nils-herrmann/clean_scirepeval huggingface dataset:
3-D Printed Microwave Patch Antenna via Fused Deposition Method and Ultrasonic Wire Mesh Embedding Technique

In this work, the design, fabrication and characterization of a 3-D printed microwave patch antenna is presented. The antenna is fabricated by combining fused filament fabrication method for the dielectric part and ultrasonic metal wire mesh embedding approach for the conductor part. Full wave finite-element simulations for different wire mesh structures and also the entire antenna have been done to make sure the embedded wire mesh has good performance at microwave frequency. A microstrip patch antenna working around 7.5 GHz is printed and characterized to demonstrate the efficiency and accuracy of this technique. The measured reflection coefficient shows a good resonance peak at 7.5 GHz. The measured gain of this antenna is 5.5 dB at the resonance frequency. Good agreement between simulation and measurement is obtained in both reflection coefficient and radiation pattern.
PoseShop: Human Image Database Construction and Personalized Content Synthesis

We present PoseShop - a pipeline to construct segmented human image database with minimal manual intervention. By downloading, analyzing, and filtering massive amounts of human images from the Internet, we achieve a database which contains 400 thousands human figures that are segmented out of their background. The human figures are organized based on action semantic, clothes attributes, and indexed by the shape of their poses. They can be queried using either silhouette sketch or a skeleton to find a given pose. We demonstrate applications for this database for multiframe personalized content synthesis in the form of comic-strips, where the main character is the user or his/her friends. We address the two challenges of such synthesis, namely personalization and consistency over a set of frames, by introducing head swapping and clothes swapping techniques. We also demonstrate an action correlation analysis application to show the usefulness of the database for vision application.
Enhanced SMOTE algorithm for classification of imbalanced big-data using Random Forest

In the era of big data, the applications generating tremendous amount of data are becoming the main focus of attention as the wide increment of data generation and storage that has taken place in the last few years. This scenario is challenging for data mining techniques which are not arrogated to the new space and time requirements. In many of the real world applications, classification of imbalanced data-sets is the point of attraction. Most of the classification methods focused on two-class imbalanced problem. So, it is necessary to solve multi-class imbalanced problem, which exist in real-world domains. In the proposed work, we introduced a methodology for classification of multi-class imbalanced data. This methodology consists of two steps: In first step we used Binarization techniques (OVA and OVO) for decomposing original dataset into subsets of binary classes. In second step, the SMOTE algorithm is applied against each subset of imbalanced binary class in order to get balanced data. Finally, to achieve classification goal Random Forest (RF) classifier is used. Specifically, oversampling technique is adapted to big data using MapReduce so that this technique is able to handle as large data-set as needed. An experimental study is carried out to evaluate the performance of proposed method. For experimental analysis, we have used different datasets from UCI repository and the proposed system is implemented on Apache Hadoop and Apache Spark platform. The results obtained shows that proposed method outperforms over other methods.
Searching Concepts and Keywords in the Holy Quran

The Arabic language is one of the oldest languages in the world that presents its own features and challenges while searching for Arabic-based content. The most search systems for the Holy Quran is organized around words (contained in the target verses) rather than the concepts which those words denote. As a word denote many concepts (polysemy) and a concept can be denoted by many words (synonyms). In this study, we present a methodology to search about concepts and keywords in the Holy Quran while improving the chances of finding all desired verses. Three approaches are applied; text-based, stembased, and synonyms-based system. These three approaches are compared with respect to precision. The text-based approach is based on a full word, the synonyms-based approach is based on the synonyms of the word, and the stem-based approach is based on the stem of the word. We apply the light stemming algorithm to find the stem of the word.
A survey of evaluation techniques used in augmented reality studies

In this paper we report on an initial survey of user evaluation techniques used in Augmented Reality (AR) research. To identify all papers which include AR evaluations we reviewed research publications between the years 1993 and 2007 from online databases of selected scientific publishers. Starting with a total of 6071 publications we filtered the articles in several steps which resulted in 165 AR related publications with user evaluations. These publications were classified in two different ways: according to the evaluation type used following an earlier literature survey classification scheme; and according to the evaluation methods or approach used. We present the results of out literature survey, provide a comprehensive list of references of the selected publications, and discuss some possible research opportunities for future work.
Recognition of Plants by Leaves Digital Image and Neural Network

