nils-herrmann/clean_scirepeval huggingface dataset:
CryptSubmit: Introducing Securely Timestamped Manuscript Submission and Peer Review Feedback Using the Blockchain

Manuscript submission systems are a central fixture in scholarly publishing. However, with existing systems, researchers must trust that their yet unpublished findings will not prematurely be disseminated due to technical weaknesses and that anonymous peer reviewers or committee members will not plagiarize unpublished content. To address this limitation, we present CryptSubmit - a system that automatically creates a decentralized, tamperproof, and publicly verifiable timestamp for each submitted manuscript by utilizing the blockchain of the cryptocurrency Bitcoin. The publicly accessible and tamperproof infrastructure of the blockchain allows researchers to independently verify the validity of the timestamp associated with their manuscript at the time of submission to a conference or journal. Our system supports researchers in protecting their intellectual property even in the face of vulnerable submission platforms or dishonest peer reviewers. Optionally, the system also generates trusted timestamps for the feedback shared by peer reviewers to increase the traceability of ideas. CryptSubmit integrates these features into the open source conference management system OJS. In the future, the method could be integrated at nearly no overhead cost into other manuscript submission systems, such as EasyChair, ConfTool, or Ambra. The introduced method can also improve electronic pre-print services and storage systems for research data.
Facial Artery Musculomucosal (FAMM) flap for nasal lining in reconstruction of large full thickness lateral nasal defects

Obviously, restoring the nasal lining is a great challenge in the reconstruction of nasal defects. Full thickness nasal defects usually require special flaps for reconstructing the nasal lining. Intranasal mucosal flaps, hinge over flaps, perinasal second flaps, folded or second forehead flaps and finally free flaps are examples that can be used for this purpose. Moreover, the case presented in this article expresses a new role for the superiorly based Facial Artery Musculomucosal (FAMM) flap in this topic. Furthermore, mucosal island variant of this flap is presented to reduce the tension on this flap while restoring the nasal lining in large full thickness nasal defect.
A multifunctional AAV–CRISPR–Cas9 and its host response

CRISPR–Cas9 delivery by adeno-associated virus (AAV) holds promise for gene therapy but faces critical barriers on account of its potential immunogenicity and limited payload capacity. Here, we demonstrate genome engineering in postnatal mice using AAV–split-Cas9, a multifunctional platform customizable for genome editing, transcriptional regulation, and other previously impracticable applications of AAV–CRISPR–Cas9. We identify crucial parameters that impact efficacy and clinical translation of our platform, including viral biodistribution, editing efficiencies in various organs, antigenicity, immunological reactions, and physiological outcomes. These results reveal that AAV–CRISPR–Cas9 evokes host responses with distinct cellular and molecular signatures, but unlike alternative delivery methods, does not induce extensive cellular damage in vivo. Our study provides a foundation for developing effective genome therapeutics.
Sexual assault of women.

Sexual violence affects up to one third of women during their lifetime. Sexual assault is underreported, and more than one half of assaults are committed by someone known to the survivor. Although both men and women can be sexually assaulted, women are at greatest risk. Some groups are more vulnerable, including adolescents; survivors of childhood sexual or physical abuse; persons who are disabled; persons with substance abuse problems; sex workers; persons who are poor or homeless; and persons living in prisons, institutions, or areas of military conflict. Family physicians care for sexual assault survivors immediately and years after the assault. Immediate care includes the treatment of injuries, prophylaxis for sexually transmitted infections, administration of emergency contraception to prevent pregnancy, and the sensitive management of psychological issues. Family physicians should collect evidence for a "rape kit" only if they are experienced in treating persons who have been sexually assaulted because of the legal ramifications of improper collection and storage of evidence. Sexual assault may result in long-term mental and physical health problems. Presentations to the family physician may include self-destructive behaviors, chronic pelvic pain, and difficulty with pelvic examinations. Prevention of sexual assault is societal and should focus on public health education. Safety and support programs have been shown to reduce sexual assaults.
A Fundamental Storage-Communication Tradeoff in Distributed Computing with Straggling Nodes

