nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised author disambiguation using Dempster–Shafer theory

The name ambiguity problem presents many challenges for scholar finding, citation analysis and other related research fields. To attack this issue, various disambiguation methods combined with separate disambiguation features have been put forward. In this paper, we offer an unsupervised Dempster–Shafer theory (DST) based hierarchical agglomerative clustering algorithm for author disambiguation tasks. Distinct from existing methods, we exploit the DST in combination with Shannon’s entropy to fuse various disambiguation features and come up with a more reliable candidate pair of clusters for amalgamation in each iteration of clustering. Also, some solutions to determine the convergence condition of the clustering process are proposed. Depending on experiments, our method outperforms three unsupervised models, and achieves comparable performances to a supervised model, while does not prescribe any hand-labelled training data.
Applying OLSR routing in FANETs

MANETs are getting exposure due to their versatile applications in the last few years. New networking paradigms like VANETs and FANETs have evolved by using the concept of MANETs. FANETs provide a distinguished approach to tackle with the situations like emergency, natural disaster, military battle fields, UAVs etc. Due to the high mobility in FANETs nodes and rapid topology change, it is a big challenge for researcher to apply routing in FANETs. Mobility models also play a very important role in optimizing the performance of routing protocol in FANET. The research presented aims to apply OLSR routing protocol in FANETs and study of OLSR under different mobility models to optimize the performance of OLSR in FANETs.
Research Division Federal Reserve Bank of St . Louis Working Paper Series Credit Scoring and Loan Default

This paper introduces a measure of credit score performance that abstracts from the inuence of situational factors.Using this measure, we study the role and e¤ectiveness of credit scoring that underlied subprime securities during the mortgage boom of 2000-2006. Parametric and nonparametric measures of credit score performance reveal di¤erent trends, especially on originations with low credit scores. The paper demonstrates an increasing trend of reliance on credit scoring not only as a measure of credit risk but also as a means to o¤set other riskier attributes of the origination. This reliance led to deterioration in loan performance even though average credit quality as measured in terms of credit scores actually improved over the years.
Interaction of visual and proprioceptive feedback during adaptation of human reaching movements.

People tend to make straight and smooth hand movements when reaching for an object. These trajectory features are resistant to perturbation, and both proprioceptive as well as visual feedback may guide the adaptive updating of motor commands enforcing this regularity. How is information from the two senses combined to generate a coherent internal representation of how the arm moves? Here we show that eliminating visual feedback of hand-path deviations from the straight-line reach (constraining visual feedback of motion within a virtual, "visual channel") prevents compensation of initial direction errors induced by perturbations. Because adaptive reduction in direction errors occurred with proprioception alone, proprioceptive and visual information are not combined in this reaching task using a fixed, linear weighting scheme as reported for static tasks not requiring arm motion. A computer model can explain these findings, assuming that proprioceptive estimates of initial limb posture are used to select motor commands for a desired reach and visual feedback of hand-path errors brings proprioceptive estimates into registration with a visuocentric representation of limb position relative to its target. Simulations demonstrate that initial configuration estimation errors lead to movement direction errors as observed experimentally. Registration improves movement accuracy when veridical visual feedback is provided but is not invoked when hand-path errors are eliminated. However, the visual channel did not exclude adjustment of terminal movement features maximizing hand-path smoothness. Thus visual and proprioceptive feedback may be combined in fundamentally different ways during trajectory control and final position regulation of reaching movements.
A task in a suit and a tie: paraphrase generation with semantic augmentation

Paraphrasing is rooted in semantics. We show the effectiveness of transformers (Vaswani et al. 2017) for paraphrase generation and further improvements by incorporating PropBank labels via a multi-encoder. Evaluating on MSCOCO and WikiAnswers, we find that transformers are fast and effective, and that semantic augmentation for both transformers and LSTMs leads to sizable 2-3 point gains in BLEU, METEOR and TER. More importantly, we find surprisingly large gains on human evaluations compared to previous models. Nevertheless, manual inspection of generated paraphrases reveals ample room for improvement: even our best model produces human-acceptable paraphrases for only 28% of captions from the CHIA dataset (Sharma et al. 2018), and it fails spectacularly on sentences from Wikipedia. Overall, these results point to the potential for incorporating semantics in the task while highlighting the need for stronger evaluation.
Towards the first adversarially robust neural network model on MNIST

Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans. We show that even the widely recognized and by far most successful defense by Madry et al. (1) overfits on the L∞ metric (it’s highly susceptible to L2 and L0 perturbations), (2) classifies unrecognizable images with high certainty, (3) performs not much better than simple input binarization and (4) features adversarial perturbations that make little sense to humans. These results suggest that MNIST is far from being solved in terms of adversarial robustness. We present a novel robust classification model that performs analysis by synthesis using learned class-conditional data distributions. We derive bounds on the robustness and go to great length to empirically evaluate our model using maximally effective adversarial attacks by (a) applying decisionbased, score-based, gradient-based and transfer-based attacks for several different Lp norms, (b) by designing a new attack that exploits the structure of our defended model and (c) by devising a novel decision-based attack that seeks to minimize the number of perturbed pixels (L0). The results suggest that our approach yields state-of-the-art robustness on MNIST against L0, L2 and L∞ perturbations and we demonstrate that most adversarial examples are strongly perturbed towards the perceptual boundary between the original and the adversarial class.
Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation

