nils-herrmann/clean_scirepeval huggingface dataset:
Internal Coupled-Fed Shorted Monopole Antenna for GSM850/900/1800/1900/UMTS Operation in the Laptop Computer

A simple shorted strip monopole with a coupling feed for penta-band WWAN (Wireless Wide Area Network) operation in the laptop computer is presented. With the coupling feed, which effectively compensates for the large inductive reactance of the antenna, a dual-resonance excitation for the antenna's lower band at about 1 GHz is achieved; the obtained bandwidth reaches 500 MHz, allowing the antenna to easily cover GSM850/900 operation. Also, a wide operating band is achieved for the antenna's upper band at about 1.9 GHz with a bandwidth of 540 MHz, hence easily covering GSM1800/1900/UMTS operation. With two wide operating bands achieved, the antenna occupies a thickness of 5 mm only and a small area of 9 times 79 mm2 (not including the ground plane), making it very promising to be embedded inside the casing of the laptop computer as an internal WWAN antenna. The proposed antenna is studied in detail in this study.
Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.
Online Social Networks anatomy: On the analysis of Facebook and WhatsApp in cellular networks

Online Social Networks (OSNs) have rapidly become an integral part of our daily lives, and hundreds of millions of people are nowadays remotely connected trough popular OSNs such as Facebook, Google+, Twitter and WhatsApp. While much has been said and studied about the social aspects of OSNs, little is known about the network side of OSNs, specially regarding their network and traffic footprints, as well as their content delivery infrastructures. In this paper we study these networking aspects of OSNs, vivisecting the most popular OSNs in western countries: Facebook and WhatsApp. By analyzing two large-scale traffic traces collected at the cellular network of a major European ISP, we characterize and compare the networking behavior of Facebook and WhatsApp, considering not only the traffic flows but also the network infrastructures hosting them. Our study serves the main purpose of better understanding how major OSNs are provisioned in today's Internet. To the best of our knowledge, this is the first paper providing such an analysis using large-scale measurements in cellular networks.
Anaplastology in times of facial transplantation: Still a reasonable treatment option?

Optimum functional and aesthetic facial reconstruction is still a challenge in patients who suffer from inborn or acquired facial deformity. It is known that functional and aesthetic impairment can result in significant psychosocial strain, leading to the social isolation of patients who are affected by major facial deformities. Microvascular techniques and increasing experience in facial transplantation certainly contribute to better restorative outcomes. However, these technologies also have some drawbacks, limitations and unsolved problems. Extensive facial defects which include several aesthetic units and dentition can be restored by combining dental prostheses and anaplastology, thus providing an adequate functional and aesthetic outcome in selected patients without the drawbacks of major surgical procedures. Referring to some representative patient cases, it is shown how extreme facial disfigurement after oncological surgery can be palliated by combining intraoral dentures with extraoral facial prostheses using individualized treatment and without the need for major reconstructive surgery.
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares

An iterative method is given for solving Ax ~ffi b and minU Ax b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable numerical properties. Reliable stopping criteria are derived, along with estimates of standard errors for x and the condition number of A. These are used in the FORTRAN implementation of the method, subroutine LSQR. Numerical tests are described comparing I~QR with several other conjugate-gradient algorithms, indicating that I~QR is the most reliable algorithm when A is ill-conditioned.
Deep Recurrent Regression for Facial Landmark Detection

We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature maps via a deep network with stacked convolutional and deconvolutional layers. Then, in the second part, we estimate the initial coordinates of the facial key points by an additional convolutional layer on top of these deconvolutional feature maps. In the last part, by using the deconvolutional feature maps and the initial facial key points as input, we refine the coordinates of the facial key points by a recurrent network that consists of multiple long short-term memory components. Extensive evaluations on several benchmark data sets show that the proposed deep architecture has superior performance against the state-of-the-art methods.
Does knowledge based psychological ownership matter ? Transformational leadership and knowledge hiding : A proposed framework

The prevalent and negative impact of knowledge hiding behaviour among employees has called the attention of both practitioners and researchers to begin investigations on its causes, and other factors that influence it on both the targets and perpetrators. Some of the possible suggested antecedents of knowledge hiding studied include trust, job characteristic, organisational culture, and workplace environment. Despite these studies, few have attempted to investigate the influence of leadership, particularly, transformational leadership on knowledge hiding. Previous studies also indicated conflicting results, and the mechanisms through which transformational leadership influences knowledge hiding are not adequately addressed. Therefore, mediator variable is suggested. Hence, this paper proposes knowledge psychological ownership as a potential mediator on the relationship between transformational leadership and knowledge hiding behaviour of an employee in the organization. We hereby argue that transformational leadership through knowledge psychological ownership will influence employees to refrain from the knowledge hiding behaviour that may negatively affect the organisation and encourage knowledge sharing to improve the performance of the organization.  Sahnun Ladan Email address for the corresponding author: sahnunl@yahoo.com First submission received: 28th January 2017 Revised submission received: 21st February 2017  8th April 2017
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions

Following your need to always fulfil the inspiration to obtain everybody is now simple. Connecting to the internet is one of the short cuts to do. There are so many sources that offer and connect us to other world condition. As one of the products to see in internet, this website becomes a very available place to look for countless ensemble methods in data mining improving accuracy through combining predictions synthesis lectures on data mining and knowledge discovery sources. Yeah, sources about the books from countries in the world are provided.
Plasmonic Luneburg and Eaton lenses.

