nils-herrmann/clean_scirepeval huggingface dataset:
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

The loss functions of deep neural networks are complex and their geometric properties are not well understood. We show that the optima of these complex loss functions are in fact connected by simple curves over which training and test accuracy are nearly constant. We introduce a training procedure to discover these high-accuracy pathways between modes. Inspired by this new geometric insight, we also propose a new ensembling method entitled Fast Geometric Ensembling (FGE). Using FGE we can train high-performing ensembles in the time required to train a single model. We achieve improved performance compared to the recent state-of-the-art Snapshot Ensembles, on CIFAR-10, CIFAR-100, and ImageNet.
Joint Event Extraction via Recurrent Neural Networks

Event extraction is a particularly challenging problem in information extraction. The stateof-the-art models for this problem have either applied convolutional neural networks in a pipelined framework (Chen et al., 2015) or followed the joint architecture via structured prediction with rich local and global features (Li et al., 2013). The former is able to learn hidden feature representations automatically from data based on the continuous and generalized representations of words. The latter, on the other hand, is capable of mitigating the error propagation problem of the pipelined approach and exploiting the inter-dependencies between event triggers and argument roles via discrete structures. In this work, we propose to do event extraction in a joint framework with bidirectional recurrent neural networks, thereby benefiting from the advantages of the two models as well as addressing issues inherent in the existing approaches. We systematically investigate different memory features for the joint model and demonstrate that the proposed model achieves the state-of-the-art performance on the ACE 2005 dataset.
EXTENDED FINITE ELEMENT METHOD ( XFEM ) APPLIED TO AIRCRAFT DURALUMIN SPAR FATIGUE LIFE ESTIMATION

Original scientific paper In the present work, duralumin aircraft spar fatigue life is evaluated by extended finite element method (XFEM) under cyclic loading condition. The effect of the crack growth on the fatigue life of aircraft spar is discussed in detail. The values of stress intensity factors (SIFs) are extracted from the XFEM solution. Standard Paris fatigue crack growth law (currently, the only one incorporated in Abaqus) is used for the fatigue life estimation. Obtained results are compared with previously obtained experimental results.
Fog Computing Over IoT: A Secure Deployment and Formal Verification

Fog computing, being an extension to cloud computing has addressed some issues found in cloud computing by providing additional features, such as location awareness, low latency, mobility support, and so on. Its unique features have also opened a way toward security challenges, which need to be focused for making it bug-free for the users. This paper is basically focusing on overcoming the security issues encountered during the data outsourcing from fog client to fog node. We have added Shibboleth also known as security and cross domain access control protocol between fog client and fog node for improved and secure communication between the fog client and fog node. Furthermore to prove whether Shibboleth meets the security requirement needed to provide the secure outsourcing. We have also formally verified the protocol against basic security properties using high level Petri net.
Distributionally Robust Logistic Regression

This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples. If the radius of this ball is chosen judiciously, we can guarantee that it contains the unknown datagenerating distribution with high confidence. We then formulate a distributionally robust logistic regression model that minimizes a worst-case expected logloss function, where the worst case is taken over all distributions in the Wasserstein ball. We prove that this optimization problem admits a tractable reformulation and encapsulates the classical as well as the popular regularized logistic regression problems as special cases. We further propose a distributionally robust approach based on Wasserstein balls to compute upper and lower confidence bounds on the misclassification probability of the resulting classifier. These bounds are given by the optimal values of two highly tractable linear programs. We validate our theoretical out-of-sample guarantees through simulated and empirical experiments.
Monitoring and modelling of urban sprawl using remote sensing and GIS techniques

