nils-herrmann/clean_scirepeval huggingface dataset:
A Review of Methodological Approaches for the Design and Optimization of Wind Farms

This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research.
Field effect diode for effective CDM ESD protection in 45 nm SOI technology

In this paper, the improved field-effect diode (FED) has been characterized and modeled in 45 nm silicon-on-insulator (SOI) technology. It has been experimentally shown to be suitable for pad-based local clamping under normal supply voltage (Vdd) range (below 1 V) in high-speed integrated circuits. ESD protection capabilities are investigated using very fast transmission line pulse (VF-TLP) tests to predict the device's performance in charged device model (CDM) ESD events. The FED's advantages in improving transient turn-on behavior and reducing DC leakage current have been analyzed and compared with other Silicon-Controlled-Rectifier (SCR)-based SOI device variations. Technology CAD (TCAD) simulations are used to interpret the turn-on behavior and the physical effects. Process tradeoffs have been evaluated. The work prepares the device for being directly applied to high-speed Input/Output (I/O) circuit and it addresses the severe challenge in CDM ESD protection. The improved device enables the adoption of local clamping scheme that expands the ESD design window.
3D Human Body Reconstruction from a Single Image via Volumetric Regression

This paper proposes the use of an end-to-end Convolutional Neural Network for direct reconstruction of the 3D geometry of humans via volumetric regression. The proposed method does not require the fitting of a shape model and can be trained to work from a variety of input types, whether it be landmarks, images or segmentation masks. Additionally, non-visible parts, either self-occluded or otherwise, are still reconstructed, which is not the case with depth map regression. We present results that show that our method can handle both pose variation and detailed reconstruction given appropriate datasets for training.
Hypothesizing the Aptness of Social Media and the Information Richness Requirements of Disaster Management

In this article, the author first analyzes the social presence theory, media richness theory and taskmedia fit to investigate the suitability of various types of Social Media in disaster management. Then, on the basis of this analysis, use of social media is proposed to facilitate the communication tasks involved in the interaction between disaster management agencies and communities during disaster management. Next the author adapt a conceptual framework that integrates three types of communication (involving disaster management agencies and communities). The framework is further used as a springboard to develop a number of hypotheses to predict the aptness of rich and lean types of Social Media against the media richness requirements of disaster management tasks.
Being John Malkovich

Given a photo of person A, we seek a photo of person B with similar pose and expression. Solving this problem enables a form of puppetry, in which one person appears to control the face of another. When deployed on a webcam-equipped computer, our approach enables a user to control another person’s face in real-time. This image-retrievalinspired approach employs a fully-automated pipeline of face analysis techniques, and is extremely general—we can puppet anyone directly from their photo collection or videos in which they appear. We show several examples using images and videos of celebrities from the Internet.
Morpholexical and Discriminative Language Models for Turkish Automatic Speech Recognition

This paper introduces two complementary language modeling approaches for morphologically rich languages aiming to alleviate out-of-vocabulary (OOV) word problem and to exploit morphology as a knowledge source. The first model, morpholexical language model, is a generative $n$-gram model, where modeling units are lexical-grammatical morphemes instead of commonly used words or statistical sub-words. This paper also proposes a novel approach for integrating the morphology into an automatic speech recognition (ASR) system in the finite-state transducer framework as a knowledge source. We accomplish that by building a morpholexical search network obtained by the composition of lexical transducer of a computational lexicon with a morpholexical language model. The second model is a linear reranking model trained discriminatively with a variant of the perceptron algorithm using morpholexical features. This variant of the perceptron algorithm, WER-sensitive perceptron, is shown to perform better for reranking $n$ -best candidates obtained with the generative model. We apply the proposed models in Turkish broadcast news transcription task and give experimental results. The morpholexical model leads to an elegant morphology-integrated search network with unlimited vocabulary. Thus, it is highly effective in alleviating OOV problem and improves the word error rate (WER) over word and statistical sub-word models by 1.8% and 0.4% absolute, respectively. The discriminatively trained morpholexical model further improves the WER of the system by 0.8% absolute.
An ensemble classifier with random projection for predicting multi-label protein subcellular localization

