nils-herrmann/clean_scirepeval huggingface dataset:
A Three-Stage Text Normalization Strategy for Mandarin Text-to-Speech Systems

Text normalization is an important component in mandarin Text-to-Speech system. This paper develops a taxonomy of Non-Standard Words (NSW's) based on a Large-scale Chinese corpus and proposes a three-stage text normalization strategy: Finite State Automata (FSA) for initial classification, Maximum Entropy (ME) Classifier & Rules for further classification and General Rules for standard word conversion. The three-stage approach achieves Precision of 96.02% in experiments, 5.21% higher than that of simple rule based approach and 2.21% higher than that of simple machine learning method. Experiments results show that the approach of three-stage disambiguation strategy for text normalization makes considerable improvement, and works well in real TTS system.
Antifragile Communications

This paper introduces the concept of antifragile communications, which we define as the capability for a communications system to improve in performance due to a system stressor or harsh condition. Antifragility refers to systems that increase in capability, resilience, or robustness as a result of mistakes, faults, attacks, or failures. An antifragile system is fundamentally different from one that is resilient (able to recover from failure) and robust (able to resist failure). We apply the concept of antifragility to wireless communications where the system stressor is a jammer that intends to disrupt the underlying communications. Through a novel strategy, we exploit the communications jammer, providing an increase in communications capability such as bits per second. We show that an antifragile gain is possible under a wide variety of reactive jamming scenarios and provide guidelines for realizing these gains by creating an antifragile waveform.
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories

Human emotions are complex and nuanced. Yet, an overwhelming majority of the work in automatically detecting emotions from text has focused only on classifying text into positive, negative, and neutral classes, and a much smaller amount on classifying text into basic emotion categories such as joy, sadness, and fear. Our goal is to create a single textual dataset that is annotated for many emotion (or affect) dimensions (from both the basic emotion model and the VAD model). For each emotion dimension, we annotate the data for not just coarse classes (such as anger or no anger) but also for fine-grained real-valued scores indicating the intensity of emotion (anger, sadness, valence, etc.). We use Best–Worst Scaling (BWS) to address the limitations of traditional rating scale methods such as interand intra-annotator inconsistency by employing comparative annotations. We show that the fine-grained intensity scores thus obtained are reliable (repeat annotations lead to similar scores). We choose Twitter as the source of the textual data we annotate because tweets are self-contained, widely used, public posts, and tend to be rich in emotions. The new dataset is useful for training and testing supervised machine learning algorithms for multi-label emotion classification, emotion intensity regression, detecting valence, detecting ordinal class of intensity of emotion (slightly sad, very angry, etc.), and detecting ordinal class of valence (or sentiment). We make the data available for the recent SemEval-2018  Affect in Tweets, which explores these five tasks. The dataset also sheds light on crucial research questions such as: which emotions often present together in tweets?; how do the intensities of the three negative emotions relate to each other?; and how do the intensities of the basic emotions relate to valence?
Result Diversification in Automatic Citation Recommendation

The increase in the number of published papers each year makes manual literature search inefficient and furthermore insufficient. Hence, automatized reference/citation recommendation have been of interest in the last 3-4 decades. Unfortunately, some of the developed approaches, such as keyword-based ones, are prone to ambiguity and synonymy. On the other hand, using the citation information does not suffer from the same problems since they do not consider textual similarity. Today, obtaining the desired information is as hard as looking for a needle in a haystack. And sometimes, we want that small haystack, e.g., a small result set containing only a few recommendations, cover all the important and relevant parts of the literature. That is, the set should be diversified enough. Here, we investigate the problem of result diversification in automatic citation recommendation. We enhance existing techniques, which were designed to recommend a set of citations with satisfactory quality and diversity, with direction-awareness to allow the users to reach either old, well-cited, well-known research papers or recent, less-known ones. We also propose some novel techniques for a better result diversification. Experimental results show that our techniques are very useful in automatic citation recommendation.
How the Temporo-Parietal Cortex identifies Action Goals : The Mirror