To identify different plants by leaves digital image is one key problem in precision farming. By the combination of image processing and neural network, Most of the image blocks of different plants could be correctly classified. Firstly, the image enhancement processing can make objects in the source image clear. Secondly, due to the different shapes and sizes of image blocks of leaves, they could be separated and extracted from sources. Then, by using image analysis tools from Matlab, these characters such as sizes, radius, perimeters, solidity, and eccentricity could be calculated. Then, using them as input data, create a radial basis function neural networks. Divide the input data into two parts. Select one part to train the network and the other to check the validity of the model. Finally, input data from other image frames under the same condition could be used to check the model. In this work, the total accuracy is about 80%. These methods was simple and highly effective, So they could easily be integrated into auto machines in the field, which can largely saving labor and enhance productivity.
High-Efficiency Isolated Boost DC–DC Converter for High-Power Low-Voltage Fuel-Cell Applications

A new design approach achieving very high conversion efficiency in low-voltage high-power isolated boost dc-dc converters is presented. The transformer eddy-current and proximity effects are analyzed, demonstrating that an extensive interleaving of primary and secondary windings is needed to avoid high winding losses. The analysis of transformer leakage inductance reveals that extremely low leakage inductance can be achieved, allowing stored energy to be dissipated. Power MOSFETs fully rated for repetitive avalanches allow primary-side voltage clamp circuits to be eliminated. The oversizing of the primary-switch voltage rating can thus be avoided, significantly reducing switch-conduction losses. Finally, silicon carbide rectifying diodes allow fast diode turn-off, further reducing losses. Detailed test results from a 1.5-kW full-bridge boost dc-dc converter verify the theoretical analysis and demonstrate very high conversion efficiency. The efficiency at minimum input voltage and maximum power is 96.8%. The maximum efficiency of the proposed converter is 98%.
Longitudinal Analysis of Android Ad Library Permissions

This paper investigates changes over time in the behavior of Android ad libraries. Taking a sample of 114,000 apps, we extract and classify their ad libraries. By considering the release dates of the applications that use a specific ad library version, we estimate the release date for the library, and thus build a chronological map of the permissions used by various ad libraries over time. By considering install counts, we are able to estimate the number of times that a given library has been installed on users’ devices. We find that the use of most permissions has increased over the last several years, and that more libraries are able to use permissions that pose particular risks to user privacy and security.
A generalization of the Moore-Penrose inverse related to matrix subspaces of Cn×m

A natural generalization of the classical Moore–Penrose inverse is presented. The so-called S-Moore–Penrose inverse of an m n complex matrix A, denoted by AS, is defined for any linear subspace S of the matrix vector space C . The S-Moore–Penrose inverse AS is characterized using either the singular value decomposition or (for the full rank case) the orthogonal complements with respect to the Frobenius inner product. These results are applied to the preconditioning of linear systems based on Frobenius norm minimization and to the linearly constrained linear least squares problem.
Multi-loss Regularized Deep Neural Network

A proper strategy to alleviate overfitting is critical to a deep neural network (DNN). In this paper, we introduce the cross-loss-function regularization for boosting the generalization capability of the DNN, which results in the multi-loss regularized DNN (ML-DNN) framework. For a particular learning task, e.g., image classification, only a single-loss function is used for all previous DNNs, and the intuition behind the multiloss framework is that the extra loss functions with different theoretical motivations (e.g., pairwise loss and LambdaRank loss) may drag the algorithm away from overfitting to one particular single-loss function (e.g., softmax loss). In the training stage, we pretrain the model with the single-core-loss function and then warm start the whole ML-DNN with the convolutional parameters transferred from the pretrained model. In the testing stage, the outputs by the ML-DNN from different loss functions are fused with average pooling to produce the ultimate prediction. The experiments conducted on several benchmark datasets (CIFAR-10, CIFAR-100, MNIST, and SVHN) demonstrate that the proposed ML-DNN framework, instantiated by the recently proposed network in network, considerably outperforms all other state-of-the-art methods.
A view on latest audio steganography techniques

Steganography has been proposed as a new alternative technique to enforce data security. Lately, novel and versatile audio steganographic methods have been proposed. A perfect audio Steganographic technique aim at embedding data in an imperceptible, robust and secure way and then extracting it by authorized people. Hence, up to date the main challenge in digital audio steganography is to obtain robust high capacity steganographic systems. Leaning towards designing a system that ensures high capacity or robustness and security of embedded data has led to great diversity in the existing steganographic techniques. In this paper, we present a current state of art literature in digital audio steganographic techniques. We explore their potentials and limitations to ensure secure communication. A comparison and an evaluation for the reviewed techniques is also presented in this paper.
Generalizing the Convolution Operator to Extend CNNs to Irregular Domains