In protein subcellular localization prediction, a predominant scenario is that the number of available features is much larger than the number of data samples. Among the large number of features, many of them may contain redundant or irrelevant information, causing the prediction systems suffer from overfitting. To address this problem, this paper proposes a dimensionality-reduction method that applies random projection (RP) to construct an ensemble multi-label classifier for predicting protein subcellular localization. Specifically, the frequencies of occurrences of gene-ontology terms are used as feature vectors, which are projected onto lower-dimensional spaces by random projection matrices whose elements conform to a distribution with zero mean and unit variance. The transformed low-dimensional vectors are classified by an ensemble of one-vs-rest multi-label support vector machine (SVM) classifiers, each corresponding to one of the RP matrices. The scores obtained from the ensemble are then fused for making the final decision. Experimental results on two recent datasets suggest that the proposed method can reduce the dimensions by six folds and remarkably improve the classification performance.
Efficient Character Skew Rectification in Scene Text Images

We present an efficient method for character skew rectification in scene text images. The method is based on a novel skew estimators, which exploit intuitive glyph properties and which can be efficiently computed in a linear time. The estimators are evaluated on a synthetically generated data (including Latin, Cyrillic, Greek, Runic scripts) and real scene text images, where the skew rectification by the proposed method improves the accuracy of a state-of-the-art scene text recognition pipeline.
Fast data stream algorithms using associative memories

The primary goal of data stream research is to develop space and time efficient solutions for answering continuous on-line summarization queries. Research efforts over the last decade have resulted in a number of efficient algorithms with varying degrees of space and time complexities. While these techniques are developed in a standard CPU setting, many of their applications such as click-fraud detection and network-traffic summarization typically execute on special networking architectures called Network Processing Units (NPUs). These NPUs interface with special associative memories known as Ternary Content Addressable Memories (TCAMs) to provide gigabit rate forwarding at network routers. In this paper, we describe how the integrated architecture of NPU and TCAMs can be exploited towards achieving the goal of developing high-speed stream summarization solutions. We propose two TCAM-conscious solutions for the frequent elements problem in data streams and present a comprehensive evaluation of these techniques on a state-of-the-art networking platform.
Evolving a New Software Development Life Cycle Model SDLC-2013 with Client Satisfaction

216  In the era of software development there exist a large number of Models to develop software. Each model has its own characteristics, limitations and working environment. According to the requirements, software industry people use different models to develop different software. There are various models but none of them is capable to address the issues of client satisfaction. In this paper we develop a new model (SDLC-2013) for software development that lays special emphasis on client satisfaction and also tries to fulfil the objective of the Software Engineering of developing high quality product within schedule and budget. The new proposed model is designed in such a way that it allows client and developer to interact freely with each other in order to understand and implement requirements in a better way.
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions

In part I of this work we introduced and evaluated the G neralized Local Learning (GLL) framework for producing local causal and Markov blanket inductio n algorithms. In the present second part we analyze the behavior of GLL algorithms and provid e extensions to the core methods. Specifically, we investigate the empirical convergence of G LL to the true local neighborhood as a function of sample size. Moreover, we study how predictivi ty improves with increasing sample size. Then we investigate how sensitive are the algorithms t o multiple statistical testing, especially in the presence of many irrelevant features. Next we discuss the role of the algorithm parameters and also show that Markov blanket and causal graph concepts c an be used to understand deviations from optimality of state-of-the-art non-causal algorithm s. The present paper also introduces the following extensions to the core GLL framework: parallel an d distributed versions of GLL algorithms, versions with false discovery rate control, strate gies for constructing novel heuristics for specific domains, and divide-and-conquer local-to-global learning(LGL) strategies. We test the generality of the LGL approach by deriving a novel LGL-based algorithm that compares favorably c  In add ition, we investigate the use of non-causal feature selection methods to facilitate global learning. O pen problems and future research paths related to local and local-to-global causal learning are di scussed.
Performance analysis and security dependence of on paper digital signature using random and critical content

