nils-herrmann/clean_scirepeval huggingface dataset:
Narrowband and Wideband Metamaterial Antennas Based on Degenerate Band Edge and Magnetic Photonic Crystals

Historically, antennas and microwave devices relied on isotropic media. This provided for limited degrees of freedom (one for dielectric and another for magnetic) in the design process. In contrast, anisotropic media introduce several more degrees of freedom (at least three more for dielectrics and three additional ones for magnetic) opening a new direction in designing radio-frequency (RF) communication devices and wireless systems. A focus of the paper is the introduction of anisotropic media parameters emulated using simple printed, but highly coupled, transmission lines (TRLs). The paper begins by introducing the equivalence between in-plane anisotropy and coupled TRLs to realize degenerate band edge (DBE) and magnetic photonic crystal (MPC) modes. This is followed by the design of miniature antenna elements via dispersion engineering, demonstrating their performance on small finite substrates. The second part of the paper is focused on concatenating DBE and MPC antenna elements to realize smaller size wideband arrays. Such arrays exploit the current sheet antenna (CSA) concept to achieve the coveted goal of small wideband metamaterial arrays. For example, by constructing an array of antenna elements ~ λ/10&times;λ/10 in size, highly conformal (very thin) apertures delivering 5 : 1 bandwidth are demonstrated while avoiding grating lobes. In contrast to transitional approaches, the proposed method exploits (rather than suppressing) the metallic ground plane inductance. Instead, the capacitance of the tightly coupled antenna elements is used to cancel the inductance over wide bandwidths. By further employing small size array elements, large bandwidths can be achieved using a smaller footprint.
On Face Segmentation, Face Swapping, and Face Perception

We show that even when face images are unconstrained and arbitrarily paired, face swapping between them is quite simple. To this end, we make the following contributions. (a) Instead of tailoring systems for face segmentation, as others previously proposed, we show that a standard fully convolutional network (FCN) can achieve remarkably fast and accurate segmentations, provided that it is trained on a rich enough example set. For this purpose, we describe novel data collection and generation routines which provide challenging segmented face examples. (b) We use our segmentations for robust face swapping under unprecedented conditions. (c) Unlike previous work, our swapping is robust enough to allow for extensive quantitative tests. To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition. We show that our intra-subject swapped faces remain as recognizable as their sources, testifying to the effectiveness of our method. In line with established perceptual studies, we show that better face swapping produces less recognizable inter-subject results. This is the first time this effect was quantitatively demonstrated by machine vision systems.
Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams

Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We introduce an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. We describe a Web service that leverages this framework to track political memes in Twitter and help detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We present some cases of abusive behaviors uncovered by our service. Finally, we discuss promising preliminary results on the detection of suspicious memes via supervised learning based on features extracted from the topology of the diffusion networks, sentiment analysis, and crowdsourced annotations.
Hybrid intrusion detection in connected self-driving vehicles

Emerging self-driving vehicles are vulnerable to different attacks due to the principle and the type of communication systems that are used in these vehicles. These vehicles are increasingly relying on external communication via vehicular ad hoc networks (VANETs). VANETs add new threats to self-driving vehicles that contribute to substantial challenges in autonomous systems. These communication systems render self-driving vehicles vulnerable to many types of malicious attacks, such as Sybil attacks, Denial of Service (DoS), black hole, grey hole and wormhole attacks. In this paper, we propose an intelligent security system designed to secure external communications for self-driving and semi self-driving cars. The proposed scheme is based on Proportional Overlapping Score (POS) to decrease the number of features found in the Kyoto benchmark dataset. The hybrid detection system relies on the Back Propagation neural networks (BP), to detect a common type of attack in VANETs: Denial-of-Service (DoS). The experimental results show that the proposed BP-IDS is capable of identifying malicious vehicles in self-driving and semi self-driving vehicles.
An IoT electric business model based on the protocol of bitcoin

Nowadays, the development of traditional business models become more and more mature that people use them to guide various kinds of E-business activities. Internet of things(IoT), being an innovative revolution over the Internet, becomes a new platform for E-business. However, old business models could hardly fit for the E-business on the IoT. In this article, we 1) propose an IoT E-business model, which is specially designed for the IoT E-business; 2) redesign many elements in traditional E-business models; 3) realize the transaction of smart property and paid data on the IoT with the help of P2P trade based on the Blockchain and smart contract. We also experiment our design and make a comprehensive discuss.
Product placement detection based on image processing

