nils-herrmann/clean_scirepeval huggingface dataset:
Extracting Synonyms from Dictionary Definitions

Automatic extraction of synonyms and/or semantically related words has various applications in Natural Language Processing (NLP). There are currently two mainstream extraction paradigms, namely, lexicon-based and distributional approaches. The former usually suffers from low coverage, while the latter is only able to capture general relatedness rather than strict synonymy. In this paper, two rule-based extraction methods are applied to definitions from a machine-readable dictionary. Extracted synonyms are evaluated in two experiments by solving TOEFL synonym questions and being compared against existing thesauri. The proposed approaches have achieved satisfactory results in both evaluations, comparable to published studies or even the state of the art.
An E-learning System Architecture based on Cloud Computing

The massive proliferation of affordable computers, Internet broadband connectivity and rich education content has created a global phenomenon in which information an d communication technology (ICT) is being used to tra nsform education. Therefore, there is a need to redesign t he educational system to meet the needs better. The advent of comp uters with sophisticated software has made it possible to solv e many complex problems very fast and at a lower cost. This paper introduces the characteristics of the current E-Learning and then analyses the concept of cloud computing and describes the archit e ture of cloud computing platform by combining the features of E-L earning. The authors have tried to introduce cloud computing to e-learning, build an e-learning cloud, and make an active research an d exploration for it from the following aspects: architecture, const ruc ion method and external interface with the model. Architecture, Cloud Computing, E-learning, Information Technology
Open-Domain Event Detection using Distant Supervision

This paper introduces open-domain event detection, a new event detection paradigm to address issues of prior work on restricted domains and event annotation. The goal is to detect all kinds of events regardless of domains. Given the absence of training data, we propose a distant supervision method that is able to generate high-quality training data. Using a manually annotated event corpus as gold standard, our experiments show that despite no direct supervision, the model outperforms supervised models. This result indicates that the distant supervision enables robust event detection in various domains, while obviating the need for human annotation of events.
Bio-Inspired Model Learning Visual Goals and Attention Skills Through Contingencies and Intrinsic Motivations

Animal learning is driven not only by biological needs but also by intrinsic motivations (IMs) serving the acquisition of knowledge. Computational modeling involving IMs is indicating that learning of motor skills requires that autonomous agents self-generate tasks/goals and use them to acquire skills solving/leading to them. We propose a neural architecture driven by IMs that is able to self-generate goals on the basis of the environmental changes caused by the agent’s actions. The main novelties of the model are that it is focused on the acquisition of attention (looking) skills and that its architecture and functioning are broadly inspired by the functioning of relevant primate brain areas (superior colliculus, basal ganglia, and frontal cortex). These areas, involved in IM-based behavior learning, play important functions for reflexive and voluntary attention. The model is tested within a simple simulated pan-tilt camera robot engaged in learning to switch on different lights by looking at them, and is able to self-generate visual goals and learn attention skills under IM guidance. The model represents a novel hypothesis on how primates and robots might autonomously learn attention skills and has a potential to account for developmental psychology experiments and the underlying brain mechanisms.
Conditional Random Fields as Recurrent Neural Networks

Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks. One central issue in this methodology is the limited capacity of deep learning techniques to delineate visual objects. To solve this problem, we introduce a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To this end, we formulate Conditional Random Fields with Gaussian pairwise potentials and mean-field approximate inference as Recurrent Neural Networks. This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a deep network that has desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF modelling with CNNs, making it possible to train the whole deep network end-to-end with the usual back-propagation algorithm, avoiding offline post-processing methods for object delineation. We apply the proposed method to the problem of semantic image segmentation, obtaining top results on the challenging Pascal VOC 2012 segmentation benchmark.
Generating Accurate Rule Sets Without Global Optimization