The content of physical paper documents is a major concern, after the development of modern scanning and printing technologies. The tendency for the document fraud has been growing and the paper-based documents require authenticating the sender identity and ensuring the content integrity. This paper presents a reliable and simple method for authenticating the content of a paper document by implementing an on paper digital signature. This method randomly selects the words at random positions of the document along with critical content and creates on paper digital signature. This method employs an online document verification system and the unauthorized access is denied by the user authentication mechanism. Performance analysis and security dependence on the system is also studied in this paper.
Estimator initialization in vision-aided inertial navigation with unknown camera-IMU calibration

This paper focuses on motion estimation using inertial measurements and observations of naturally occurring point features. To date, this task has primarily been addressed using filtering methods, which track the system state starting from known initial conditions. However, when no prior knowledge of the initial system state is available, (e.g., at the onset of the system's operation), the existing approaches are not applicable. To address this problem, in this work we present algorithms for computing the system's observable quantities (platform attitude and velocity, feature positions, and IMU-camera calibration) directly from the sensor measurements, without any prior knowledge. A key contribution of this work is a convex-optimization based algorithm for computing the rotation matrix between the camera and IMU. We show that once this rotation matrix has been computed, all remaining quantities can be determined by solving a quadratically constrained least-squares problem. To increase their accuracy, the initial estimates are refined by an iterative maximum-likelihood estimator.
Non-rigid Photometric Stereo with Colored Lights

We present an algorithm and the associated capture methodology to acquire and track the detailed 3D shape, bends, and wrinkles of deforming surfaces. Moving 3D data has been difficult to obtain by methods that rely on known surface features, structured light, or silhouettes. Multispec- tral photometric stereo is an attractive alternative because it can recover a dense normal field from an un-textured surface. We show how to capture such data and register it over time to generate a single deforming surface. Experiments were performed on video sequences of un- textured cloth, filmed under spatially separated red, green, and blue light sources. Our first finding is that using zero- depth-silhouettes as the initial boundary condition already produces rather smoothly varying per-frame reconstructions with high detail. Second, when these 3D reconstructions are augmented with 2D optical flow, one can register the first frame's reconstruction to every subsequent frame.
Recommender systems with social regularization

Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.
Dual-camera design for coded aperture snapshot spectral imaging.

Coded aperture snapshot spectral imaging (CASSI) provides an efficient mechanism for recovering 3D spectral data from a single 2D measurement. However, since the reconstruction problem is severely underdetermined, the quality of recovered spectral data is usually limited. In this paper we propose a novel dual-camera design to improve the performance of CASSI while maintaining its snapshot advantage. Specifically, a beam splitter is placed in front of the objective lens of CASSI, which allows the same scene to be simultaneously captured by a grayscale camera. This uncoded grayscale measurement, in conjunction with the coded CASSI measurement, greatly eases the reconstruction problem and yields high-quality 3D spectral data. Both simulation and experimental results demonstrate the effectiveness of the proposed method.
Haptic feedback for pen computing: directions and strategies

We seek to improve the xperience of using pen computing devices by augmenting them with haptic, tactile feedback displays. We present the design of the haptic display for pen computers, and explore interaction techniques that allow users to feel GUI elements, textures, photographs and other interface elements with a pen. We discuss research directions in haptic displays for pen devices and report results of an early experimental study that evaluated the benefits of tactile feedback in pen computing.
Complete Analytical Forward and Inverse Kinematics for the NAO Humanoid Robot