The optimal storage-computation tradeoff is characterized for a MapReduce-like distributed computing system with straggling nodes, where only a part of the nodes can be utilized to compute the desired output functions. The result holds for arbitrary output functions and thus generalizes previous results that restricted to linear functions. Specifically, in this work, we propose a new information-theoretical converse and a new matching coded computing scheme, that we call coded computing for straggling systems (CCS).
Maxout Networks

We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout’s fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR100, and SVHN.
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines

Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy. These computational models have seemingly insatiable appetites for computational resources not only while training, but also when deployed at scales ranging from data centers all the way down to embedded devices. As such, increasing consideration is being made to maximize the computational efficiency given limited hardware and energy resources and, as a result, inferencing with reduced precision has emerged as a viable alternative to the IEEE 754 Standard for Floating-Point Arithmetic. We propose a quantization scheme that allows inferencing to be carried out using arithmetic that is fundamentally more efficient when compared to even half-precision floating-point. Our quantization procedure is significant in that we determine our quantization scheme parameters by calibrating against its reference floating-point model using a single inference batch rather than (re)training and achieve end-to-end post quantization accuracies comparable to the reference model.
A versatile FMCW Radar System Simulator for Millimeter-Wave Applications

For the successful design of low-cost and high-performance radar systems accurate and efficient system simulation is a key requirement. In this paper we present a new versatile simulation environment for frequency-modulated continuous-wave radar systems. Besides common hardware simulation it covers integrated system simulation and concept analysis from signal synthesis to baseband. It includes a flexible scenario generator, accurate noise modeling, and efficiently delivers simulation data for development and testing of signal processing algorithms. A comparison of simulations and measurement results for an integrated 77-GHz radar prototype shows the capabilities of the simulator on two different scenarios.
OptoCOMM: Introducing a new optical underwater wireless communication modem

OptoCOMM aims at demonstrating the potential, at physical level, of a communication facility for the SUNRISE platform constituted by an Optical Underwater Wireless Communication (OUWC) module with target performance of 10 Mb/s transmission rate at 10 meters range in shallow medium/high turbidity harbour waters. The module, which is based on blue Light Emitting Diode (LED) units and common photodiodes, is an evolution of the proof-of-principle prototype already proven in laboratory (pool). It will constitute an additional node integrated in the Littoral Ocean Observatory Network (LOON) test-bed of the SUNRISE infrastructures, providing a high speed and short-range communication node, which will complete the capability of acoustic modems already present in the test-bed. Three modules (nodes) will be developed and experimentally demonstrated: one for direct integration with the LOON infrastructures, one, battery powered, to be potentially installed on buoys, Remotely Operated Vehicles (ROVs), etc., and one to be installed on the eFolaga Autonomous Underwater Vehicle (AUV) of the proponents. The paper describes in detail the development of the modems as well as the first lab experiments, where the core technology has been successfully tested.
On the importance of initialization and momentum in deep learning

Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned. Our success training these models suggests that previous attempts to train deep and recurrent neural networks from random initializations have likely failed due to poor initialization schemes. Furthermore, carefully tuned momentum methods su ce for dealing with the curvature issues in deep and recurrent network training objectives without the need for sophisticated second-order methods.
A Neural Network-Powered Cognitive Method of Identifying Semantic Entities in Earth Science Papers

In the current era of knowledge explosion, it is becoming increasingly critical to help researchers quickly grasp the core ideas and methods used in the sea of published articles. As a first step toward the aim, this paper proposes a novel approach that simulates the cognitive process of how human beings read Earth science articles, and automatically identifies semantic entities from the articles. Among others, one major objective is to identify the datasets studied in articles. Oftentimes, however, researchers do not explicitly cite the datasets used. Thus, we propose a profile-matching method strengthened by a neural network-based method to identify implicitly cited dataset entities based on the context. Our experiments have demonstrated the effectiveness of our approaches.
Evaluation of Objective Quality Measures for Speech Enhancement

