nils-herrmann/clean_scirepeval huggingface dataset:
Big Data Methods for Computational Linguistics

Many tasks in computational linguistics traditionally rely on hand-crafted or curated resources like thesauri or word-sense-annotated corpora. The availability of big data, from the Web and other sources, has changed this situation. Harnessing these assets requires scalable methods for data and text analytics. This paper gives an overview on our recent work that utilizes big data methods for enhancing semantics-centric tasks dealing with natural language texts. We demonstrate a virtuous cycle in harvesting knowledge from large data and text collections and leveraging this knowledge in order to improve the annotation and interpretation of language in Web pages and social media. Specifically, we show how to build large dictionaries of names and paraphrases for entities and relations, and how these help to disambiguate entity mentions in texts.
Learning to diagnose from scratch by exploiting dependencies among labels

The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures. Many tasks in radiology, for example, are largely problems of multi-label classification wherein medical images are interpreted to indicate multiple present or suspected pathologies. Clinical settings drive the necessity for high accuracy simultaneously across a multitude of pathological outcomes and greatly limit the utility of tools which consider only a subset. This issue is exacerbated by a general scarcity of training data and maximizes the need to extract clinically relevant features from available samples – ideally without the use of pre-trained models which may carry forward undesirable biases from tangentially related tasks. We present and evaluate a partial solution to these constraints in using LSTMs to leverage interdependencies among target labels in predicting 14 pathologic patterns from chest x-rays and establish state of the art results on the largest publicly available chest x-ray dataset from the NIH without pre-training. Furthermore, we propose and discuss alternative evaluation metrics and their relevance in clinical practice.
Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities

Polarity classification of opinionated sentences with both positive and negative sentiments1 is a key challenge in sentiment analysis. This paper presents a novel unsupervised method for discovering intra-sentence level discourse relations for eliminating polarity ambiguities. Firstly, a discourse scheme with discourse constraints on polarity was defined empirically based on Rhetorical Structure Theory (RST). Then, a small set of cuephrase-based patterns were utilized to collect a large number of discourse instances which were later converted to semantic sequential representations (SSRs). Finally, an unsupervised method was adopted to generate, weigh and filter new SSRs without cue phrases for recognizing discourse relations. Experimental results showed that the proposed methods not only effectively recognized the defined discourse relations but also achieved significant improvement by integrating discourse information in sentence-level polarity classification.
Photoplethysmogram intensity ratio: A potential indicator for improving the accuracy of PTT-based cuffless blood pressure estimation

The most commonly used method for cuffless blood pressure (BP) measurement is using pulse transit time (PTT), which is based on Moens-Korteweg (M-K) equation underlying the assumption that arterial geometries such as the arterial diameter keep unchanged. However, the arterial diameter is dynamic which varies over the cardiac cycle, and it is regulated through the contraction or relaxation of the vascular smooth muscle innervated primarily by the sympathetic nervous system. This may be one of the main reasons that impair the BP estimation accuracy. In this paper, we propose a novel indicator, the photoplethysmogram (PPG) intensity ratio (PIR), to evaluate the arterial diameter change. The deep breathing (DB) maneuver and Valsalva maneuver (VM) were performed on five healthy subjects for assessing parasympathetic and sympathetic nervous activities, respectively. Heart rate (HR), PTT, PIR and BP were measured from the simultaneously recorded electrocardiogram (ECG), PPG, and continuous BP. It was found that PIR increased significantly from inspiration to expiration during DB, whilst BP dipped correspondingly. Nevertheless, PIR changed positively with BP during VM. In addition, the spectral analysis revealed that the dominant frequency component of PIR, HR and SBP, shifted significantly from high frequency (HF) to low frequency (LF), but not obvious in that of PTT. These results demonstrated that PIR can be potentially used to evaluate the smooth muscle tone which modulates arterial BP in the LF range. The PTT-based BP measurement that take into account the PIR could therefore improve its estimation accuracy.
Connected Car: Quantified Self becomes Quantified Car