This meta-analysis explores the location and function of brain areas involved in social cognition, or the capacity to understand people’s behavioral intentions, social beliefs, and personality traits. On the basis of over 200 fMRI studies, it tests alternative theoretical proposals that attempt to explain how several brain areas process information relevant for social cognition. The results suggest that inferring temporary states such as goals, intentions, and desires of other people—even when they are false and unjust from our own perspective—strongly engages the temporo-parietal junction (TPJ). Inferring more enduring dispositions of others and the self, or interpersonal norms and scripts, engages the medial prefrontal cortex (mPFC), although temporal states can also activate the mPFC. Other candidate tasks reflecting general-purpose brain processes that may potentially subserve social cognition are briefly reviewed, such as sequence learning, causality detection, emotion processing, and executive functioning (action monitoring, attention, dual task monitoring, episodic memory retrieval), but none of them overlaps uniquely with the regions activated during social cognition. Hence, it appears that social cognition particularly engages the TPJ and mPFC regions. The available evidence is consistent with the role of a TPJ-related mirror system for inferring temporary goals and intentions at a relatively perceptual level of representation, and the mPFC as a module that integrates social information across time and allows reflection and representation of traits and norms, and presumably also of intentionality, at a more abstract cognitive level. Hum Brain Mapp 30:829–858, 2009. VC 2008 Wiley-Liss, Inc.
An overlap invariant entropy measure of 3D medical image alignment

This paper is concerned with the development of entropy-based registration criteria for automated 3D multi-modality medical image alignment. In this application where misalignment can be large with respect to the imaged field of view, invariance to overlap statistics is an important consideration. Current entropy measures are reviewed and a normalised measure is proposed which is simply the ratio of the sum of the marginal entropies and the joint entropy. The effect of changing overlap on current entropy measures and this normalised measure are compared using a simple image model and experiments on clinical image data. Results indicate that the normalised entropy measure provides significantly improved behaviour over a range of imaged fields of view. ( 1999 Pattern Recognition Society.
An architectural pattern language of cloud-based applications

The properties of clouds -- elasticity, pay-per-use, and standardization of the runtime infrastructure -- enable cloud providers and users alike to benefit from economies of scale, faster provisioning times, and reduced runtime costs. However, to achieve these benefits, application architects and developers have to respect the characteristics of the cloud environment.
 To reduce the complexity of cloud application architectures, we propose a pattern-based approach for cloud application design and development. We defined a pattern format to describe the principles of cloud computing, available cloud offerings, and cloud application architectures. Based on this format we developed an architectural pattern language of cloud-based applications: through interrelation of patterns for cloud offering descriptions and cloud application architectures, developers are guided during the identification of cloud environments and architecture patterns applicable to their problems. We cover the proceeding how we identified patterns in various information sources and existing productively used applications, give an overview of previously discovered patterns, and introduce one new pattern. Further, we propose a framework for the organizations of patterns and the guidance of developers during pattern instantiation.
Utility max-min fair resource allocation for communication networks with multipath routing

This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max-min fairness is achieved, which is essential for balancing QoS (Quality of Service) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max-min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.
Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression.

