nils-herrmann/clean_scirepeval huggingface dataset:
Digital Advertising: An Information Scientist's Perspective

Digital online advertising is a form of promotion that uses the Internet and World Wide Web for the express purpose of delivering marketing messages to attract customers. Examples of online advertising include text ads that appear on search engine results pages, banner ads, in-text ads, or Rich Media ads that appear on regular web pages, portals, or applications. Over the past 15 years online advertising, a $65 billion industry worldwide in 2009, has been pivotal to the success of the World Wide Web. That being said, the field of advertising has been equally revolutionized by the Internet, World Wide Web, and more recently, by the emergence of the social web, and mobile devices. This success has arisen largely from the transformation of the advertising industry from a low-tech, human intensive, “Mad Men” way of doing work to highly optimized, quantitative, mathematical, computerand data-centric processes that enable highly targeted, personalized, performance based advertising. This chapter provides a clear and detailed overview of the technologies and business models that are transforming the field of online advertising primarily from statistical machine learning and information science perspectives.
The Airport Ground Movement Problem : Past and Current Research and Future Directions

Determining efficient airport operations is an important and critical problem for airports, airlines, passengers and other stakeholders. Moreover, it is likely to become even more so given the traffic increases which are expected over the next few years. The ground movement problem forms the link between other airside problems, such as arrival sequencing, departure sequencing and gate/stand allocation. This paper provides an overview, categorisation and critical examination of the previous research for ground movement and highlights various important open areas of research. Of particular importance is the question of the integration of various airport operations and their relationships which are considered in this paper.
Efficient sparse-matrix multi-vector product on GPUs

Sparse Matrix-Vector (SpMV) and Sparse Matrix-Multivector (SpMM) products are key kernels for computational science and data science. While GPUs offer significantly higher peak performance and memory bandwidth than multicore CPUs, achieving high performance on sparse computations on GPUs is very challenging. A tremendous amount of recent research has focused on various GPU implementations of the SpMV kernel. But the multi-vector SpMM kernel has received much less attention. In this paper, we present an in-depth analysis to contrast SpMV and SpMM, and develop a new sparse-matrix representation and computation approach suited to achieving high data-movement efficiency and effective GPU parallelization of SpMM. Experimental evaluation using the entire SuiteSparse matrix suite demonstrates significant performance improvement over existing SpMM implementations from vendor libraries.
Illuminating Light: An Optical Design Tool with a Luminous-Tangible Interface

We describe a novel system for rapid prototyping of laserbased optical and holographic layouts. Users of this optical prototyping tool – called the Illuminating Light system – move physical representations of various optical elements about a workspace, while the system tracks these components and projects back onto the workspace surface the simulated propagation of laser light through the evolving layout. This application is built atop the Luminous Room infrastructure, an aggregate of interlinked, computer-controlled projector-camera units called I/O Bulbs. Philosophically, the work embodies the emerging ideas of the Luminous Room and builds on the notions of ‘graspable media’. We briefly introduce the I/O Bulb and Luminous Room concepts and discuss their current implementations. After an overview of the optical domain that the Illuminating Light system is designed to address, we present the overall system design and implementation, including that of an intermediary toolkit called voodoo which provides a general facility for object identification and tracking.
Anonymous Post-Quantum Cryptocash ? ( Full Version )

In this paper, we construct an anonymous and decentralized cryptocash system which is potentially secure against quantum computers. In order to achieve that, a linkable ring signature based on ideal lattices is proposed. The size of a signature in our scheme is O(logN), where N is the cardinality of the ring. The framework of our cryptocash system follows that of CryptoNote with some modifications. By adopting the short quantum-resistant linkable ring signature scheme, our system is anonymous and efficient. We also introduce how to generate the verifying and signing key pairs of the linkable ring signature temporarily. With these techniques, the privacy of users is protected, even though their transactions are recorded in the public ledger.
Facial Performance Capture with Deep Neural Networks

We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor’s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character.
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach

The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classifiers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products. Furthermore, this method scales well and allowed us to successfully perform domain adaptation on a larger industrial-strength dataset of 22 domains.
Learning From Semi-Supervised Weak-Label Data

Multi-label learning deals with data objects associated with multiple labels simultaneously. Previous studies typically assume that for each instance, the full set of relevant labels associated with each training instance is given. In many applications such as image annotation, however, it’s usually difficult to get the full label set for each instance and only a partial or even empty set of relevant labels is available. We call this kind of problem as ‘semi-supervised weak-label learning’ problem. In this work we propose the SSWL (Semi-Supervised Weak-Label) method to address this problem. Both instance similarity and label similarity are considered for the complement of missing labels. Ensemble of multiple models are utilized to improve the robustness when label information is insufficient. We formulate the objective as a bi-convex optimization problem with an efficient block coordinate descent algorithm. Experiments validate the effectiveness of SSWL.
Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents novel quantum-behaved PSO (QPSO) approaches using mutation operator with Gaussian probability distribution. The application of Gaussian mutation operator instead of random sequences in QPSO is a powerful strategy to improve the QPSO performance in preventing premature convergence to local optima. In this paper, new combinations of QPSO and Gaussian probability distribution are employed in well-studied continuous optimization problems of engineering design. Two case studies are described and evaluated in this work. Our results indicate that Gaussian QPSO approaches handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature.
Artificial Neural Network based Web Application Firewall for SQL Injection

In recent years with the rapid development of Internet and the Web, more and more web applications have been deployed in many fields and organizations such as finance, military, and government. Together with that, hackers have found more subtle ways to attack web applications. According to international statistics, SQL Injection is one of the most popular vulnerabilities of web applications. The consequences of this type of attacks are quite dangerous, such as sensitive information could be stolen or authentication systems might be by-passed. To mitigate the situation, several techniques have been adopted. In this research, a security solution is proposed using Artificial Neural Network to protect web applications against this type of attacks. The solution has been experimented on sample datasets and has given promising result. The solution has also been developed in a prototypic web application firewall called ANNbWAF. Artificial Neural Networks ANN, SQL Injection, Web Application Firewall WAF, Web Application Scanner WAS.
The Social Explanatory Styles Questionnaire: Assessing Moderators of Basic Social-Cognitive Phenomena Including Spontaneous Trait Inference, the Fundamental Attribution Error, and Moral Blame

Why is he poor? Why is she failing academically? Why is he so generous? Why is she so conscientious? Answers to such everyday questions--social explanations--have powerful effects on relationships at the interpersonal and societal levels. How do people select an explanation in particular cases? We suggest that, often, explanations are selected based on the individual's pre-existing general theories of social causality. More specifically, we suggest that over time individuals develop general beliefs regarding the causes of social events. We refer to these beliefs as social explanatory styles. Our goal in the present article is to offer and validate a measure of individual differences in social explanatory styles. Accordingly, we offer the Social Explanatory Styles Questionnaire (SESQ), which measures three independent dimensions of social explanatory style: Dispositionism, historicism, and controllability. Studies 1-3 examine basic psychometric properties of the SESQ and provide positive evidence regarding internal consistency, factor structure, and both convergent and divergent validity. Studies 4-6 examine predictive validity for each subscale: Does each explanatory dimension moderate an important phenomenon of social cognition? Results suggest that they do. In Study 4, we show that SESQ dispositionism moderates the tendency to make spontaneous trait inferences. In Study 5, we show that SESQ historicism moderates the tendency to commit the Fundamental Attribution Error. Finally, in Study 6 we show that SESQ controllability predicts polarization of moral blame judgments: Heightened blaming toward controllable stigmas (assimilation), and attenuated blaming toward uncontrollable stigmas (contrast). Decades of research suggest that explanatory style regarding the self is a powerful predictor of self-functioning. We think it is likely that social explanatory styles--perhaps comprising interactive combinations of the basic dimensions tapped by the SESQ--will be similarly potent predictors of social functioning. We hope the SESQ will be a useful tool for exploring that possibility.
Position sensing in brake-by-wire callipers using resolvers