Previous work has shown that high quality phrasal paraphrases can be extracted from bilingual parallel corpora. However, it is not clear whether bitexts are an appropriate resource for extracting more sophisticated sentential paraphrases, which are more obviously learnable from monolingual parallel corpora. We extend bilingual paraphrase extraction to syntactic paraphrases and demonstrate its ability to learn a variety of general paraphrastic transformations, including passivization, dative shift, and topicalization. We discuss how our model can be adapted to many text generation tasks by augmenting its feature set, development data, and parameter estimation routine. We illustrate this adaptation by using our paraphrase model for the task of sentence compression and achieve results competitive with state-of-the-art compression systems.
A critical look at technological innovation typology and innovativeness terminology : a literature review

A plethora of definitions for innovation types has resulted in an ambiguity in the way the terms ‘innovation’ and ‘innovativeness’ are operationalized and utilized in the new product development literature. The terms radical, really-new, incremental and discontinuous are used ubiquitously to identify innovations. One must question, what is the difference between these different classifications? To date consistent definitions for these innovation types have not emerged from the new product research community. A review of the literature from the marketing, engineering, and new product development disciplines attempts to put some clarity and continuity to the use of these terms. This review shows that it is important to consider both a marketing and technological perspective as well as a macrolevel and microlevel perspective when identifying innovations. Additionally, it is shown when strict classifications from the extant literature are applied, a significant shortfall appears in empirical work directed toward radical and really new innovations. A method for classifying innovations is suggested so that practitioners and academics can talk with a common understanding of how a specific innovation type is identified and how the innovation process may be unique for that particular innovation type. A recommended list of measures based on extant literature is provided for future empirical research concerning technological innovations and innovativeness. A rose is a rose is a rose. And a rose by any other name would smell just as sweet.” Gertrude Stein & William Shakespeare
A single feed dual-band circularly polarized millimeter-wave antenna for 5G communication

In this paper, one simple dual-band (28 and 38 GHz) circularly polarized slotted patch antenna, suitable for future millimeter wave mobile communication, is presented. The proposed monolayer circularly polarized patch antenna is considered without any 90° phase shifter and is excited by a single-feed microstrip line, which is very desirable for high gain antenna array implementation in millimeter wave band in order to compensate the link loss. From EM simulation, it has been found that the dimensions of the L-shaped slots significantly affect the performance of the antenna. The antenna has been prototyped and the measured results show that the antenna can simultaneously provide circular polarization in the two suggested millimeter-wave bands for the upcoming 5G mobile communications.
VAES 3 scheme for FFX An addendum to “ The FFX Mode of Operation for Format-Preserving Encryption ”

VAES stands for variable AES. VAES3 is the third generation format-preserving encryption algorithm that was developed in a report [4] simultaneously with the comprehensive paper on FPE  and subsequently updated slightly to be in concert with the FFX standard proposal. The standard proposal of FFX includes, in an appendix, example instantiations called A2 and A10. A follow on addendum  includes an instantiation called FFX[radix] . The stated intent of FFX is that it is a framework under which many implementations are compliant. The VAES3 scheme is compliant to those requirements. VAES3 was designed to meet security goals and requirements beyond the original example instantiations, and its design goals are slightly different than those of FFX[radix]. One of the unique features of VAES3 is a subkey step that enhances security and lengthens the lifetime of the key.
Zero-Shot Activity Recognition with Verb Attribute Induction

In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb “salute” has several properties, such as being a light movement, a social act, and short in duration. We use these attributes as the internal mapping between visual and textual representations to reason about a previously unseen action. In contrast to much prior work that assumes access to gold standard attributes for zero-shot classes and focuses primarily on object attributes, our model uniquely learns to infer action attributes from dictionary definitions and distributed word representations. Experimental results confirm that action attributes inferred from language can provide a predictive signal for zero-shot prediction of previously unseen activities.
Building an Argument Search Engine for the Web

Computational argumentation is expected to play a critical role in the future of web search. To make this happen, many searchrelated questions must be revisited, such as how people query for arguments, how to mine arguments from the web, or how to rank them. In this paper, we develop an argument search framework for studying these and further questions. The framework allows for the composition of approaches to acquiring, mining, assessing, indexing, querying, retrieving, ranking, and presenting arguments while relying on standard infrastructure and interfaces. Based on the framework, we build a prototype search engine, called args, that relies on an initial, freely accessible index of nearly 300k arguments crawled from reliable web resources. The framework and the argument search engine are intended as an environment for collaborative research on computational argumentation and its practical evaluation.
Melatonin and the pineal gland: influence on mammalian seasonal and circadian physiology.