The first brain-wide voxel-level resting state functional connectivity neuroimaging analysis of depression is reported, with 421 patients with major depressive disorder and 488 control subjects. Resting state functional connectivity between different voxels reflects correlations of activity between those voxels and is a fundamental tool in helping to understand the brain regions with altered connectivity and function in depression. One major circuit with altered functional connectivity involved the medial orbitofrontal cortex Brodmann area 13, which is implicated in reward, and which had reduced functional connectivity in depression with memory systems in the parahippocampal gyrus and medial temporal lobe, especially involving the perirhinal cortex Brodmann area 36 and entorhinal cortex Brodmann area 28. The Hamilton Depression Rating Scale scores were correlated with weakened functional connectivity of the medial orbitofrontal cortex Brodmann area 13. Thus in depression there is decreased reward-related and memory system functional connectivity, and this is related to the depressed symptoms. The lateral orbitofrontal cortex Brodmann area 47/12, involved in non-reward and punishing events, did not have this reduced functional connectivity with memory systems. Second, the lateral orbitofrontal cortex Brodmann area 47/12 had increased functional connectivity with the precuneus, the angular gyrus, and the temporal visual cortex Brodmann area 21. This enhanced functional connectivity of the non-reward/punishment system (Brodmann area 47/12) with the precuneus (involved in the sense of self and agency), and the angular gyrus (involved in language) is thus related to the explicit affectively negative sense of the self, and of self-esteem, in depression. A comparison of the functional connectivity in 185 depressed patients not receiving medication and 182 patients receiving medication showed that the functional connectivity of the lateral orbitofrontal cortex Brodmann area 47/12 with these three brain areas was lower in the medicated than the unmedicated patients. This is consistent with the hypothesis that the increased functional connectivity of the lateral orbitofrontal cortex Brodmann area 47/12 is related to depression. Relating the changes in cortical connectivity to our understanding of the functions of different parts of the orbitofrontal cortex in emotion helps to provide new insight into the brain changes related to depression.
An overview of Fog computing and its security issues

Fog computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage and application services to end users. In this article, we elaborate the motivation and advantages of Fog computing and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state of the art of Fog computing and similar work under the same umbrella. Distinguished from other reviewing work of Fog computing, this paper further discloses the security and privacy issues according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of system security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device. In addition, we discuss the authentication and authorization techniques that can be used in Fog computing. An example of authentication techniques is introduced to address the security scenario where the connection between Fog and Cloud is fragile.
A general approach to sizing and power density equations for comparison of electrical machines

Whenever an electrical machine is meant to be fed by a power converter, the design should be approached as a system optimization, more than a simple machine sizing. A great variety of electrical machines is available to accomplish this goal, and the task of comparing the different options can be very difficult. A general purpose sizing equation, easily adjustable for every topology, that could take into account different waveforms and machine characteristics, would be a very desirable tool. In this paper, a general approach is presented to develop and to discuss such an equation. Sample applications of the sizing and power density equations are utilized to compare the induction machine and the doubly salient permanent magnet (DSPM) machine.
Learning Good Regions to Deblur Images

The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image. Recent methods focus on exploiting natural image priors or additional image observations for deblurring, but pay less attention to the influence of image structure on estimating blur kernels. What is the useful image structure and how can one select good regions for deblurring? We formulate the problem of learning good regions for deblurring within the conditional random field framework. To better compare blur kernels, we develop an effective similarity metric for labeling training samples. The learned model is able to predict good regions from an input blurred image for deblurring without user guidance. Qualitative and quantitative evaluations demonstrate that good regions can be selected by the proposed algorithms for effective single image deblurring.
Sample size estimation in diagnostic test studies of biomedical informatics

This review provided a conceptual framework of sample size calculations in the studies of diagnostic test accuracy in various conditions and test outcomes.



The formulae of sample size calculations for estimation of adequate sensitivity/specificity, likelihood ratio and AUC as an overall index of accuracy and also for testing in single modality and comparing two diagnostic tasks have been presented for desired confidence interval.



The required sample sizes were calculated and tabulated with different levels of accuracies and marginal errors with 95% confidence level for estimating and for various effect sizes with 80% power for purpose of testing as well. The results show how sample size is varied with accuracy index and effect size of interest.



This would help the clinicians when designing diagnostic test studies that an adequate sample size is chosen based on statistical principles in order to guarantee the reliability of study.
Fast and extensible building modeling from airborne LiDAR data

This paper presents an automatic algorithm which reconstructs building models from airborne LiDAR (light detection and ranging) data of urban areas. While our algorithm inherits the typical building reconstruction pipeline, several major distinct features are developed to enhance efficiency and robustness: 1) we design a novel vegetation detection algorithm based on differential geometry properties and unbalanced SVM; 2) after roof patch segmentation, a fast boundary extraction method is introduced to produce topology-correct water tight boundaries; 3) instead of making assumptions on the angles between roof boundary lines, we propose a data-driven algorithm which automatically learns the principal directions of roof boundaries and uses them in footprint production. Furthermore, we show the extendability of our algorithm by supporting non-flat object patterns with the help of only a few user interactions. We demonstrate the efficiency and accuracy of our algorithm by showing experiment results on urban area data of several different data sets.
Juvenile Breast Hypertrophy: A Successful Breast Reduction of 14.9% Body Weight without Recurrence in a 5-Year Follow-Up