Plasmonics takes advantage of the properties of surface plasmon polaritons, which are localized or propagating quasiparticles in which photons are coupled to the quasi-free electrons in metals. In particular, plasmonic devices can confine light in regions with dimensions that are smaller than the wavelength of the photons in free space, and this makes it possible to match the different length scales associated with photonics and electronics in a single nanoscale device. Broad applications of plasmonics that have been demonstrated to date include biological sensing, sub-diffraction-limit imaging, focusing and lithography and nano-optical circuitry. Plasmonics-based optical elements such as waveguides, lenses, beamsplitters and reflectors have been implemented by structuring metal surfaces or placing dielectric structures on metals to manipulate the two-dimensional surface plasmon waves. However, the abrupt discontinuities in the material properties or geometries of these elements lead to increased scattering of surface plasmon polaritons, which significantly reduces the efficiency of these components. Transformation optics provides an alternative approach to controlling the propagation of light by spatially varying the optical properties of a material. Here, motivated by this approach, we use grey-scale lithography to adiabatically tailor the topology of a dielectric layer adjacent to a metal surface to demonstrate a plasmonic Luneburg lens that can focus surface plasmon polaritons. We also make a plasmonic Eaton lens that can bend surface plasmon polaritons. Because the optical properties are changed gradually rather than abruptly in these lenses, losses due to scattering can be significantly reduced in comparison with previously reported plasmonic elements.
Event Detection and Domain Adaptation with Convolutional Neural Networks

We study the event detection problem using convolutional neural networks (CNNs) that overcome the two fundamental limitations of the traditional feature-based approaches to this task: complicated feature engineering for rich feature sets and error propagation from the preceding stages which generate these features. The experimental results show that the CNNs outperform the best reported feature-based systems in the general setting as well as the domain adaptation setting without resorting to extensive external resources.
Extraction of Significant Patterns from Heart Disease Warehouses for Heart Attack Prediction

The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various factors or symptoms is a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. The healthcare industry gathers enormous amounts of heart disease data that regrettably, are not “mined” to determine concealed information for effective decision making by healthcare practitioners. In this paper, we have proposed an efficient approach for the extraction of significant patterns from the heart disease warehouses for heart attack prediction. Initially, the data warehouse is preprocessed to make it appropriate for the mining process. After preprocessing, the heart disease warehouse is clustered using the K-means clustering algorithm, which will extract the data relevant to heart attack from the warehouse. Subsequently the frequent patterns are mined from the extracted data, relevant to heart disease, using the MAFIA algorithm. Then the significant weightage of the frequent patterns are calculated. Further, the patterns significant to heart attack prediction are chosen based on the calculated significant weightage. These significant patterns can be used in the development of heart attack prediction system.
Influence of PEEK Coating on Hip Implant Stress Shielding: A Finite Element Analysis

Stress shielding is a well-known failure factor in hip implants. This work proposes a design concept for hip implants, using a combination of metallic stem with a polymer coating (polyether ether ketone (PEEK)). The proposed design concept is simulated using titanium alloy stems and PEEK coatings with thicknesses varying from 100 to 400 μm. The Finite Element analysis of the cancellous bone surrounding the implant shows promising results. The effective von Mises stress increases between 81 and 92% for the complete volume of cancellous bone. When focusing on the proximal zone of the implant, the increased stress transmission to the cancellous bone reaches between 47 and 60%. This increment in load transferred to the bone can influence mineral bone loss due to stress shielding, minimizing such effect, and thus prolonging implant lifespan.
Abstractive Multi-Document Summarization : An Overview

In recent times, the necessity of generating single document summary has gained popularity among the researchers due to its extensive applicability. The text summarization can be categorized with different approaches like: extractive and abstractive from single document or multi document, goal of text summarization (intent, focus and coverage), characteristic of text summarization (frequency-based, knowledge-based and discoursebased), level of processing (surface level, entities level and discourse level) and kind of information (lexicon, structure information and deep understanding). Recently, the efforts in research are transferred from single document summarization to multi document summarization Multi-document summarization considerably differs from single in issues related to compression, speed, redundancy and passage selection are critical in the formation of useful summaries. In this paper, we review the techniques that have done for multi document summarization. Next, we describe evaluation method In conclusion; we propose our future work for multi document
The origin of bursts and heavy tails in human dynamics

The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behaviour into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. In contrast, there is increasing evidence that the timing of many human activities, ranging from communication to entertainment and work patterns, follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. Here I show that the bursty nature of human behaviour is a consequence of a decision-based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, with most tasks being rapidly executed, whereas a few experience very long waiting times. In contrast, random or priority blind execution is well approximated by uniform inter-event statistics. These finding have important implications, ranging from resource management to service allocation, in both communications and retail.
Using inaccurate models in reinforcement learning