The automotive industry could be facing a situation of profound change and opportunity in the coming decades. There are a number of influencing factors such as increasing urban and aging populations, self-driving cars, 3D parts printing, energy innovation, and new models of transportation service delivery (Zipcar, Uber). The connected car means that vehicles are now part of the connected world, continuously Internet-connected, generating and transmitting data, which on the one hand can be helpfully integrated into applications, like real-time traffic alerts broadcast to smartwatches, but also raises security and privacy concerns. This paper explores the automotive connected world, and describes five killer QS (Quantified Self)-auto sensor applications that link quantified-self sensors (sensors that measure the personal biometrics of individuals like heart rate) and automotive sensors (sensors that measure driver and passenger biometrics or quantitative automotive performance metrics like speed and braking activity). The applications are fatigue detection, real-time assistance for parking and accidents, anger management and stress reduction, keyless authentication and digital identity verification, and DIY diagnostics. These kinds of applications help to demonstrate the benefit of connected world data streams in the automotive industry and beyond where, more fundamentally for human progress, the automation of both physical and now cognitive tasks is underway.
A High-Linearity, 17 ps Precision Time-to-Digital Converter Based on a Single-Stage Vernier Delay Loop Fine Interpolation

This paper presents a time-to-digital converter (TDC) architecture capable of reaching high-precision and high-linearity with moderate area occupation per measurement channel. The architecture is based on a coarse counter and a couple of two-stage interpolators that exploit the cyclic sliding scale technique in order to improve the conversion linearity. The interpolators are based on a new coarse-fine synchronization circuit and a new single-stage Vernier delay loop fine interpolation. In a standard cost-effective 0.35 μm CMOS technology the TDC reaches a dynamic range of 160 ns, 17.2 ps precision and differential non-linearity better than 0.9% LSB rms. The TDC building block was designed in order to be easily assembled in a multi-channel monolithic TDC chip. Coupled with a SPAD photodetector it is aimed for TCSPC applications (like FLIM, FCS, FRET) and direct ToF 3-D ranging.
Supervision via competition: Robot adversaries for learning tasks

There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots. For supplementary video see: youtu.be/QfK3Bqhc6Sk
Combining LSTM and Latent Topic Modeling for Mortality Prediction

There is a great need for technologies that can predict the mortality of patients in intensive care units with both high accuracy and accountability. We present joint end-to-end neural network architectures that combine long short-term memory (LSTM) and a latent topic model to simultaneously train a classifier for mortality prediction and learn latent topics indicative of mortality from textual clinical notes. For topic interpretability, the topic modeling layer has been carefully designed as a single-layer network with constraints inspired by LDA. Experiments on the MIMIC-III dataset show that our models significantly outperform prior models that are based on LDA topics in mortality prediction. However, we achieve limited success with our method for interpreting topics from the trained models by looking at the neural network weights.
Path following control for autonomous formula racecar: Autonomous formula student competition

This paper summarizes our work of an envelope path following controller for a four-wheel independently actuated (FWIA) autonomous racecar. The controller consists of three modules through the integrated control of active front-wheel steer (AFS) and direct yaw-moment control (DYC). An electric autonomous Formula Student racecar is used as testbed to test the controller Moreover,  With the cooperation with SAE China, the authors are trying to establish the autonomous Formula Student racecar competition in China. In this paper, the basic concept and rules of autonomous Formula Student racecar competition is described.
A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both singleand multichannel dereverberation techniques and automatic speech recognition (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single-channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation, based on both real, single-channel, andmultichannel recordings. This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processing.
Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering

A range of Natural Language Processing tasks involve making judgments about the semantic relatedness of a pair of sentences, such as Recognizing Textual Entailment (RTE) and answer selection for Question Answering (QA). A key challenge that these tasks face in common is the lack of explicit alignment annotation between a sentence pair. We capture the alignment by using a novel probabilistic model that models tree-edit operations on dependency parse trees. Unlike previous tree-edit models which require a separate alignment-finding phase and resort to ad-hoc distance metrics, our method treats alignments as structured latent variables, and offers a principled framework for incorporating complex linguistic features. We demonstrate the robustness of our model by conducting experiments for RTE and QA, and show that our model performs competitively on both tasks with the same set of general features.
Modelling IT projects success with Fuzzy Cognitive Maps

IT projects have certain features that make them different from other engineering projects. These include increased complexity and higher chances of project failure. To increase the chances of an IT project to be perceived as successful by all the parties involved in the project from its conception, development and implementation, it is necessary to identify at the outset of the project what the important factors influencing that success are. Current methodologies and tools used for identifying, classifying and evaluating the indicators of success in IT projects have several limitations that can be overcome by employing the new methodology presented in this paper. This methodology is based on using Fuzzy Cognitive Maps (FCM) for mapping success, modelling Critical Success Factors (CSFs) perceptions and the relations between them. This is an area where FCM has never been applied before. The applicability of the FCM methodology is demonstrated through a case study based on a new project idea, the Mobile Payment System (MPS) Project, related to the fast evolving world of mobile telecommunications.
Evulvalution: the portrayal of women's external genitalia and physique across time and the current barbie doll ideals.

Media images of the female body commonly represent reigning appearance ideals of the era in which they are published. To date, limited documentation of the genital appearance ideals in mainstream media exists. Analysis 1 sought to describe genital appearance ideals (i.e., mons pubis and labia majora visibility, labia minora size and color, and pubic hair style) and general physique ideals (i.e., hip, waist, and bust size, height, weight, and body mass index [BMI]) across time based on 647 Playboy Magazine centerfolds published between 1953 and 2007. Analysis 2 focused exclusively on the genital appearance ideals embodied by models in 185 Playboy photographs published between 2007 and 2008. Taken together, results suggest the perpetuation of a "Barbie Doll" ideal characterized by a low BMI, narrow hips, a prominent bust, and hairless, undefined genitalia resembling those of a prepubescent female.
Smart grid neighborhood area networks: a survey

Smart grid is an intelligent power network featured by its two-way flows of electricity and information. With an integrated communication infrastructure, smart grid manages the operation of all connected components to provide reliable and sustainable electricity supplies. Many advanced communication technologies have been identified for their applications in different domains of smart grid networks. This paper focuses on wireless communication networking technologies for smart grid neighborhood area networks (NANs). In particular, we aim to offer a comprehensive survey to address various important issues on implementation of smart grid NANs, including network topology, gateway deployment, routing algorithms, and security. We will identify four major challenges for the implementation of NANs, including timeliness management, security assurance, compatibility design, and cognitive spectrum access, based on which the future research directions are suggested.
Mobility and Handover Management for Heterogeneous Networks in LTE-Advanced

3GPP Long Term Evolution-Advanced (LTE-A) aims at enhancement of LTE performance in many respects including the system capacity and network coverage. This enhancement can be accomplished by heterogeneous networks (HetNets) where additional micro-nodes that require lower transmission power are efficiently deployed. More careful management of mobility and handover (HO) might be required in HetNets compared to homogeneous networks where all nodes require the same transmission power. In this article, we provide a technical overview of mobility and HO management for HetNets in LTEA. Moreover, we investigate the A3-event which requires a certain criterion to be met for HO. The criterion involves the reference symbol received power/quality of user equipment (UE), hysteresis margin, and a number of offset parameters based on proper HO timing, i.e., time-to-trigger (TTT). Optimum setting of these parameters are not trivial task, and has to be determined depending on UE speed, propagation environment, system load, deployed HetNets configuration, etc. Therefore, adaptive TTT values with given hysteresis margin for the lowest ping pong rate within 2 % of radio link failure rate depending on UE speed and deployed HetNets configuration are investigated in this article.
Cole electrical impedance Model-a critique and an alternative