In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based, and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the quality of enhanced speech along three dimensions: signal distortion, noise distortion, and overall quality. This paper reports on the evaluation of correlations of several objective measures with these three subjective rating scales. Several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.
Query optimization using column statistics in hive

Hive is a data warehousing solution on top of the Hadoop MapReduce framework that has been designed to handle large amounts of data and store them in tables like a relational database management system or a conventional data warehouse while using the parallelization and batch processing functionalities of the Hadoop MapReduce framework to speed up the execution of queries. Data inserted into Hive is stored in the Hadoop FileSystem (HDFS), which is part of the Hadoop MapReduce framework. To make the data accessible to the user, Hive uses a query language similar to SQL, which is called HiveQL. When a query is issued in HiveQL, it is translated by a parser into a query execution plan that is optimized and then turned into a series of map and reduce iterations. These iterations are then executed on the data stored in the HDFS, writing the output to a file.
 The goal of this work is to to develop an approach for improving the performance of the HiveQL queries executed in the Hive framework. For that purpose, we introduce an extension to the Hive MetaStore which stores metadata that has been extracted on the column level of the user database. These column level statistics are then used for example in combination with join ordering algorithms which are adapted to the specific needs of the Hadoop MapReduce environment to improve the overall performance of the HiveQL query execution.
Visualizing and Understanding Neural Models in NLP

While neural networks have been successfully applied to many NLP tasks the resulting vector-based models are very difficult to interpret. For example it’s not clear how they achieve compositionality, building sentence meaning from the meanings of words and phrases. In this paper we describe strategies for visualizing compositionality in neural models for NLP, inspired by similar work in computer vision. We first plot unit values to visualize compositionality of negation, intensification, and concessive clauses, allowing us to see wellknown markedness asymmetries in negation. We then introduce methods for visualizing a unit’s salience, the amount that it contributes to the final composed meaning from first-order derivatives. Our generalpurpose methods may have wide applications for understanding compositionality and other semantic properties of deep networks.
Political polarization in social media: Analysis of the “Twitter political field” in Japan

There is an ongoing debate about whether the Internet is like a public sphere or an echo chamber. Among many forms of social media, Twitter is one of the most crucial online places for political debate. Most of the previous studies focus on the formal structure of the Twitter political field, such as its homophilic tendency, or otherwise limit the analysis to a few topics. In order to explore whether Twitter functions as an echo chamber in general, however, we have to investigate not only the structure but also the contents of Twitter's political field. Accordingly, we conducted both large-scale social network analysis and natural language processing. We firstly applied a community detection method to the reciprocal following network in Twitter and found five politically distinct communities in the field. We further examined dominant topics discussed therein by employing a topic model in analyzing the content of the tweets, and we found that a topic related to xenophobia is circulated solely in right-wing communities. To our knowledge, this is the first study to address echo chambers in Japanese Twitter political field and to examine the formal structure and the contents of tweets with the combination of large-scale social network analysis and natural language processing.
Hierarchical Grouping to Optimize an Objective Function

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.
Construction of extended technology acceptance model of electronic medical records in Jordan: The influence of doctors’ self-efficacy andperceived behavioral control

The implementation of Electronic Medical Record (EMR) in Jordan public hospitals has started since 2009. The motivation of doing this study is to observe doctors’ acceptance of EMR in Jordanian public hospitals. This study aims at constructing doctors’ acceptance model of EMR using Technology Acceptance Model (TAM) based on individual capabilities. The model will apply TAM as the basic theory and extending Self-Efficacy (SE) and Perceived Behavioral Control (PBC) as two factors of individual characteristics. Initial findings show that the main factors of the model should compose of Perceived Usefulness, Perceived Ease of Use, Behavioral Intention to Use, Self-Efficacy, and Perceived Behavioral Control. The result of the pilot test shows that all factors are reliable with Cronbach Alpa 0.838. The recommendation to the top management of healthcare organization, in order to increase doctors’ acceptance of EMR, that the new deployment of EMR should focus on delivering awareness and continuous training on using EMR among doctors at the early stage of EMR implementation .
Brain structures differ between musicians and non-musician

