local entity-mention similarities and the global topical coherence. We introduce a novel framework based on convolutional neural networks and recurrent neural networks to simultaneously model the local and global features for entity linking. The proposed model benefits from the capacity of convolutional neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. Our evaluation on multiple datasets demonstrates the effectiveness of the model and yields the state-of-the-art performance on such datasets. In addition, we examine the entity linking systems on the domain adaptation setting that further demonstrates the cross-domain robustness of the proposed model.
Mix&Match - Agent Curricula for Reinforcement Learning

We introduce Mix & Match (M&M) – a training framework designed to facilitate rapid and effective learning in RL agents, especially those that would be too slow or too challenging to train otherwise. The key innovation is a procedure that allows us to automatically form a curriculum over agents. Through such a curriculum we can progressively train more complex agents by, effectively, bootstrapping from solutions found by simpler agents. In contradistinction to typical curriculum learning approaches, we do not gradually modify the tasks or environments presented, but instead use a process to gradually alter how the policy is represented internally. We show the broad applicability of our method by demonstrating significant performance gains in three different experimental setups: (1) We train an agent able to control more than 700 actions in a challenging 3D first-person task; using our method to progress through an action-space curriculum we achieve both faster training and better final performance than one obtains using traditional methods. (2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state. (3) Finally, we illustrate how a variant of our method can be used to improve agent performance in a multitask setting.
Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset

Wi-Fi has become the de facto wireless technology for achieving short- to medium-range device connectivity. While early attempts to secure this technology have been proved inadequate in several respects, the current more robust security amendments will inevitably get outperformed in the future, too. In any case, several security vulnerabilities have been spotted in virtually any version of the protocol rendering the integration of external protection mechanisms a necessity. In this context, the contribution of this paper is multifold. First, it gathers, categorizes, thoroughly evaluates the most popular attacks on 802.11 and analyzes their signatures. Second, it offers a publicly available dataset containing a rich blend of normal and attack traffic against 802.11 networks. A quite extensive first-hand evaluation of this dataset using several machine learning algorithms and data features is also provided. Given that to the best of our knowledge the literature lacks such a rich and well-tailored dataset, it is anticipated that the results of the work at hand will offer a solid basis for intrusion detection in the current as well as next-generation wireless networks.
Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates

Smartphones are becoming a daily necessity for most undergraduates in Mainland China. Because the present scenario of problematic smartphone use (PSU) is largely unexplored, in the current study we aimed to estimate the prevalence of PSU and to screen suitable predictors for PSU among Chinese undergraduates in the framework of the stress-coping theory.



A sample of 1062 undergraduate smartphone users was recruited by means of the stratified cluster random sampling strategy between April and May 2015. The Problematic Cellular Phone Use Questionnaire was used to identify PSU. We evaluated five candidate risk factors for PSU by using logistic regression analysis while controlling for demographic characteristics and specific features of smartphone use.



The prevalence of PSU among Chinese undergraduates was estimated to be 21.3%. The risk factors for PSU were majoring in the humanities, high monthly income from the family (≥1500 RMB), serious emotional symptoms, high perceived stress, and perfectionism-related factors (high doubts about actions, high parental expectations).



PSU among undergraduates appears to be ubiquitous and thus constitutes a public health issue in Mainland China. Although further longitudinal studies are required to test whether PSU is a transient phenomenon or a chronic and progressive condition, our study successfully identified socio-demographic and psychological risk factors for PSU. These results, obtained from a random and thus representative sample of undergraduates, opens up new avenues in terms of prevention and regulation policies.
KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems

To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items. Existing datasets or methods either use side information from original recommender systems (containing very few kinds of useful information) or utilize private knowledge base (KB). In this paper, we present the first public linked KB dataset for recommender systems, named KB4Rec v1.0, which has linked three widely used RS datasets with the popular KB Freebase. Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i.e., popularity and recency) on whether a RS item can be linked to a KB entity. Finally, we present the comparison of several knowledge-aware recommendation algorithms on our linked dataset.
FREQUENCY EFFECTS IN LANGUAGE PROCESSING A Review with Implications for Theories of Implicit and Explicit Language Acquisition

This article shows how language processing is intimately tuned to input frequency. Examples are given of frequency effects in the processing of phonology, phonotactics, reading, spelling, lexis, morphosyntax, formulaic language, language comprehension, grammaticality, sentence production, and syntax. The implications of these effects for the representations and developmental sequence of SLA are discussed. Usage-based theories hold that the acquisition of language is exemplar based. It is the piecemeal learning of many thousands of constructions and the frequency-biased abstraction of regularities within them. Determinants of pattern productivity include the power law of practice, cue competition and constraint satisfaction, connectionist learning, and effects of type and token frequency. The regularities of language emerge from experience as categories and prototypical patterns. The typical route of emergence of constructions is from formula, through low-scope pattern, to construction. Frequency plays a large part in explaining sociolinguistic variation and language change. Learners’ sensitivity to frequency in all these domains has implications for theories of implicit and explicit learning and their interactions. The review concludes by considering the history of frequency as an explanatory concept in theoretical and applied linguistics, its 40 years of exile, and its necessary reinstatement as a bridging variable that binds the different schools of language acquisition research.
The next-to-shortest path problem on directed graphs with positive edge weights

Given an edge-weighted graph G and two distinct vertices s and t of G, the next-to-shortest path problem asks for a path from s to t of minimum length among all paths from s to t except the shortest ones. In this article, we consider the version where G is directed and all edge weights are positive. Some properties of the requested path are derived when G is an arbitrary digraph. In addition, if G is planar, an O(n3)-time algorithm is proposed, where n is the number of vertices of G.