The two dominant schemes for rule-learning, C4.5 and RIPPER, both operate in two stages. First they induce an initial rule set and then they refine it using a rather complex optimization stage that discards (C4.5) or adjusts (RIPPER) individual rules to make them work better together. In contrast, this paper shows how good rule sets can be learned one rule at a time, without any need for global optimization. We present an algorithm for inferring rules by repeatedly generating partial decision trees, thus combining the two major paradigms for rule generation—creating rules from decision trees and the separate-and-conquer rule-learning technique. The algorithm is straightforward and elegant: despite this, experiments on standard datasets show that it produces rule sets that are as accurate as and of similar size to those generated by C4.5, and more accurate than RIPPER’s. Moreover, it operates efficiently, and because it avoids postprocessing, does not suffer the extremely slow performance on pathological example sets for which the C4.5 method has been criticized.
Cross-platform mobile development: a study on apps with animations

The paper presents a comparison between different frameworks for cross-platform mobile development, MoSync, Titanium, jQuery Mobile and Phonegap, with particular attention to development of applications with animations. We define a set of criteria for the evaluation and we develop the same game as case study app, with the aim to provide an unbias judgement.
 Our analysis recommends Titanium as the best framework to develop mobile applications with animations.
Optimal and Autonomous Control Using Reinforcement Learning: A Survey

This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.
optCluster: An R Package for Determining the Optimal Clustering Algorithm

There exist numerous programs and packages that perform validation for a given clustering solution; however, clustering algorithms fare differently as judged by different validation measures. If more than one performance measure is used to evaluate multiple clustering partitions, an optimal result is often difficult to determine by visual inspection alone. This paper introduces optCluster, an R package that uses a single function to simultaneously compare numerous clustering partitions (created by different algorithms and/or numbers of clusters) and obtain a "best" option for a given dataset. The method of weighted rank aggregation is utilized by this package to objectively aggregate various performance measure scores, thereby taking away the guesswork that often follows a visual inspection of cluster results. The optCluster package contains biological validation measures as well as clustering algorithms developed specifically for RNA sequencing data, making it a useful tool for clustering genomic data.



This package is available for free through the Comprehensive R Archive Network (CRAN) at http://cran.rproject.org/web/packages/optCluster/.
MARKETING AND FIRM VALUE Metrics , Methods , Findings , and Future Directions

The marketing profession is being challenged to assess and communicate the value created by its actions on shareholder value. These demands create a need to translate marketing resource allocations and their performance consequences into financial and firm value effects. The objective of this paper is to integrate the existing knowledge on the impact of marketing on firm value. The authors first frame the important research questions on marketing and firm value and review the important investor response metrics and relevant analytical models, as they relate to marketing. The authors next summarize the empirical findings to date on how marketing creates shareholder value, including the impact of brand equity, customer equity, customer satisfaction, R&D, product quality and specific marketing-mix actions. In addition the authors review emerging findings on biases in investor response to marketing actions. The paper concludes by formulating an agenda for future research challenges in this emerging area.
De-noising by soft-thresholding

Donoho and Johnstone (1992a) proposed a method for reconstructing an unknown function f on [0; 1] from noisy data di = f(ti) + zi, i = 0; : : : ; n 1, ti = i=n, zi iid N(0; 1). The reconstruction f̂ n is de ned in the wavelet domain by translating all the empirical wavelet coe cients of d towards 0 by an amount p 2 log(n) = p n. We prove two results about that estimator.  With high probability f̂ n is at least as smooth as f , in any of a wide variety of smoothness measures.  The estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. Our proof of these results develops new facts about abstract statistical inference and its connection with an optimal recovery model.
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling

State-of-the-art slot filling models for goal-oriented human/machine conversational language understanding systems rely on deep learning methods. While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems. This paper proposes a deep learning based approach that can utilize only the slot description in context without the need for any labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The main idea of this paper is to leverage the encoding of the slot names and descriptions within a multi-task deep learned slot filling model, to implicitly align slots across domains. The proposed approach is promising for solving the domain scaling problem and eliminating the need for any manually annotated data or explicit schema alignment. Furthermore, our experiments on multiple domains show that this approach results in significantly better slot-filling performance when compared to using only in-domain data, especially in the low data regime.
Vein Detection System using Infrared Light