Convolutional Neural Networks (CNNs) have become the state-of-the-art in supervised learning vision tasks. Their convolutional filters are of paramount importance for they allow to learn patterns while disregarding their locations in input images. When facing highly irregular domains, generalized convolutional operators based on an underlying graph structure have been proposed. However, these operators do not exactly match standard ones on grid graphs, and introduce unwanted additional invariance (e.g. with regards to rotations). We propose a novel approach to generalize CNNs to irregular domains using weight sharing and graph-based operators. Using experiments, we show that these models resemble CNNs on regular domains and offer better performance than multilayer perceptrons on distorded ones.
Anonymous Secure Framework in Connected Smart Home Environments

The smart home is an environment, where heterogeneous electronic devices and appliances are networked together to provide smart services in a ubiquitous manner to the individuals. As the homes become smarter, more complex, and technology dependent, the need for an adequate security mechanism with minimum individual’s intervention is growing. The recent serious security attacks have shown how the Internet-enabled smart homes can be turned into very dangerous spots for various ill intentions, and thus lead the privacy concerns for the individuals. For instance, an eavesdropper is able to derive the identity of a particular device/appliance via public channels that can be used to infer in the life pattern of an individual within the home area network. This paper proposes an anonymous secure framework (ASF) in connected smart home environments, using solely lightweight operations. The proposed framework in this paper provides efficient authentication and key agreement, and enables devices (identity and data) anonymity and unlinkability. One-time session key progression regularly renews the session key for the smart devices and dilutes the risk of using a compromised session key in the ASF. It is demonstrated that computation complexity of the proposed framework is low as compared with the existing schemes, while security has been significantly improved.
A Novel Dual-Broadband Dual-Polarized Electrical Downtilt Base Station Antenna for 2G/3G Applications

A novel dual-broadband dual-polarized base station antenna array with compact structure and low profile is proposed in this paper for the existing mobile communication system operating over 0.79–0.96 GHz (European Digital Dividend/CDMA/GSM) and 1.71–2.17 GHz (DCS/PCS/UMTS). The antenna array is mainly composed of five lower-band elements, ten upper-band elements, some U-shaped metal baffles, and a metal reflector with specific shape. In order to reduce the overall size of the antenna array, lower-band element is designed as octagon aperture shape that upper-band elements can be embedded in it. Two kinds of radiation elements (five for each kind of element) are applied in the antenna array as upper-band elements to achieve better radiation performance. The proposed antenna array achieves electrical downtilt (0°–14° and 0°–10° at lower frequency band and upper frequency band, respectively) by adjusting input amplitude and phase of each array element. Measured results demonstrate that the antenna array has good broadside radiation characteristics, including low voltage standing wave ratio (VSWR < 1.5), high port-to-port isolation (>28 dB), low backlobe level (>25 dB), high cross-polarization discrimination (>20 dB), and stable radiation pattern with horizontal half-power beamwidth (HPBW) 65° ± 5° at both frequency bands and all electrical downtilt angles. The peak gains of 15.1 and 17.3 dBi are obtained at lower and upper bands respectively. Owing to these advantages, the antenna array is suitable for existing 2G/3G applications in modern mobile communication systems.
Dynamic Inference of Likely Metamorphic Properties to Support Differential Testing

Metamorphic testing is an advanced technique to test programs without a true test oracle such as machine learning applications. Because these programs have no general oracle to identify their correctness, traditional testing techniques such as unit testing may not be helpful for developers to detect potential bugs. This paper presents a novel system, Kabu, which can dynamically infer properties of methods' states in programs that describe the characteristics of a method before and after transforming its input. These Metamorphic Properties (MPs) are pivotal to detecting potential bugs in programs without test oracles, but most previous work relies solely on human effort to identify them and only considers MPs between input parameters and output result (return value) of a program or method. This paper also proposes a testing concept, Metamorphic Differential Testing (MDT). By detecting different sets of MPs between different versions for the same method, Kabu reports potential bugs for human review. We have performed a preliminary evaluation of Kabu by comparing the MPs detected by humans with the MPs detected by Kabu. Our preliminary results are promising: Kabu can find more MPs than human developers, and MDT is effective at detecting function changes in methods.
OpenGV: A unified and generalized approach to real-time calibrated geometric vision