The pineal hormone melatonin is secreted with a marked circadian rhythm. Normally, maximum production occurs during the dark phase of the day and the duration of secretion reflects the duration of the night. The changing profile of secretion as a function of daylength conveys photoperiodic information for the organization of seasonal rhythms in mammals. The role of melatonin in mammalian circadian physiology is less clear. However, exogenous melatonin can phase shift, and in some cases entrain, circadian rhythms in rodents and humans. It can also lower body temperature and induce transient sleepiness. These properties indicate that melatonin can be used therapeutically in circadian rhythm disorder. Successful outcomes have been reported, for example in jet lag and shift work, and with cyclic sleep disorder of some blind subjects. Melatonin receptors of several subtypes are found in the brain, the retina, the pituitary and elsewhere. They are currently under intense investigation. Melatonin agonists and antagonists are under development.
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial

This tutorial introduces a new and powerful set of techniques variously called “neural machine translation” or “neural sequence-to-sequence models”. These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyone who wants to model sequential data of some sort. The tutorial assumes that the reader knows the basics of math and programming, but does not assume any particular experience with neural networks or natural language processing. It attempts to explain the intuition behind the various methods covered, then delves into them with enough mathematical detail to understand them concretely, and culiminates with a suggestion for an implementation exercise, where readers can test that they understood the content in practice.
Deep Learning Based Recommender System: A Survey and New Perspectives

With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.
Small Signal Analysis for LLC Resonant Converter

− In this paper, small signal characteristic of a LLC resonant converter is analyzed. LLC resonant converter was been reported in many papers recently because of its simple structure, high efficiency and high switching frequency capability. In order to make practical use of this topology, understanding its small signal characteristic is essential to design the control loop. For PWM converter, state space average method is well established and verified. For resonant converter, because of the different ways of operation, state space average model cannot give satisfactory result. In this paper, simulation method is used to derive the small signal model of resonant converter. With this method, Series Resonant Converter and LLC resonant converter were been analyzed. Finally, a prototype of LLC resonant converter was built and tested to verify the results get from previous analysis.
Deep Learning Models For Multiword Expression Identification

Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, with a convolutional neural network with three hidden layers giving the best performance.
SemEval-2017 Task 3: Community Question Answering

This paper describes the SemEval–2016 Task 3 on Community Question Answering, which we offered in English and Arabic. For English, we had three subtasks: Question–Comment Similarity (subtask A), Question–Question Similarity (B), and Question–External Comment Similarity (C). For Arabic, we had another subtask: Rerank the correct answers for a new question (D). Eighteen teams participated in the task, submitting a total of 95 runs (38 primary and 57 contrastive) for the four subtasks. A variety of approaches and features were used by the participating systems to address the different subtasks, which are summarized in this paper. The best systems achieved an official score (MAP) of 79.19, 76.70, 55.41, and 45.83 in subtasks A, B, C, and D, respectively. These scores are significantly better than those for the baselines that we provided. For subtask A, the best system improved over the 2015 winner by 3 points absolute in terms of Accuracy.
Human-Centric Justification of Machine Learning Predictions

Human decision makers in many domains can make use of predictions made by machine learning models in their decision making process, but the usability of these predictions is limited if the human is unable to justify his or her trust in the prediction. We propose a novel approach to producing justifications that is geared towards users without machine learning expertise, focusing on domain knowledge and on human reasoning, and utilizing natural language generation. Through a taskbased experiment, we show that our approach significantly helps humans to correctly decide whether or not predictions are accurate, and significantly increases their satisfaction with the justification.
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss – an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification. Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation. Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected. By leveraging both the network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training. To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering of the nodes imposed by the network structure. Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks. Additionally, we demonstrate the benefits of modeling uncertainty – by analyzing it we can estimate neighborhood diversity and detect the intrinsic latent dimensionality of a graph.
Neural network gradient-based learning of black-box function interfaces

Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training. In many cases, it is possible to decompose a task to a series of functions, of which for some we may prefer to use a neural network to learn the functionality, while for others the preferred method would be to use existing black-box functions. We propose a method for end-to-end training of a base neural network that integrates calls to existing blackbox functions. We do so by approximating the black-box functionality with a differentiable neural network in a way that drives the base network to comply with the black-box function interface during the end-to-end optimization process. At inference time, we replace the differentiable estimator with its external blackbox non-differentiable counterpart such that the base network output matches the input arguments of the black-box function. Using this “Estimate and Replace” paradigm, we train a neural network, end to end, to compute the input to blackbox functionality while eliminating the need for intermediate labels. We show that by leveraging the existing precise black-box function during inference, the integrated model generalizes better than a fully differentiable model, and learns more efficiently compared to RL-based methods.