Recent designs for brake-by-wire systems use "resolvers" to provide accurate and continuous measurements for the absolute position and speed of the rotor of the electric actuators in brake callipers (permanent magnet DC motors). Resolvers are absolute-angle transducers that are integrated with estimator modules called "angle tracking observer" and together they provide position and speed measurements. Current designs for angle-tracking observers are unstable in applications with high acceleration and/or speed. In this paper, we introduce a new angle-tracking observer in which a closed-loop linear time-invariant (LTI) observer is integrated with a quadrature encoder. Finite-gain stability of the proposed design and its robustness to three different kinds of parameter variations are proven based on theorems of input-output stability in nonlinear control theory. In our experiments, we examined the performance of our observer and two other methods (a well-known LTI observer and an extended Kalman filter) to estimate the position and speed of a brake-by-wire actuator. The results show that because of the very high speed and acceleration of the actuator in this application, the LTI observer and Kalman filter cannot track the rotor position and diverge. In contrast, with a properly designed open-loop transfer function and selecting a suitable switching threshold, our proposed angle-tracking observer is stable and highly accurate in a brake-by-wire application
Mindfulness-based cognitive therapy for depression: a new approach to preventing relapse.

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Designing web usability

Where you can find the designing web usability easily? Is it in the book store? On-line book store? are you sure? Keep in mind that you will find the book in this site. This book is very referred for you because it gives not only the experience but also lesson. The lessons are very valuable to serve for you, that's not about who are reading this designing web usability book. It is about this book that will give wellness for all people from many societies.
Fuzzy logic controller for an inverted pendulum system using quantum genetic optimization

In this paper, we propose a new generalized design methodology of intelligent robust fuzzy control systems based on quantum genetic algorithm (QGA) called quantum fuzzy model that enhance robustness of fuzzy logic controllers. The QGA is adopted because of their capabilities of directed random search for global optimization to find the parameters of the shape and width of membership functions and rule set of the FLC to obtain the optimal fuzzy controller simultaneously. We test the optimal FLC obtained by the quantum computing applied on the control of dynamic balance and motion of cart-pole balancing system. We compare the proposed technique with existing mamdani fuzzy logic controller which is designed through conventional genetic algorithm. Simulation results reveal that QGA performs better than conventional GA in terms of running speed and optimizing capability.
Analysis of a microstrip patch array fed cylindric lens antenna for 77GHz automotive radar

Regarding gain and SLL, the new lens antenna concept has better focusing abilities compared to the column antenna. Pattern perturbations due to lens edge effects were analyzed with regard to digital array processing. They can be confined either with an appropriate choice for the lens length L and the xlscr,i of the radar sensorpsilas receiving ULA or with narrower feeding beams in the H-plane.
Attribute Extraction from Product Titles in eCommerce

This paper presents a named entity extraction system for detecting attributes in product titles of eCommerce retailers like Walmart. The absence of syntactic structure in such short pieces of text makes extracting attribute values a challenging problem. We find that combining sequence labeling algorithms such as Conditional Random Fields and Struc-tured Perceptron with a curated normalization scheme produces an effective system for the task of extracting product attribute values from titles. To keep the discussion concrete, we will illustrate the mechanics of the system from the point of view of a particular attribute-brand. We also discuss the importance of an attribute extraction system in the context of retail websites with large product catalogs, compare our approach to other potential approaches to this problem and end the paper with a discussion of the performance of our system for extracting attributes.
Introduction to Linear and Nonlinear Programming

In undergoing this life, many people always try to do and get the best. New knowledge, experience, lesson, and everything that can improve the life will be done. However, many people sometimes feel confused to get those things. Feeling the limited of experience and sources to be better is one of the lacks to own. However, there is a very simple thing that can be done. This is what your teacher always manoeuvres you to do this one. Yeah, reading is the answer. Reading a book as this introduction to linear and nonlinear programming and other references can enrich your life quality. How can it be?
Co-RPL: RPL routing for mobile low power wireless sensor networks using Corona mechanism