The Cole single-dispersion impedance model is based upon a constant phase element (CPE), a conductance parameter as a dependent parameter and a characteristic time constant as an independent parameter. Usually however, the time constant of tissue or cell suspensions is conductance dependent, and so the Cole model is incompatible with general relaxation theory and not a model of first choice. An alternative model with conductance as a free parameter influencing the characteristic time constant of the biomaterial has been analyzed. With this free-conductance model it is possible to separately follow CPE and conductive processes, and the nominal time constant no longer corresponds to the apex of the circular arc in the complex plane.
Wavelet Transform-Based Analysis of QRS complex in ECG Signals

In the present paper we have reported a wavelet based time-frequency multiresolution analysis of an ECG signal. The ECG (electrocardiogram), which records hearts electrical activity, is able to provide with useful information about the type of Cardiac disorders suffered by the patient depending upon the deviations from normal ECG signal pattern. We have plotted the coefficients of continuous wavelet transform using Morlet wavelet. We used different ECG signal available at MIT-BIH database and performed a comparative study. We demonstrated that the coefficient at a particular scale represents the presence of QRS signal very efficiently irrespective of the type or intensity of noise, presence of unusually high amplitude of peaks other than QRS peaks and Base line drift errors. We believe that the current studies can enlighten the path towards development of very lucid and time efficient algorithms for identifying and representing the QRS complexes that can be done with normal computers and processors. KeywordsECG signal, Continuous Wavelet Transform, Morlet Wavelet, Scalogram, QRS Detector.
Agricultural Plant Leaf Disease Detection and Diagnosis Using Image Processing Based on Morphological Feature Extraction

Leaf spots can be indicative of crop diseases, where leaf batches (spots) are usually examined and subjected to expert opinion. In our proposed system, we are going to develop an integrated image processing system to help automated inspection of these leaf batches and helps identify the disease type. Conventional Expert systems mainly those which used to diagnose the disease in agriculture domain depends only on textual input. Usually abnormalities for a given crop are manifested as symptoms on various plant parts. To enable an expert system to produce correct results, end user must be capable of mapping what they see in a form of abnormal symptoms to answer to questions asked by that expert system. This mapping may be inconsistent if a full understanding of the abnormalities does not exist. The proposed system consists of four stages; the first is the enhancement, which includes HIS transformation, histogram analysis, and intensity adjustment. The second stage is segmentation, which includes adaptation of fuzzy c-means algorithm. Feature extraction is the third stage, which deals with three features, namely color size and shape of spot. The fourth stage is classification, which comprises back propagation based neural networks.
Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks

Semantic labeling (or pixel-level land-cover classification) in ultrahigh-resolution imagery (<10 cm) requires statistical models able to learn high-level concepts from spatial data, with large appearance variations. Convolutional neural networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction. In this paper, we present a CNN-based system relying on a downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them back to the original resolution by deconvolutions. By doing so, the CNN learns to densely label every pixel at the original resolution of the image. This results in many advantages, including: 1) the state-of-the-art numerical accuracy; 2) the improved geometric accuracy of predictions; and 3) high efficiency at inference time. We test the proposed system on the Vaihingen and Potsdam subdecimeter resolution data sets, involving the semantic labeling of aerial images of 9- and 5-cm resolution, respectively. These data sets are composed by many large and fully annotated tiles, allowing an unbiased evaluation of models making use of spatial information. We do so by comparing two standard CNN architectures with the proposed one: standard patch classification, prediction of local label patches by employing only convolutions, and full patch labeling by employing deconvolutions. All the systems compare favorably or outperform a state-of-the-art baseline relying on superpixels and powerful appearance descriptors. The proposed full patch labeling CNN outperforms these models by a large margin, also showing a very appealing inference time.