The concentration of people in densely populated urban areas, especially in developing countries, calls for the use of monitoring systems like remote sensing. Such systems along with spatial analysis techniques like digital image processing and geographical information system (GIS) can be used for the monitoring and planning purposes as these enable the reporting of overall sprawl at a detailed level. In the present work, urban sprawl of the Ajmer city (situated in Rajasthan State of India) has been studied at a mid scale level, over a period of 25 years (1977–2002), to extract the information related to sprawl, area of impervious surfaces and their spatial and temporal variability. Statistical classification approaches have been used for the classification of the remotely sensed images obtained from various sensors viz. Landsat MSS, TM, ETM+ and IRS LISS-III. Urban sprawl and its spatial and temporal characteristics have been derived from the classified satellite images. The Shannon’s entropy and landscape metrics (patchiness and map density) have been computed in terms of spatial phenomenon, in order to quantify the urban form (impervious area). Further, multivariate statistical techniques have been used to establish the relationship between the urban sprawl and its causative factors. Results reveal that land development (160.8%) in Ajmer is more than three times the population growth (50.1%). Shannon’s entropy and landscape metrics has revealed the spatial distribution of the urban sprawl over a period of last 25 years.  (2008) 26–43
A review of natural language processing techniques for opinion mining systems

As the prevalence of social media on the Internet, opinion mining has become an essential approach to analyzing so many data. Various applications appear in a wide range of industrial domains. Meanwhile, opinions have diverse expressions which bring along research challenges. Both of the practical demands and research challenges make opinion mining an active research area in recent years. In this paper, we present a review of Natural Language Processing (NLP) techniques for opinion mining. First, we introduce general NLP techniques which are required for text preprocessing. Second, we investigate the approaches of opinion mining for different levels and situations. Then we introduce comparative opinion mining and deep learning approaches for opinion mining. Opinion summarization and advanced topics are introduced later. Finally, we discuss some challenges and open problems related to opinion mining.
Ethereum: State of Knowledge and Research Perspectives

Ethereum is a major blockchain-based platform for smart contracts – Turing complete programs that are executed in a decentralized network and usually manipulate digital units of value. A peer-to-peer network of mutually distrusting nodes maintains a common view of the global state and executes code upon request. The stated is stored in a blockchain secured by a proof-of-work consensus mechanism similar to that in Bitcoin. The core value proposition of Ethereum is a full-featured programming language suitable for implementing complex business logic. Decentralized applications without a trusted third party are appealing in areas like crowdfunding, financial services, identity management, and gambling. Smart contracts are a challenging research topic that spans over areas ranging from cryptography, consensus algorithms, and programming languages to governance, finance, and law. This paper summarizes the state of knowledge in this field. We provide a technical overview of Ethereum, outline open challenges, and review proposed solutions. We also mention alternative smart contract blockchains.
Improved synthesis of graphene oxide.

An improved method for the preparation of graphene oxide (GO) is described. Currently, Hummers' method (KMnO(4), NaNO(3), H(2)SO(4)) is the most common method used for preparing graphene oxide. We have found that excluding the NaNO(3), increasing the amount of KMnO(4), and performing the reaction in a 9:1 mixture of H(2)SO(4)/H(3)PO(4) improves the efficiency of the oxidation process. This improved method provides a greater amount of hydrophilic oxidized graphene material as compared to Hummers' method or Hummers' method with additional KMnO(4). Moreover, even though the GO produced by our method is more oxidized than that prepared by Hummers' method, when both are reduced in the same chamber with hydrazine, chemically converted graphene (CCG) produced from this new method is equivalent in its electrical conductivity. In contrast to Hummers' method, the new method does not generate toxic gas and the temperature is easily controlled. This improved synthesis of GO may be important for large-scale production of GO as well as the construction of devices composed of the subsequent CCG.
Systematic design of unitary space-time constellations

We propose a systematic method for creating constellations of unitary space-time signals for multipleantenna communication links. Unitary space-time signals, which are orthonormal in time across the antennas, have been shown to be well-tailored to a Rayleigh fading channel where neither the transmitter nor the receiver knows the fading coefficients. The signals can achieve low probability of error by exploiting multiple-antenna diversity. Because the fading coefficients are not known, the criterion for creating and evaluating the constellation is nonstandard and differs markedly from the familiar maximum-Euclidean-distance norm. Our construction begins with the first signal in the constellation—an oblong complex-valued matrix whose columns are orthonormal—and systematically produces the remaining signals by successively rotating this signal in a high-dimensional complex space. This construction easily produces large constellations of high-dimensional signals. We demonstrate its efficacy through examples involving one, two, and three transmitter antennas. Index Terms —Multi-element antenna arrays, wireless communications, fading channels, transmit diversity, receive diversity, Unitary Space-Time Modulation
An image processing system for driver assistance