OpenGV is a new C++ library for calibrated realtime 3D geometric vision. It unifies both central and non-central absolute and relative camera pose computation algorithms within a single library. Each problem type comes with minimal and non-minimal closed-form solvers, as well as non-linear iterative optimization and robust sample consensus methods. OpenGV therefore contains an unprecedented level of completeness with regard to calibrated geometric vision algorithms, and it is the first library with a dedicated focus on a unified real-time usage of non-central multi-camera systems, which are increasingly popular in robotics and in the automotive industry. This paper introduces OpenGV's flexible interface and abstraction for multi-camera systems, and outlines the performance of all contained algorithms. It is our hope that the introduction of this open-source platform will motivate people to use it and potentially also include more algorithms, which would further contribute to the general accessibility of geometric vision algorithms, and build a common playground for the fair comparison of different solutions.
Malayalam word sense disambiguation

This paper presents an outline of our work to develop a word sense disambiguation system in Malayalam. Word sense disambiguation (WSD) is a linguistically based mechanism for automatically defining the correct sense of a word in the context. WSD is a long standing problem in computational linguistics. A particular word may have different meanings in different contexts. For human beings, it is easy to extract the correct meaning by analyzing the sentences. In the area of natural language processing, we are trying to simulate all of these human capabilities with a computer system. In many natural language processing tasks such as machine translation, information retrieval etc., Word Sense Disambiguation plays an important role to improve the quality of systems.
The FrameNet Constructicon

The Berkeley  Project1 has been engaged since 1997 in discovering and describing the semantic and distributional properties of words in the general vocabulary of English.2 Notions from FRAME SEMANTICS (see Fillmore and Baker 2009 and references therein) provide the basis of the semantic description of the lexical units in the database, and sentences extracted from the FrameNet (FN) text corpora3 serve as material for analysis and annotation. The goal is to describe the combinatorial properties of each word, both semantically and syntactically, as these properties are revealed in the corpora. The present chapter reviews some of the principles and displays some of the results of FrameNet’s lexical research but focuses on an appended project for recognizing and cataloguing GRAMMATICAL CONSTRUCTIONS in English. The registry of English constructions to be created by this secondary project—the Constructicon—will describe the grammatical characteristics and semantic import of each construction, and will link to each a collection of sample sentences that have been annotated to exhibit these characteristics, using tools developed for the earlier lexical work. While building a Constructicon has different goals from those of designing a construction-based grammar of the language, the intention is that each construction will be represented in a way compatible with the development of full grammar of the language of the sort presented elsewhere in this volume (see especially Sag this volume). In some cases we offer precise proposals for the treatment of a construction as it would appear in the grammar; in other cases the descriptions we present should be seen at least as organized observations about individual constructions, observations that need to be accounted for in a future complete grammar. In all cases we expect that the constructicon will contain useful information for advanced language pedagogy and that it will suggest new levels of expectation for parsing and other NLP activities. The main body of the finished Constructicon will display the properties of constructional phenomena in an abbreviated format, alongside of a representative sample of English sentences annotated to display the properties claimed for each construction. The annotation procedure follows a method of identifying and labeling phrases originally developed for FN’s lexicographic activities but adapted to indicate (1) the stretches of language that count as instances of given constructions (e.g. the phrase bracketed in { } in They
An Anatomic Basis for Treatment of Retinal Artery Occlusions Caused by Hyaluronic Acid Injections: A Cadaveric Study

In ophthalmic artery occlusion by hyaluronic acid injection, the globe may get worse by direct intravitreal administration of hyaluronidase. Retrograde cannulation of the ophthalmic artery may have the potential for restoration of retinal perfusion and minimizing the risk of phthisis bulbi. The study investigated the feasibility of cannulation of the ophthalmic artery for retrograde injection. In 10 right orbits of 10 cadavers, cannulation and ink injection of the supraorbital artery in the supraorbital approach were performed under surgical loupe magnification. In 10 left orbits, the medial upper lid was curvedly incised to retrieve the retroseptal ophthalmic artery for cannulation by a transorbital approach. Procedural times were recorded. Diameters of related arteries were bilaterally measured for comparison. Dissections to verify dye distribution were performed. Cannulation was successfully performed in 100 % and 90 % of the transorbital and the supraorbital approaches, respectively. The transorbital approach was more practical to perform compared with the supraorbital approach due to a trend toward a short procedure time (18.4 ± 3.8 vs. 21.9 ± 5.0 min, p = 0.74). The postseptal ophthalmic artery exhibited a tortious course, easily retrieved and cannulated, with a larger diameter compared to the supraorbital artery (1.25 ± 0.23 vs. 0.84 ± 0.16 mm, p = 0.000). The transorbital approach is more practical than the supraorbital approach for retrograde cannulation of the ophthalmic artery. This study provides a reliable access route implication for hyaluronidase injection into the ophthalmic artery to salvage central retinal occlusion following hyaluronic acid injection. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .