nils-herrmann/clean_scirepeval huggingface dataset:
Control allocation for yaw stabilization in automotive vehicles using multiparametric nonlinear programming

We investigate the use of a nonlinear control allocation scheme for automotive vehicles. Such a scheme is useful in e.g. yaw or roll stabilization of the vehicle. The control allocation allows a modularization of the control task, such that a higher level control system specifies a desired moment to work on the vehicle, while the control allocation distributes this moment among the individual wheels by commanding appropriate wheel slips. The control allocation problem is defined as a nonlinear optimization problem, to which an explicit piecewise linear approximate solution function is computed off-line. Such a solution function can computationally efficiently be implemented in real time with at most a few hundred arithmetic operations per sample. Yaw stabilization of the vehicle yaw dynamics is used as an example of use of the control allocation. Simulations show that the controller stabilizes the vehicle in an extreme manoeuvre where the vehicle yaw dynamics otherwise becomes unstable.
Effective Botnet Detection Through Neural Networks on Convolutional Features

Botnet is one of the major threats on the Internet for committing cybercrimes, such as DDoS attacks, stealing sensitive information, spreading spams, etc. It is a challenging issue to detect modern botnets that are continuously improving for evading detection. In this paper, we propose a machine learning based botnet detection system that is shown to be effective in identifying P2P botnets. Our approach extracts convolutional version of effective flow-based features, and trains a classification model by using a feed-forward artificial neural network. The experimental results show that the accuracy of detection using the convolutional features is better than the ones using the traditional features. It can achieve 94.7% of detection accuracy and 2.2% of false positive rate on the known P2P botnet datasets. Furthermore, our system provides an additional confidence testing for enhancing performance of botnet detection. It further classifies the network traffic of insufficient confidence in the neural network. The experiment shows that this stage can increase the detection accuracy up to 98.6% and decrease the false positive rate up to 0.5%.
A coplanar Vivaldi antenna with wide band balun proposal for the low frequency band of the SKA: approach to the FPA solution

A 17:1 band coplanar Vivaldi antenna with wide band balun for the low frequencies of the SKA (square kilometre array) is designed, simulated and measured. Looking for best capabilities in the widest possible band, keeping in mind the low cost and weight desired for the application and the requirement for a later activation of the antenna, a close study to the feeding method was developed
Social media competitive analysis and text mining: A case study in the pizza industry

Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut,
Optical spatial solitons: historical overview and recent advances.

Solitons, nonlinear self-trapped wavepackets, have been extensively studied in many and diverse branches of physics such as optics, plasmas, condensed matter physics, fluid mechanics, particle physics and even astrophysics. Interestingly, over the past two decades, the field of solitons and related nonlinear phenomena has been substantially advanced and enriched by research and discoveries in nonlinear optics. While optical solitons have been vigorously investigated in both spatial and temporal domains, it is now fair to say that much soliton research has been mainly driven by the work on optical spatial solitons. This is partly due to the fact that although temporal solitons as realized in fiber optic systems are fundamentally one-dimensional entities, the high dimensionality associated with their spatial counterparts has opened up altogether new scientific possibilities in soliton research. Another reason is related to the response time of the nonlinearity. Unlike temporal optical solitons, spatial solitons have been realized by employing a variety of noninstantaneous nonlinearities, ranging from the nonlinearities in photorefractive materials and liquid crystals to the nonlinearities mediated by the thermal effect, thermophoresis and the gradient force in colloidal suspensions. Such a diversity of nonlinear effects has given rise to numerous soliton phenomena that could otherwise not be envisioned, because for decades scientists were of the mindset that solitons must strictly be the exact solutions of the cubic nonlinear Schrödinger equation as established for ideal Kerr nonlinear media. As such, the discoveries of optical spatial solitons in different systems and associated new phenomena have stimulated broad interest in soliton research. In particular, the study of incoherent solitons and discrete spatial solitons in optical periodic media not only led to advances in our understanding of fundamental processes in nonlinear optics and photonics, but also had a very important impact on a variety of other disciplines in nonlinear science. In this paper, we provide a brief overview of optical spatial solitons. This review will cover a variety of issues pertaining to self-trapped waves supported by different types of nonlinearities, as well as various families of spatial solitons such as optical lattice solitons and surface solitons. Recent developments in the area of optical spatial solitons, such as 3D light bullets, subwavelength solitons, self-trapping in soft condensed matter and spatial solitons in systems with parity-time symmetry will also be discussed briefly.
ParlAI: A Dialog Research Software Platform