From an early age, musicians learn complex motor and auditory skills (e.g., the translation of visually perceived musical symbols into motor commands with simultaneous auditory monitoring of output), which they practice extensively from childhood throughout their entire careers. Using a voxel-by-voxel morphometric technique, we found gray matter volume differences in motor, auditory, and visual-spatial brain regions when comparing professional musicians (keyboard players) with a matched group of amateur musicians and non-musicians. Although some of these multiregional differences could be attributable to innate predisposition, we believe they may represent structural adaptations in response to long-term skill acquisition and the repetitive rehearsal of those skills. This hypothesis is supported by the strong association we found between structural differences, musician status, and practice intensity, as well as the wealth of supporting animal data showing structural changes in response to long-term motor training. However, only future experiments can determine the relative contribution of predisposition and practice.
Analysis of cyber security for industrial control systems

Industrial control systems (ICS) are specialized information systems that differs significantly form traditional information systems used in the IT world. The main use of ICS is to manage critical infrastructures such as, Oil and Natural Gas facilities, nuclear plants, smart grids, water and waste water ... etc. ICS have many unique functional characteristics, including a need for real-time response and extremely high availability, predictability, reliability, as well as distributed intelligence. Which for, many advanced computing, communication and internet technologies were integrated to the ICS to cover more costumers requirements such as mobility, data analytics, extensibility ... etc The integration of these technologies makes from the ICS open systems to the external world; this openness exposes the critical infrastructures to several Cyber security critical issues. Nowadays, cyber security emerges to be one of the most critical issues because of the immediate impact and the high cost of cyber-attacks. In this paper, we present a comprehensive analysis of cyber security issues for ICS. Specifically we focus on discussing and reviewing the different types and architectures of an ICS, security requirements, different threats attacks, and existing solutions to secure Industrial control systems. By this survey, we desire to provide a clear understanding of security issues in ICS and clarify the different research issues to solve in the future.
Comparison and combination of iris matchers for reliable personal authentication

The personal identification approaches using iris images are receiving increasing attention in the biometrics literature. Several methods have been presented in the literature and those based on the phase encoding of texture information are suggested to be the most promising. However, there has not been any attempt to combine these approaches to achieve further improvement in the performance. This paper presents a comparative study of the performance from the iris authentication using Log-Gabor, Haar wavelet, DCT and FFT based features. Our experimental results suggest that the performance from the Haar wavelet and Log-Gabor filter based phase encoding is the most promising among all the four approaches considered in this work. Therefore the combination of these two matchers is most promising, both in terms of performance and the computational complexity. Our experimental results from the all 411 users (CASIA v3) and 224 users (IITD v1) database illustrate significant improvement in the performance which is not possible with either of these approaches individually.
Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition

Fine-grained car recognition aims to recognize the category information of a car, such as car make, car model, or even the year of manufacture. A number of recent studies have shown that a deep convolutional neural network (DCNN) trained on a large-scale data set can achieve impressive results at a range of generic object classification tasks. In this paper, we propose a spatially weighted pooling (SWP) strategy, which considerably improves the robustness and effectiveness of the feature representation of most dominant DCNNs. More specifically, the SWP is a novel pooling layer, which contains a predefined number of spatially weighted masks or pooling channels. The SWP pools the extracted features of DCNNs with the guidance of its learnt masks, which measures the importance of the spatial units in terms of discriminative power. As the existing methods that apply uniform grid pooling on the convolutional feature maps of DCNNs, the proposed method can extract the convolutional features and generate the pooling channels from a single DCNN. Thus minimal modification is needed in terms of implementation. Moreover, the parameters of the SWP layer can be learned in the end-to-end training process of the DCNN. By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature. We advance the state-of-the-art results by improving the accuracy from 92.6% to 93.1% on the Stanford Cars-196 data set and 91.2% to 97.6% on the recent CompCars data set. We have also tested the proposed method on two additional large-scale data sets with impressive results observed.