Mobility support for wireless sensor networks has always been a challenging research topic. This paper addresses the issue of mobility support in the Routing Protocol for Low power and lossy networks (RPL), the recently adopted IETF routing protocol standard for low power wireless sensor networks. RPL was originally designed for static networks, with no support for mobility. In this work, we address this gap and propose Co-RPL as an extension to RPL based on the Corona mechanism to support mobility. To demonstrate the effectiveness of Co-RPL, we conducted an extensive simulation study using the Contiki/Cooja simulator and compared the performance against standard RPL. We study the impact of node speed, packet transmission rate and number of Directed Acyclic Graphs (DAG) roots on network performance. The simulation results show that Co-RPL decreases packet loss ratio by 45%, average energy consumption by 50% and end-to-end delay by 2.5 seconds, in comparison with the standard RPL.
Landing page component classification with convolutional neural networks for online advertising

Pages on digital platforms used for online advertising in order to attract customer attention for a target product are called landing pages. The aim of landing pages is to increase advertisement conversion rates using metrics like clicks, views or subscriptions. In this study, a method is presented to automatically detect the most commonly used components on landing pages; buttons, texts and checkboxes. Landing page images given as inputs, are segmented by morphological and thresholding-based image analysis methods, and each segment is classified using Convolutional Neural Networks (CNN). The proposed method is anticipated to be an important step in the process of automatically designing landing pages with high advertisement conversion rates by segmenting pages into components that have higher performance metrics. In preliminary experiments, high accuracy is achieved in the test data set.
Emerging late adolescent friendship networks and Big Five personality traits: a social network approach.

The current study focuses on the emergence of friendship networks among just-acquainted individuals, investigating the effects of Big Five personality traits on friendship selection processes. Sociometric nominations and self-ratings on personality traits were gathered from 205 late adolescents (mean age=19 years) at 5 time points during the first year of university. SIENA, a novel multilevel statistical procedure for social network analysis, was used to examine effects of Big Five traits on friendship selection. Results indicated that friendship networks between just-acquainted individuals became increasingly more cohesive within the first 3 months and then stabilized. Whereas individuals high on Extraversion tended to select more friends than those low on this trait, individuals high on Agreeableness tended to be selected more as friends. In addition, individuals tended to select friends with similar levels of Agreeableness, Extraversion, and Openness.
Understanding of DC leakage and faults in floating photovoltaic systems: Trojan horse to undermine metallic infrastructure and safety

This article elaborates on a recently published issue that calls the entire PV community's and utility management attention. It specifically provides further analysis on DC leakage and fault detection blind spots associated with the operation of floating PV Systems. Floating systems are almost a standard practice in Europe when it comes to the operation of large-scale PV parks. A floating PV system has neither the positive nor the negative DC current-carrying conductors connected to earth. The system nevertheless benefits from other earthing connections, including the bonding to earth of exposed metals (e.g. metal frames of the PV modules, supporting infrastructure, combiner boxes) that could become energised in a fault situation or even under normal operation due to DC coupling mechanisms. These may entail safety as well as accelerated DC corrosion concerns.
Harmonic Path (HAPA) algorithm for non-contact vital signs monitoring with IR-UWB radar

We introduce the Harmonic Path (HAPA) algorithm for estimation of heart rate (HR) and respiration rate (RR) with Impulse Radio Ultrawideband (IR-UWB) radar. A well known result is that a periodic movement, such as the lung wall or heart wall movement, induces a fundamental frequency and its harmonics. IR-UWB enables capture of these spectral components and frequency domain processing enables a low cost implementation. Most existing methods try to identify the fundamental component to estimate the HR and/or RR. However, often the fundamental is distorted or cancelled by interference, such as RR harmonics interference on the HR fundamental, leading to significant error for HR estimation. HAPA is the first reported algorithm to take advantage of the HR harmonics, where there is less interference, to achieve more reliable and robust estimation of the fundamental frequency. Example experimental results for HR estimation demonstrate how our algorithm eliminates errors caused by interference.