Product positioning in stores is of importance for the manufacturers. With this aim, the placement on shelves is done according to some documents named planograms. To check whether the placement is in compliance with the planogram, manufacturers need tools to gather information easily. In this work, using existing image processing techniques, we aim for the automation of planogram compliance control. We propose a novel technique for inventory management with the assumption that we can extract meaningful information from images using planogram context. The controller will be able to capture a photograph of the shelves and upload to the server to be processed by our system. The system is composed of three modules: 1) shelf detection, 2) product detection and 3) brand recognition. Basically, we make use of Hough Transform, Cascade Object Detection algorithm, and Support Vector Machines for the modules respectively. We test each module separately on a dataset we collected with cigarette products.
SR-4000 and CamCube3.0 Time of Flight (ToF) Cameras: Tests and Comparison

In this paper experimental comparisons between two Time-of-Flight (ToF) cameras are reported in order to test their performance and to give some procedures for testing data delivered by this kind of technology. In particular, the SR-4000 camera by Mesa Imaging AG and the CamCube3.0 by PMD Technologies have been evaluated since they have good performances and are well known to researchers dealing with Time-ofFlight (ToF) cameras. After a brief overview of commercial ToF cameras available on the market and the main specifications of the tested devices, two topics are presented in this paper. First, the influence of camera warm-up on distance measurement is analyzed: a warm-up of 40 minutes is suggested to obtain the measurement stability, especially in the case of the CamCube3.0 camera, that exhibits distance measurement variations of several centimeters. Secondly, the variation of distance measurement precision variation over integration time is presented: distance measurement precisions of some millimeters are obtained in both cases. Finally, a comparison between the two cameras based on the experiments and some information about future work on evaluation of sunlight influence on distance measurements are reported.
Performance Comparisons of Facial Expression Recognition in Jaffe Database

Facial expression provides an important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression recognition has recently become a promising research area. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this paper, we investigate various feature representation and expression classification schemes to recognize seven different facial expressions, such as happy, neutral, angry, disgust, sad, fear and surprise, in the JAFFE database. Experimental results show that the method of combining 2D-LDA (Linear Discriminant Analysis) and SVM (Support Vector Machine) outperforms others. The recognition rate of this method is 95.71% by using leave-one-out strategy and 94.13% by using cross-validation strategy. It takes only 0.0357 second to process one image of size 256 × 256.
A Survey On Data Mining Techniques In Customer Churn Analysis For Telecom Industry

Customer churn prediction in Telecom Industry is a core research topic in recent years. A huge amount of data is generated in Telecom Industry every minute. On the other hand, there is lots of development in data mining techniques. Customer churn has emerged as one of the major issues in Telecom Industry. Telecom research indicates that it is more expensive to gain a new customer than to retain an existing one. In order to retain existing customers, Telecom providers need to know the reasons of churn, which can be realized through the knowledge extracted from Telecom data. This paper surveys the commonly used data mining techniques to identify customer churn patterns. The recent literature in the area of predictive data mining techniques in customer churn behavior is reviewed and a discussion on the future research directions is offered.
Araucaria: Software for Argument Analysis, Diagramming and Representation

Argumentation theory involves the analysis of naturally occurring argument, and one key tool employed to this end both in the academic community and in teaching critical thinking skills to undergraduates is argument diagramming. By identifying the structure of an argument in terms of its constituents and the relationships between them, it becomes easier to critically evaluate each part of an argument in turn. The task of analysis and diagramming, however, is labor intensive and often idiosyncratic, which can make academic exchange difficult. The Araucaria system provides an interface which supports the diagramming process, and then saves the result using AML, an open standard, designed in XML, for describing argument structure. Araucaria aims to be of use not only in pedagogical situations, but also in support of research activity. As a result, it has been designed from the outset to handle more advanced argumentation theoretic concepts such as schemes, which capture stereotypical patterns of reasoning. The software is also designed to be compatible with a number of applications under development, including dialogic interaction and online corpus provision. Together, these features, combined with its platform independence and ease of use, have the potential to make Araucaria a valuable resource for the academic community.
Printem: Instant Printed Circuit Boards with Standard Office Printers & Inks