The process of obtaining intravenous (IV) access, Venipuncture, is an everyday invasive procedure in medical settings and there are more than one billion venipuncture related procedures like blood draws, peripheral catheter insertions, intravenous therapies, etc. performed per year . Excessive venipunctures are both time and resource consuming events causing anxiety, pain and distress in patients, or can lead to severe harmful injuries . The major problem faced by the doctors today is difficulty in accessing veins for intra-venous drug delivery & other medical situations . There is a need to develop vein detection devices which can clearly show veins. This project deals with the design development of non-invasive subcutaneous vein detection system and is implemented based on near infrared imaging and interfaced to a laptop to make it portable. A customized CCD camera is used for capturing the vein images and Computer Software modules (MATLAB & LabVIEW) is used for the processing .
Detection and Prediction of Phishing Websites using Classification Mining Techniques

Phishing is serious web security problem that involves mimicking legitimate websites to deceive online users in order to steal their sensitive information. Phishing can be seen as a typical classification problem in data mining where the classifier is constructed from large number of website’s features.There are high demands on identifying the best set of features that when mined the predictive accuracy of the classifiers is enhanced. This paper investigates features selection aiming to determine the effective set of features in terms of classification performance. We compare two known featuresselectionmethod in order to determine the least set of features of phishing detection using data mining. Experimental tests on large number of features data set havebeen doneusing Information Gain and Correlation Features set methods. Further, two data mining algorithms namely PART and IREP have been trained on different sets of selected features to show the pros and cons of the feature selection process. We have been able to identify new knowledge in the forms of rules that show vital correlations among significant features.
Repairing skeletal muscle: regenerative potential of skeletal muscle stem cells.

Skeletal muscle damaged by injury or by degenerative diseases such as muscular dystrophy is able to regenerate new muscle fibers. Regeneration mainly depends upon satellite cells, myogenic progenitors localized between the basal lamina and the muscle fiber membrane. However, other cell types outside the basal lamina, such as pericytes, also have myogenic potency. Here, we discuss the main properties of satellite cells and other myogenic progenitors as well as recent efforts to obtain myogenic cells from pluripotent stem cells for patient-tailored cell therapy. Clinical trials utilizing these cells to treat muscular dystrophies, heart failure, and stress urinary incontinence are also briefly outlined.
Working Paper / Document de travail 2013-26 Uncertain Fiscal Consolidations

Bank of Canada working papers are theoretical or empirical works-in-progress on subjects in economics and finance. The views expressed in this paper are those of the authors. No responsibility for them should be attributed to the Bank of Canada. ii Acknowledgements We are grateful to Alberto Alesina and Silvia Ardagna for providing us with their dataset. For many useful comments, we thank the editor, Wouter Den Haan, and an anonymous referee, as well as  The paper explores the macroeconomic consequences of fiscal consolidations whose timing and composition—either tax-or spending-based—are uncertain. We find that the composition of the fiscal consolidation, its duration, the monetary policy stance, the level of government debt, and expectations over the likelihood and composition of fiscal consolidations all matter in determining the extent to which a given consolidation is expansionary or successful in stabilizing government debt. We argue that the conditions that could render fiscal consolidation efforts expansionary are unlikely to apply in the current economic environment. Résumé L'étude explore les conséquences macroéconomiques de programmes d'assainissement budgétaire dont on ne connaît avec certitude ni le calendrier, ni la composition (l'État haussera-t-il les impôts ou réduira-t-il ses dépenses?). Les auteurs montrent que la composition d'un programme donné, sa durée, les anticipations relatives à la vraisemblance de sa mise en oeuvre et à sa composition, l'orientation de la politique monétaire et le niveau de la dette publique influent tous sur la mesure dans laquelle ce programme aura des effets expansionnistes ou réussira à stabiliser la dette publique. Ils jugent peu probable, dans le contexte économique actuel, que les conditions nécessaires pour que les efforts de redressement budgétaire stimulent l'activité économique soient réunies.
Network Programmability Using POX Controller

POX is a Python based open source OpenFlow/Software Defined Networking (SDN) Controller. POX is used for faster development and prototyping of new network applications. POX controller comes pre installed with the mininet virtual machine. Using POX controller you can turn dumb openflow devices into hub, switch, load balancer, firewall devices. The POX controller allows easy way to run OpenFlow/SDN experiments. POX can be passed different parameters according to real or experimental topologies, thus allowing you to run experiments on real hardware, testbeds or in mininet emulator. In this paper, first section will contain introduction about POX, OpenFlow and SDN, then discussion about relationship between POX and Mininet. Final Sections will be regarding creating and verifying behavior of network applications in POX.
Infant-Guided, Co-Regulated Feeding in the Neonatal Intensive Care Unit. Part I: Theoretical Underpinnings for Neuroprotection and Safety.

The rapid progress in medical and technical innovations in the neonatal intensive care unit (NICU) has been accompanied by concern for outcomes of NICU graduates. Although advances in neonatal care have led to significant changes in survival rates of very small and extremely preterm neonates, early feeding difficulties with the transition from tube feeding to oral feeding are prominent and often persist beyond discharge to home. Progress in learning to feed in the NICU and continued growth in feeding skills after the NICU may be closely tied to fostering neuroprotection and safety. The experience of learning to feed in the NICU may predispose preterm neonates to feeding problems that persist. Neonatal feeding as an area of specialized clinical practice has grown considerably in the last decade. This article is the first in a two-part series devoted to neonatal feeding. Part 1 explores factors in NICU feeding experiences that may serve to constrain or promote feeding skill development, not only in the NICU but long after discharge to home. Part II describes approaches to intervention that support neuroprotection and safety.
Playing Go without Game Tree Search Using Convolutional Neural Networks

The game of Go has a long history in East Asian countries, but the field of Computer Go has yet to catch up to humans until the past couple of years. While the rules of Go are simple, the strategy and combinatorics of the game are immensely complex. Even within the past couple of years, new programs that rely on neural networks to evaluate board positions still explore many orders of magnitude more board positions per second than a professional can. We attempt to mimic human intuition in the game by creating a convolutional neural policy network which, without any sort of tree search, should play the game at or above the level of most humans. We introduce three structures and training methods that aim to create a strong Go player: non-rectangular convolutions, which will better learn the shapes on the board, supervised learning, training on a data set of 53,000 professional games, and reinforcement learning, training on games played between different versions of the network. Our network has already surpassed the skill level of intermediate amateurs simply using supervised learning. Further training and implementation of non-rectangular convolutions and reinforcement learning will likely increase this skill level much further.
Analysis of Retinal Vessel Segmentation with Deep Learning and its Effect on Diabetic Retinopathy Classification

The success of deep learning methodologies draws a huge attention to their applications in medical image analysis. One of the applications of deep learning is in segmentation of retinal vessel and severity classification of diabetic retinopathy (DR) from retinal funduscopic image. This paper studies U-Net model performance in segmenting retinal vessel with different settings of dropout and batch normalization and use it to investigate the effect of retina vessel in DR classification. Pre-trained Inception V1 network was used to classify the DR severity. Two sets of retinal images, with and without the presence of vessel, were created from MESSIDOR dataset. The vessel extraction process was done using the best trained U-Net on DRIVE dataset. Final analysis showed that retinal vessel is a good feature in classifying both severe and early cases of DR stage.
Adversarial Feature Augmentation for Unsupervised Domain Adaptation

Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to train powerful classifiers for the target samples. In particular, it was shown that a GAN objective function can be used to learn target features indistinguishable from the source ones. In this work, we extend this framework by (i) forcing the learned feature extractor to be domain-invariant, and (ii) training it through data augmentation in the feature space, namely performing feature augmentation. While data augmentation in the image space is a well established technique in deep learning, feature augmentation has not yet received the same level of attention. We accomplish it by means of a feature generator trained by playing the GAN minimax game against source features. Results show that both enforcing domain-invariance and performing feature augmentation lead to superior or comparable performance to state-of-the-art results in several unsupervised domain adaptation benchmarks.
Accurate Image Super-Resolution Using Very Deep Convolutional Networks

We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates (104 times higher than SRCNN [6]) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.