Systems for automated image analysis are useful for a variety of tasks and their importance is still growing due to technological advances and an increase of social acceptance. Especially in the field of driver assistance systems the progress in science has reached a level of high performance. Fully or partly autonomously guided vehicles, particularly for road-based traffic, pose high demands on the development of reliable algorithms due to the conditions imposed by natural environments. At the Institut für Neuroinformatik methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We introduce a system which extracts the important information from an image taken by a CCD camera installed at the rear view mirror in a car. The approach consists of a sequential and a parallel sensor and information processing. Three main tasks namely the initial segmentation (object detection), the object tracking and the object classification are realized by integration in the sequential branch and by fusion in the parallel branch. The main gain of this approach is given by the integrative coupling of different algorithms providing partly redundant information.  Driver Assistance, Machine Vision,
HitNet: a neural network with capsules embedded in a Hit-or-Miss layer, extended with hybrid data augmentation and ghost capsules

Neural networks designed for the task of classification have become a commodity in recent years. Many works target the development of better networks, which results in a complexification of their architectures with more layers, multiple sub-networks, or even the combination of multiple classifiers. In this paper, we show how to redesign a simple network to reach excellent performances, which are better than the results reproduced with CapsNet on several datasets, by replacing a layer with a Hit-or-Miss layer. This layer contains activated vectors, called capsules, that we train to hit or miss a central capsule by tailoring a specific centripetal loss function. We also show how our network, named HitNet, is capable of synthesizing a representative sample of the images of a given class by including a reconstruction network. This possibility allows to develop a data augmentation step combining information from the data space and the feature space, resulting in a hybrid data augmentation process. In addition, we introduce the possibility for HitNet, to adopt an alternative to the true target when needed by using the new concept of ghost capsules, which is used here to detect potentially mislabeled images in the training data.
THE FAILURE OF E-GOVERNMENT IN DEVELOPING COUNTRIES: A LITERATURE REVIEW

This paper provides an insight to the trends that exist within academic writing in the much talked about area of e-government, and the potential they hold for developing countries. While there is much hype about success stories, the bitter truth that presents itself is that the majority of e-government projects in developing countries fail. After an introduction to Ciborra’s (2005) view on e-government, this paper proceeds to use Heeks’ (2003) ‘archetypes of failure’, which are brought about by gaps between the design of the technology itself and reality of the context, to classify some of the current literature. This classification provides a brief overview of themes manifested within this body of knowledge, serving as a useful background for practitioners and implementers of e-government in developing countries.
Discovering Similar Multidimensional Trajectories

We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest Common Subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to the similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translating of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and Time Warping distance functions (for real and synthetic data) and show the superiority of our approach, especially under the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach.
An efficient reconciliation algorithm for social networks

People today typically use multiple online social networks (Facebook, Twitter, Google+, LinkedIn, etc.). Each online network represents a subset of their “real” ego-networks. An interesting and challenging problem is to reconcile these online networks, that is, to identify all the accounts belonging to the same individual. Besides providing a richer understanding of social dynamics, the problem has a number of practical applications. At first sight, this problem appears algorithmically challenging. Fortunately, a small fraction of individuals explicitly link their accounts across multiple networks; our work leverages these connections to identify a very large fraction of the network. Our main contributions are to mathematically formalize the problem for the first time, and to design a simple, local, and efficient parallel algorithm to solve it. We are able to prove strong theoretical guarantees on the algorithm’s performance on well-established network models (Random Graphs, Preferential Attachment). We also experimentally confirm the effectiveness of the algorithm on synthetic and real social network data sets.
Blood vessel extraction and optic disc removal using curvelet transform and kernel fuzzy c-means