Printem film, a novel method for the fabrication of Printed Circuit Boards (PCBs) for small batch/prototyping use, is presented. Printem film enables a standard office inkjet or laser printer, using standard inks, to produce a PCB: the user prints a negative of the PCB onto the film, exposes it to UV or sunlight, and then tears-away the unneeded portion of the film, leaving-behind a copper PCB. PCBs produced with Printem film are as conductive as PCBs created using standard industrial methods. Herein, the composition of Printem film is described, and advantages of various materials discussed. Sample applications are also described, each of which demonstrates some unique advantage of Printem film over current prototyping methods: conductivity, flexibility, the ability to be cut with a pair of scissors, and the ability to be mounted to a rigid backplane.
 NOTE: publication of full-text held until November 9, 2015.
Liquido: Embedding Liquids into 3D Printed Objects to Sense Tilting and Motion

Tilting and motion are widely used as interaction modalities in smart objects such as wearables and smart phones (e.g., to detect posture or shaking). They are often sensed with accelerometers. In this paper, we propose to embed liquids into 3D printed objects while printing to sense various tilting and motion interactions via capacitive sensing. This method reduces the assembly effort after printing and is a low-cost and easy-to-apply way of extending the input capabilities of 3D printed objects. We contribute two liquid sensing patterns and a practical printing process using a standard dual-extrusion 3D printer and commercially available materials. We validate the method by a series of evaluations and provide a set of interactive example applications.
Engineered CRISPR-Cas9 nucleases with altered PAM specificities

Although CRISPR-Cas9 nucleases are widely used for genome editing, the range of sequences that Cas9 can recognize is constrained by the need for a specific protospacer adjacent motif (PAM). As a result, it can often be difficult to target double-stranded breaks (DSBs) with the precision that is necessary for various genome-editing applications. The ability to engineer Cas9 derivatives with purposefully altered PAM specificities would address this limitation. Here we show that the commonly used Streptococcus pyogenes Cas9 (SpCas9) can be modified to recognize alternative PAM sequences using structural information, bacterial selection-based directed evolution, and combinatorial design. These altered PAM specificity variants enable robust editing of endogenous gene sites in zebrafish and human cells not currently targetable by wild-type SpCas9, and their genome-wide specificities are comparable to wild-type SpCas9 as judged by GUIDE-seq analysis. In addition, we identify and characterize another SpCas9 variant that exhibits improved specificity in human cells, possessing better discrimination against off-target sites with non-canonical NAG and NGA PAMs and/or mismatched spacers. We also find that two smaller-size Cas9 orthologues, Streptococcus thermophilus Cas9 (St1Cas9) and Staphylococcus aureus Cas9 (SaCas9), function efficiently in the bacterial selection systems and in human cells, suggesting that our engineering strategies could be extended to Cas9s from other species. Our findings provide broadly useful SpCas9 variants and, more importantly, establish the feasibility of engineering a wide range of Cas9s with altered and improved PAM specificities.
A Broadband Dual-Polarized Base Station Antenna With Anti-Interference Capability

A broadband dual-polarized antenna with antiinterference capability is presented in this letter for base station applications. The proposed antenna contains three parts: main radiator, feed structures, and reflector. Specifically, the main radiator has two crossed dipoles to realize ±45° polarizations; two vertical substrates, which have Γ-shaped feeding lines on the front side and rectangle patches on the back side, are placed between the main radiator and reflector for feeding; the reflector placed below is used to get high gain and unidirectional radiation. In addition, C-shaped stubs are arranged next to the feeding lines in order to filter the unwanted frequency band. A prototype of the antenna was fabricated and tested. The measured results show that it has a bandwidth of 52.6% ($\rm{VSWR < 1.5}$ ) along with a sharp notch band from 2.27 to 2.53 GHz. Measured isolation of higher than 25.4 dB, half-power beamwidth of around 60° and average gain of 7.57 dBi across the operating band are also obtained. Having the advantages of anti-interference capability, dual polarization, wide bandwidth, high isolation, and good radiation performance, the proposed antenna can find its application in the next-generation wireless communication systems.
A Comparison between Agile and Traditional Software Development Methodologies