In the model-based policy search approach to reinforcement learning (RL), policies are found using a model (or "simulator") of the Markov decision process. However, for high-dimensional continuous-state tasks, it can be extremely difficult to build an accurate model, and thus often the algorithm returns a policy that works in simulation but not in real-life. The other extreme, model-free RL, tends to require infeasibly large numbers of real-life trials. In this paper, we present a hybrid algorithm that requires only an approximate model, and only a small number of real-life trials. The key idea is to successively "ground" the policy evaluations using real-life trials, but to rely on the approximate model to suggest local changes. Our theoretical results show that this algorithm achieves near-optimal performance in the real system, even when the model is only approximate. Empirical results also demonstrate that---when given only a crude model and a small number of real-life trials---our algorithm can obtain near-optimal performance in the real system.
Chapter 9 Communication Avoiding ( CA ) and Other Innovative Algorithms

In 1981 Hong and Kung proved a lower bound on the amount of communication (amount of data moved between a small, fast memory and large, slow memory) needed to perform dense, n-by-n matrix-multiplication using the conventional O(n) algorithm, where the input matrices were too large to fit in the small, fast memory. In 2004 Irony, Toledo and Tiskin gave a new proof of this result and extended it to the parallel case (where communication means the amount of data moved between processors). In both cases the lower bound may be expressed as Ω(#arithmetic operations / √ M ), where M is the size of the fast memory (or local memory in the parallel case). Here we generalize these results to a much wider variety of algorithms, including LU factorization, Cholesky factorization, LDL factorization, QR factorization, Gram–Schmidt algorithm, algorithms for eigenvalues and singular values, i.e., essentially all direct methods of linear algebra. The proof works for dense or sparse matrices, and for sequential or parallel algorithms. In addition to lower bounds on the amount of data moved (bandwidth-cost), we get lower bounds on the number of messages required to move it (latency-cost). We extend our lower bound technique to compositions of linear algebra operations (like computing powers of a matrix), to decide whether it is enough to call a sequence of simpler optimal algorithms (like matrix multiplication) to minimize communication, or if we can do better. We give examples of both. We also show how to extend our lower bounds to certain graph theoretic problems. We point out recently designed algorithms that attain many of these lower bounds.
A Survey of Procedural Noise Functions

Procedural noise functions are widely used in Computer Graphics, from off-line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This survey is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry, and the fact that many recent research developments justify the need for an up-to-date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modeling and how they are applied to surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation. We finally identify several directions for future work.
Howard: A Dynamic Excavator for Reverse Engineering Data Structures

Even the most advanced reverse engineering techniques and products are weak in recovering data structures in stripped binaries—binaries without symbol tables. Unfortunately, forensics and reverse engineering without data structures is exceedingly hard. We present a new solution, known as Howard, to extract data structures from C binaries without any need for symbol tables. Our results are significantly more accurate than those of previous methods — sufficiently so to allow us to generate our own (partial) symbol tables without access to source code. Thus, debugging such binaries becomes feasible and reverse engineering becomes simpler. Also, we show that we can protect existing binaries from popular memory corruption attacks, without access to source code. Unlike most existing tools, our system uses dynamic analysis (on a QEMU-based emulator) and detects data structures by tracking how a program uses memory.
On Arithmetic Progressions Of Cycle Lengths In Graphs

A recently posed question of Häggkvist and Scott’s asked whether or not there exists a constant c such that if G is a graph of minimum degree ck then G contains cycles of k consecutive even lengths. In this paper we answer the question by proving that for k ≥ 2, a bipartite graph of average degree at least 4k and girth g contains cycles of (g/2 − 1)k consecutive even lengths. We also obtain a short proof of the theorem of Bondy and Simonovits, that a graph of order n and size at least 8(k− 1)n has a cycle of length 2k. Erdős and Burr [4] conjectured that for every odd number k, there is a constant ck such that for every natural number m, every graph of average degree at least ck contains a cycle of length m modulo k. Erdős and Burr  settled their conjecture in the case m = 2 and Robertson (see ) settled the case m = 0. The full conjecture was resolved by Bollobás , who proved the conjecture with ck = 2[(k + 1) k − 1]/k. In this paper, we show that ck = 8k will do. Thomassen [11] later showed cycles of all even lengths modulo k are obtained under the hypothesis that the average degree is at least 4k(k + 1), without requiring k to be odd.  also proved that if G is a graph of minimum degree at least three and girth at least 2(k2 +1)(3 ·2k +1 +(k2 +1)2 −1), then G contains cycles of all even lengths modulo k.
A simple PEM fuel cell emulator using electrical circuit model

This paper presents the development of a simple emulator for proton exchange membrane (PEM) fuel cell. This emulator is built based on the dynamic model of PEM fuel cells using electrical circuits. The electrical circuit model has been modeled in Matlab Simulink environments. The double-layer charging effect characteristic inside the fuel cell is included in the models. The steady-state and transient response of the model is investigated. Emulator performance is validated using experimental data from a 500-W commercial PEM fuel-cell stack. The emulator could be used in PEM fuel-cell control related studies.