This paper proposes an automatic blood vessel extraction method on retinal images using matched filtering in an integrated system design platform that involves curvelet transform and kernel based fuzzy c-means. Since curvelet transform represents the lines, the edges and the curvatures very well and in compact form (by less number of coefficients) compared to other multi-resolution techniques, this paper uses curvelet transform for enhancement of the retinal vasculature. Matched filtering is then used to intensify the blood vessels' response which is further employed by kernel based fuzzy c-means algorithm that extracts the vessel silhouette from the background through non-linear mapping. For pathological images, in addition to matched filtering, Laplacian of Gaussian filter is also employed to distinguish the step and the ramp like signal from that of vessel structure. To test the efficacy of the proposed method, the algorithm has also been applied to images in presence of additive white Gaussian noise where the curvelet transform has been used for image denoising. Performance is evaluated on publicly available DRIVE, STARE and DIARETDB1 databases and is compared with the large number of existing blood vessel extraction methodologies. Simulation results demonstrate that the proposed method is very much efficient in detecting the long and the thick as well as the short and the thin vessels with an average accuracy of 96.16% for the DRIVE and 97.35% for the STARE database wherein the existing methods fail to extract the tiny and the thin vessels.
C-RNN-GAN: Continuous recurrent neural networks with adversarial training

Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.
User-level sentiment analysis incorporating social networks

We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.
An Ensemble of Convolutional Neural Networks Using Wavelets for Image Classification

Machine learning is an integral technology many people utilize in all areas of human life. It is pervasive in modern living worldwide, and has multiple usages. One application is image classification, embraced across many spheres of influence such as business, finance, medicine, etc. to enhance produces, causes, efficiency, etc. This need for more accurate, detail-oriented classification increases the need for modifications, adaptations, and innovations to Deep Learning Algorithms. This article used Convolutional Neural Networks (CNN) to classify scenes in the CIFAR-10 database, and detect emotions in the KDEF database. The proposed method converted the data to the wavelet domain to attain greater accuracy and comparable efficiency to the spatial domain processing. By dividing image data into subbands, important feature learning occurred over differing low to high frequencies. The combination of the learned low and high frequency features, and processing the fused feature mapping resulted in an advance in the detection accuracy. Comparing the proposed methods to spatial domain CNN and Stacked Denoising Autoencoder (SDA), experimental findings revealed a substantial increase in accuracy.
Analysis and Design of Profiled Blast Walls

This report presents the study on the design and analysis of stainless steel profiled barriers subjected to blast loading generated from typical hydrocarbon explosions. The Technical Note 5 (TN5) issued by the Fire and Blast Information Group and a time domain finite element commercial software packages will be used for the above purposes. Particular attention is given to the plastic response of the blastwalls. Through this study, extensive discussions will be given to the adequacy of the simple design tool commonly used by the offshore industry for the analysis and design of blastwalls. In addition, appropriate guidance will also be presented on the use of the finite element numerical tool for the above purpose. This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording or otherwise) without the prior written permission of the copyright owner. ii SUMMARY Profiled barriers have been increasingly used as blastwalls in typical offshore topsides modules to provide a safety barrier for working personnel and critical equipments. Most of the blastwalls are designed using Single Degree of Freedom Method as recommended in the design guidance Technical Note 5. One of the uncertainties in blastwalls design remains in the accurate evaluation of the explosion loading. As such, they are normally designed against a nominal pressure of 0.5 bar which often results in the elastic response of the section. However, recent large scale explosion tests on the blastwalls have shown the possibility of as high as 4 bar overpressure with a shorter duration in a typical module. Under such conditions, elastic design of blastwalls, in which most blastwalls has been traditionally designed, is no longer economical and feasible. This report presents the study on the design and analysis of stainless steel profiled barriers subjected to blast loading generated from typical hydrocarbon explosions. The Technical Note 5 (TN5) issued by the Fire and Blast Information Group and a time domain finite element commercial software packages will be used for the above purposes. Particular attention is given to the plastic response of the blastwalls. Through this study, extensive discussions will be given to the …