Software has been part of modern society for more than 50 years. There are several software development methodologies in use today. Some companies have their own customized methodology for developing their software but the majority speaks about two kinds of methodologies: heavyweight and lightweight. Heavyweight methodologies, also considered as the traditional way to develop software, claim their support to comprehensive planning, detailed documentation, and expansive design. The lightweight methodologies, also known as agile modeling, have gained significant attention from the software engineering community in the last few years. Unlike traditional methods, agile methodologies employ short iterative cycles, and rely on tacit knowledge within a team as opposed to documentation. In this dissertation, I have described the characteristics of some traditional and agile methodologies that are widely used in software development. I have also discussed the strengths and weakness between the two opposing methodologies and provided the challenges associated with implementing agile processes in the software industry. This anecdotal evidence is rising regarding the effectiveness of agile methodologies in certain environments; but there have not been much collection and analysis of empirical evidence for agile projects. However, to support my dissertation I conducted a questionnaire, soliciting feedback from software industry practitioners to evaluate which methodology has a better success rate for different sizes of software development. According to our findings agile methodologies can provide good benefits for small scaled and medium scaled projects but for large scaled projects traditional methods seem dominant.
VIS-Tracker: A Wearable Vision-Inertial Self-Tracker

We present a demonstrated and commercially viable self-tracker, using robust software that fuses data from inertial and vision sensors. Compared to infrastructurebased trackers, self-trackers have the advantage that objects can be tracked over an extremely wide area, without the prohibitive cost of an extensive network of sensors or emitters to track them. So far most AR research has focused on the long-term goal of a purely vision-based tracker that can operate in arbitrary unprepared environments, even outdoors. We instead chose to start with artificial fiducials, in order to quickly develop the first self-tracker which is small enough to wear on a belt, low cost, easy to install and self-calibrate, and low enough latency to achieve AR registration. We also present a roadmap for how we plan to migrate from artificial fiducials to natural ones. By designing to the requirements of AR, our system can easily handle the less challenging applications of wearable VR systems and robot navigation.
Unsupervised kernel least mean square algorithm for solving ordinary differential equations

In this paper a novel method is introduced based on the use of an unsupervised version of kernel least mean square (KLMS) algorithm for solving ordinary differential equations (ODEs). The algorithm is unsupervised because here no desired signal needs to be determined by user and the output of the model is generated by iterating the algorithm progressively. However, there are several new implementation, fast convergence and also little error. Furthermore, it is also a KLMS with obvious characteristics. In this paper the ability of KLMS is used to estimate the answer of ODE. First a trial solution of ODE is written as a sum of two parts, the first part satisfies the initial condition and the second part is trained using the KLMS algorithm so as the trial solution solves the ODE. The accuracy of the method is illustrated by solving several problems. Also the sensitivity of the convergence is analyzed by changing the step size parameters and kernel functions. Finally, the proposed method is compared with neuro-fuzzy [21] approach.
A dual-loop delay-locked loop using multiple voltage-controlled delay lines

This paper describes a dual-loop delay-locked loop (DLL) which overcomes the problem of a limited delay range by using multiple voltage-controlled delay lines (VCDLs). A reference loop generates quadrature clocks, which are then delayed with controllable amounts by four VCDLs and multiplexed to generate the output clock in a main loop. This architecture enables the DLL to emulate the infinite-length VCDL with multiple finite-length VCDLs. The DLL incorporates a replica biasing circuit for low-jitter characteristics and a duty cycle corrector immune to prevalent process mismatches. A test chip has been fabricated using a 0.25m CMOS process. At 400 MHz, the peak-to-peak jitter with a quiet 2.5-V supply is 54 ps, and the supply-noise sensitivity is 0.32 ps/mV.
Deep Convolutional Networks on Graph-Structured Data

Deep Learning’s recent successes have mostly relied on Convolutional Networks, which exploit fundamental statistical properties of images, sounds and video data: the local stationarity and multi-scale compositional structure, that allows expressing long range interactions in terms of shorter, localized interactions. However, there exist other important examples, such as text documents or bioinformatic data, that may lack some or all of these strong statistical regularities. In this paper we consider the general question of how to construct deep architectures with small learning complexity on general non-Euclidean domains, which are typically unknown and need to be estimated from the data. In particular, we develop an extension of Spectral Networks which incorporates a Graph Estimation procedure, that we test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate.
Competing risk analysis using R: an easy guide for clinicians

In the last decade with widespread use of quantitative analyses in medical research, close co-operation between statisticians and physicians has become essential from the experimental design through all phases of complex statistical analysis. On the other hand, easy-to-use statistical packages allow clinicians to perform basic statistical analyses themselves. Since the software they most commonly use does not perform in depth competing risk analysis, we recommend an add-on package for the R statistical software. We provide all the instructions for downloading it from internet and illustrate how to use it for analysis of a sample dataset of patients who underwent haematopoietic stem cell transplantation for acute leukaemia.