We introduce ParlAI (pronounced “parlay”), an open-source software platform for dialog research implemented in Python, available at http://parl.ai. Its goal is to provide a unified framework for sharing, training and testing dialog models; integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning; and a repository of machine learning models for comparing with others’ models, and improving upon existing architectures. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated, including neural models such as memory networks, seq2seq and attentive LSTMs.
Refinement of surface mesh for accurate multiview reconstruction

In this paper we propose a pipeline for accurate 3D reconstruction from multiple images that deals with some of the possible sources of inaccuracy present in the input data. Namely, we address the problem of inaccurate camera calibration by including a method  adjusting the camera parameters in a global structure-and-motion problem which is solved with a depth map representation that is suitable to large scenes. Secondly, we take the triangular mesh and calibration improved by the global method in the first phase to refine the surface both geometrically and radiometrically. Here we propose surface energy which combines photo consistency with contour matching and minimize it with a gradient method. Our main contribution lies in effective computation of the gradient that naturally balances weight between regularizing and data terms by employing scale space approach to find the correct local minimum. The results are demonstrated on standard high-resolution datasets and a complex outdoor scene.
Scientific Articles Exploration System Model based in Immersive Virtual Reality and Natural Language Processing Techniques

After having carried out a historical review and identifying the state of the art in relation to the interfaces for the exploration of scientific articles, the authors propose a model based in an immersive virtual environment, natural user interfaces and natural language processing, which provides an excellent experience for the user and allows for better use of some of its capabilities, for example, intuition and cognition in 3-dimensional environments. In this work, the Oculus Rift and Leap Motion Hardware devices are used. This work aims to contribute to the proposal of a tool which would facilitate and optimize the arduous task of reviewing literature in scientific databases. The case study is the exploration and information retrieval of scientific articles using ALICIA (Scientific database of Perú). Finally, conclusions and recommendations for future work are laid out and discussed. Immersive virtual environment; human computer interaction; natural user interfaces; natural language processing; Oculus Rift
Fiducial registration error and target registration error are uncorrelated

Image-guidance systems based on fiducial registration typically display some measure of registration accuracy based on the goodness of fit of the fiducials. A common measure is fiducial registration error (FRE), which equals the root-meansquare error in fiducial alignment between image space and physical space. It is natural for the surgeon to regard the displayed estimate of error as an indication of the accuracy of the system’s ability to provide guidance to surgical targets for a given case. Thus, when the estimate is smaller than usual, it may be assumed that the target registration error (TRE) is likely to be smaller than usual. We show that this assumption, while intuitively convincing, is in fact wrong. We show it in two ways. First, we prove to first order that for a given system with a given level of normally distributed fiducial localization error, all measures of goodness of fit are statistically independent of TRE, and therefore FRE and TRE are uncorrelated. Second, we demonstrate by means of computer simulations that they are uncorrelated for the exact problem as well. Since TRE is the true measure of registration accuracy of importance to the success of the surgery, our results show that no estimate of accuracy for a given patient that is based on goodness of fiducial fit for that patient gives any information whatever about true registration accuracy for that patient. Therefore surgeons should stop using such measures as indicators of registration quality for the patients on whom they are about to operate.
Bidirectional wireless power transfer topology using current-fed half bridge CLC transmitter LC receiver configuration for medium power applications

This paper presents a new bidirectional wireless power transfer (WPT) topology using current fed half bridge converter. Generally, WPT topology with current fed converter uses parallel LC resonant tank network in the transmitter side to compensate the reactive power. However, in medium power application this topology suffers a major drawback that the voltage stress in the inverter switches are considerably high due to high reactive power consumed by the loosely coupled coil. In the proposed topology this is mitigated by adding a suitably designed capacitor in series with the transmitter coil. Both during grid to vehicle and vehicle to grid operations the power flow is controlled through variable switching frequency to achieve extended ZVS of the inverter switches. Detail analysis and converter design procedure is presented for both grid to vehicle and vehicle to grid operations. A 1.2kW lab-prototype is developed and experimental results are presented to verify the analysis.
LeakDAF: An Automated Tool for Detecting Leaked Activities and Fragments of Android Applications