Juvenile hypertrophy of the breast (JHB) is a rare and relentless disease affecting women in the peripubertal period. We present a 13-year-old girl with massive bilateral JHB, successfully treated with a breast reduction and free nipple graft technique. A total of 7300 grams of breast tissue had been removed, accounting for 14.9% of the patient's total body weight. Prophylactic hormonal therapy was not commenced. During the 5-year follow-up period, there was no recurrence and the patient remains satisfied with the aesthetic outcome. A recent meta-analysis study indicates that subcutaneous mastectomy is associated with reduced risk of recurrence, but it is more deforming and the aesthetic result is inferior to a reduction mammaplasty. In patients treated with the latter technique, some evidence exists suggesting that the use of a free nipple graft is associated with a less frequent risk of recurrence than a pedicle technique. This present case is unique as it demonstrates the clinical course of this patient at a considerably longer follow-up period than most reported studies. We adhered to the limited available evidence and highlight the long-term reliability of breast reduction with free nipple grafting as the first line surgical option in JHB, eliminating the need for repeated surgeries.
Dynamic switching-based reliable flooding in low-duty-cycle wireless sensor networks

Reliable flooding in wireless sensor networks (WSNs) is desirable for a broad range of applications and network operations. However, it is a challenging problem to ensure 100% flooding coverage efficiently considering the combined effects of low-duty-cycle operation and unreliable wireless transmission. In this work, we propose a novel dynamic switching-based reliable flooding (DSRF) framework, which is designed as an enhancement layer to provide efficient and reliable flooding over a variety of existing flooding tree structures in low-duty-cycle WSNs. Through comprehensive simulations, we demonstrate that DSRF can effectively improve both flooding energy efficiency and latency.
The Relationship Between Health and Growth : When Lucas

This paper revisits the relationship between health and growth in light of modern endogenous growth theory. We propose a unified framework that encompasses the growth effects of both the rate of improvement of health and the level of health. Based on cross-country regressions over the period 1960-2000, where we instrument for both variables, we find that a higher initial level and a higher rate of improvement in life expectancy both have a significantly positive impact on per capita GDP growth. Then, restricting attention to OECD countries, we find supportive evidence that only the reduction in mortality below age forty generates productivity gains, which in turn may explain why the positive correlation between health and growth in cross-OECD country regressions appears to have weakened since 1960.
Performance assessment of a smart road management system for the wireless detection of wildlife road-crossing

A wireless distributed system for the detection of the wildlife road-crossing events is addressed to prevent the problem of collisions with the approaching vehicles. The system is based on a wireless sensor network architecture deployed along the road sides in order to alert in real-time the drivers about the dangerous presence of deers within a security area determined by Doppler radar modules integrated in the nodes of the wireless network. The system aims at providing the information for the smart management of road signs through their adaptive activation only in presence of real dangers. The performance of the detection has been assessed during a long-term measurement campaign along a road stretch in Trentino, Italy, where the problem of deer road-crossing significantly impacts the driver safety and security.
Correlation for user confidence in predictive decision making

Despite the recognized value of Machine Learning (ML) techniques and high expectation of applying ML techniques within various applications, significant barriers to the widespread adoption and local implementation of ML approaches still exist in the areas of trust (of ML results), comprehension (of ML processes), as well as confidence (in decision making) by users. This paper investigates the effects of correlation between features and target values on user confidence in data analytics-driven decision making. Our user study found that revealing the correlation between features and target variables affected user confidence in decision making significantly. Moreover, users felt more confident in decision making when correlation shared the same trend with the prediction model performance. These findings would help design intelligent user interfaces and evaluate the effectiveness of machine learning models in applications.