Memory leak, one of the most common problems threatening android apps, might drain the limited memory of mobile devices, cause unexpected delays, no-responses or even crashes to apps. Activity/Fragment Leak is one of the most common and serious causes of memory leaks and has a vast influence on the Android app market. Existing work to identify leaked activities/fragments either depend highly on the experience of developers, or require app's source code and manual interactions. In this paper, we propose an automatic tool named LeakDAF for detecting leaked activities/fragments automatically without manual intervention. LeakDAF makes use of UI testing technique to execute automatically the app under test, and applies memory analysis technique to inspect dumped heap files to identify leaked activities and fragments based on Android's mechanisms for managing them. To evaluate the effectiveness of LeakDAF, we successfully applied it to 35 open source and 64 commercial apps, and we detected at least one leaked activity or fragment for 10 open source apps and 35 commercial apps.
Real-time seismology and earthquake hazard mitigation

Recent advances in seismic sensor technology, data acquisition systems, digital communications, and computer hardware and software make it possible to build reliable real-time earthquake information systems. Such systems provide a means for modern urban regions to cope effectively with the aftermath of major earthquakes and, in some cases, they may even provide warning, seconds before the arrival of seismic waves. In the long term these systems also provide basic data for mitigation strategies such as improved building codes.
CySCom: Cybersecurity COMics

Every year, companies are searching for professionals in cybersecurity more than ever before; however, there is a big void in the pipeline. Interestingly enough, the largest source of security incidents are unintentional insiders, which demonstrates the need for cybersecurity education not only for developing cybersecurity workforce but also to raise mere awareness and consciousness about these issues among general users of technology. It has been surveyed that 87% of American teenagers, ages 13-17, have been reported to have or have access to a desktop or laptop computer The goal of this project is to seed interest in cybersecurity by educating the youth about cybersecurity related concepts and safe online practices through the fun, simplistic visual form of comics.
Internet addiction assessment tools: dimensional structure and methodological status.

Excessive internet use is becoming a concern, and some have proposed that it may involve addiction. We evaluated the dimensions assessed by, and psychometric properties of, a range of questionnaires purporting to assess internet addiction.



Fourteen questionnaires were identified purporting to assess internet addiction among adolescents and adults published between January 1993 and October 2011. Their reported dimensional structure, construct, discriminant and convergent validity and reliability were assessed, as well as the methods used to derive these.



Methods used to evaluate internet addiction questionnaires varied considerably. Three dimensions of addiction predominated: compulsive use (79%), negative outcomes (86%) and salience (71%). Less common were escapism (21%), withdrawal symptoms (36%) and other dimensions. Measures of validity and reliability were found to be within normally acceptable limits.



There is a broad convergence of questionnaires purporting to assess internet addiction suggesting that compulsive use, negative outcome and salience should be covered and the questionnaires show adequate psychometric properties. However, the methods used to evaluate the questionnaires vary widely and possible factors contributing to excessive use such as social motivation do not appear to be covered.
A practical tool for maximal information coefficient analysis

The ability of finding complex associations in large omics datasets, assessing their significance, and prioritizing them according to their strength can be of great help in the data exploration phase. Mutual information-based measures of association are particularly promising, in particular after the recent introduction of the TICe and MICe estimators, which combine computational efficiency with superior bias/variance properties. An open-source software implementation of these two measures providing a complete procedure to test their significance would be extremely useful.



Here, we present MICtools, a comprehensive and effective pipeline that combines TICe and MICe into a multistep procedure that allows the identification of relationships of various degrees of complexity. MICtools calculates their strength assessing statistical significance using a permutation-based strategy. The performances of the proposed approach are assessed by an extensive investigation in synthetic datasets and an example of a potential application on a metagenomic dataset is also illustrated.



We show that MICtools, combining TICe and MICe, is able to highlight associations that would not be captured by conventional strategies.
A Survey on Handwritten Character Recognition Techniques for Various Indian Languages

Handwritten character recognition is always an interesting area of pattern recognition for research in the field of image processing. Many researchers have presented their work in this area and still research is undergoing to achieve high accuracy. This paper is mainly concerned for the people who are working on the character recognition and review of work to recognize handwritten character for various Indian languages. The objective of this paper is to describe the set of preprocessing, segmentation, feature extraction and classification techniques.
Learning Factorized Transforms for Unsupervised Adaptation of LSTM-RNN Acoustic Models

Factorized Hidden Layer (FHL) adaptation has been proposed for speaker adaptation of deep neural network (DNN) based acoustic models. In FHL adaptation, a speaker-dependent (SD) transformation matrix and an SD bias are included in addition to the standard affine transformation. The SD transformation is a linear combination of rank-1 matrices whereas the SD bias is a linear combination of vectors. Recently, the Long ShortTerm Memory (LSTM) Recurrent Neural Networks (RNNs) have shown to outperform DNN acoustic models in many Automatic Speech Recognition (ASR) tasks. In this work, we investigate the effectiveness of SD transformations for LSTM-RNN acoustic models. Experimental results show that when combined with scaling of LSTM cell states’ outputs, SD transformations achieve 2.3% and 2.1% absolute improvements over the baseline LSTM systems for the AMI IHM and AMI SDM tasks respectively.
Compelling Intelligent User Interfaces - How Much AI?

Efforts to incorporate intelligence into the user interface have been underway for decades, but the commercial impact of this work has not lived up to early expectations, and is not immediately apparent. This situation appears to be changing. However, so far the most interesting intelligent user interfaces (IUIS) have tended to use minimal or simplistic AI. In this panel we consider whether more or less AI is the key to the development of compelling IUIS. The panelists will present examples of compelling IUIS that use a selection of AI techniques, mostly simple, but some complex. Each panelist will then comment on the merits of different kinds and quantities of AI in the development of pragmatic interface technology.
Experiences and lessons learned from a first SDTM submission project

This paper presents the experiences and the lessons learned from an SDTM conversion project. Within the scope of this project were (1) the late-stage conversion of datasets in internal standard to SDTM, (2) the creation of Trial Design datasets, (3) the development of macros to create define.xml and define.pdf, (4) the actual creation of these data definition files, and (5) the creation of SDTM annotated CRFs. Topics covered are the project strategy and our approach to the development of define.xml and define.pdf. We will present details of the conversion process and give examples of challenging domain implementations. Moreover, we will discuss the results of WebSDM checks performed as part a sample submission. Finally, the activity metrics of the project are presented.  After a period of piloting and tool development, a planned NDA submission with relatively few, recent studies offered an excellent opportunity to target for our first SDTM submission. Providing SDTM datasets implicated a full conversion process since our CDMS and reporting/analysis data are entirely based on internal, non-CDISC standards. For this submission we used reporting datasets as source data for SDTM (fig. 1). Thus, we adopted a ‘retrospective development’ strategy as described by Kenny & Litzsinger (2005). Main argument for choosing this strategy was that we wished to remain in sync with the reporting datasets and be able to reuse any derived variables needed for SDTM. Being the basis for all reporting and analyses related to our data the reporting datasets formed the main submission component. Supply of ADaM datasets was considered out-of-scope.  Clinical Data Management System CRF: (annotated) Case Report Form eCDT: electronic Common Technical Document ISS: Integrated Summary of Safety ISE: Integrated Summary of Efficacy TFL: Tables, Figures and Listings XPT: SAS transport files Figure 1: SDTM in the submission data flow from CDMS to eCTD Piloting prior to this project gave us a good level of experience with SDTM conversion. Moreover, it provided a valuable toolset and work environment, consisting of: • Structured sample programs (Base/SAS) for the conversion of 13 standard domains; • A macro library supporting various routine steps, e.g. creation of ISO8601 date/time variables, addition of MedDRA terms, creation of SUPPQUAL datasets, checking of SDTM metadata and structure compliance; • An extensible SDTM metadata repository, including CDISC controlled terminology and zero-observation datasets for all standard domains; • A template mapping sheet. CDMS extract Reporting datasets XPTs, CRFs, define.xml, define.pdf eCTD XPTs, CRFs define.xml, define.pdf SDTM datasets Analyses, TFLs Study reports, ISS, ISE Tabulation data
A Bootstrap Method for Automatic Rule Acquisition on Emotion Cause Extraction

Emotion cause extraction is one of the promising research topics in sentiment analysis, but has not been well-investigated so far. This task enables us to obtain useful information for sentiment classification and possibly to gain further insights about human emotion as well. This paper proposes a bootstrapping technique to automatically acquire conjunctive phrases as textual cue patterns for emotion cause extraction. The proposed method first gathers emotion causes via manually given cue phrases. It then acquires new conjunctive phrases from emotion phrases that contain similar emotion causes to previously gathered ones. In existing studies, the cost for creating comprehensive cue phrase rules for building emotion cause corpora was high because of their dependencies both on languages and on textual natures. The contribution of our method is its ability to automatically create the corpora from just a few cue phrases as seeds. Our method can expand cue phrases at low cost and acquire a large number of emotion causes of the promising quality compared to human annotations.