Articulated robots with multiple degrees of freedom, such as humanoid robots, have become popular research platforms in robotics and artificial intelligence. Such robots can perform complex motions, including the balancing, walking, and kicking skills required in the RoboCup robot soccer competition. The design of complex dynamic motions is achievable only through the use of robot kinematics, which is an application of geometry to the study of arbitrary robotic chains. This thesis studies the problems of forward and inverse kinematics for the Aldebaran NAO humanoid robot and presents for the first time a complete analytical solution to both problems with no approximations, including an implementation of a software library for real-time execution. The forward kinematics allow NAO developers to map any configuration of the robot from its own joint space to the three-dimensional physical space, whereas the inverse kinematics provide closedform solutions to finding joint configurations that drive the end effectors of the robot to desired points in the three-dimensional space. The proposed solution was made feasible through a decomposition into five independent problems (head, two arms, two legs), the use of the Denavit-Hartenberg method, and the analytical solution of a non-linear system of equations. The main advantage of the proposed inverse kinematics compared to existing numerical approaches is its accuracy, its efficiency, and the elimination of singularities. The implemented NAO kinematics library, which additionally offers centerof-mass calculation, is demonstrated in two motion design tasks: pointing to the ball and basic balancing. The library has been integrated into the software architecture of the RoboCup team “Kouretes” and is currently being used in various motion design problems, such as dynamic balancing, trajectory following, dynamic kicking, and omnidirectional walking.
The role of tutoring in problem solving.

THIS PAPER is concerned with the nature of the tutorial process; the means whereby an adult or "expert" helps somebody who is less adult or less expert. Though its aim is general, it is expressed in terms of a particular task: a tutor seeks to teach children aged 3, 4 and 5 yr to build a particular three-dimensional structure that requires a degree of skill that is initially beyond them. It is the usual type of tutoring situation in which one member "knows the answer" and the other does not, rather like a "practical" in which only the instructor "knows how". The changing interaction of tutor and children provide our data. A great deal of early problem solving by the developing child is of this order. Although from the earliest months of life he is a "natural" problem solver in his own right (e.g. Bruner, 1973) it is often the ease that his efforts are assisted and fostered by others who are more skilful than he is (Kaye, 1970). Whether he is learning the procedures that constitute the skills of attending, communicating, manipulating objects, locomoting, or, indeed, a more effective problem solving procedure itself, there are usually others in attendance who help him on his way. Tutorial interactions are, in short, a crucial feature of infancy and childhood. Our species, moreover, appears to be the only one in which any "intentional" tutoring goes on (Bruner, 1972; Hinde, 1971). For although it is true that many of the higher primate species learn by observation of their elders (Hamburg, 1968; van Lawick-Goodall, 1968), there is no evidence that those elders do anything to instruct their charges in the performance of the skill in question. What distinguishes man as a species is not only his capacity for learning, but for teaching as well. It is the main aim of this paper to examine some of the major implications of this interactive, instructional relationship between the developing child and his elders for the study of skill acquisition and problem solving. The acquisition of skill in the human child can be fruitfully conceived as a hierarchical program in which component skills are combined into "higher skills" by appropriate orchestration to meet new, more complex task requirements (Bruner, 1973). The process is analogous to problem solving in which mastery of "lower order" or constituent problems in a sine qua non for success with a larger jjroblcm, each level influencing the other—as with reading where the deciphering of words makes possible the deciphering of sentences, and sentences then aid in the deciphering of particular words (F. Smith, 1971). Given persistent intention in the young learner, given a "lexicon" of constituent skills, the crucial task is often one of com-
Autopoiesis, Structural Coupling and Cognition: A history of these and other notions in the biology of cognition

My intent in this essay is to reflect on the history of some biological notions such as autopoiesis, structural coupling, and cognition, that I have developed since the early 1960's as a result of my work on visual perception and the organization of the living. No doubt I shall repeat things that I have said in other publications (Maturana & Varela, 1980, 1988), and I shall present notions"that once they are said appear as obvious truisms. Moreover, I shall refine or expand the meaning of such notions, or even modify them. Yet, in any case, the reader is not invited to attend to the truisms, or to what seems to be obvious, rather he or she is invited to attend to the consequences that those notions entail for the understanding of cognition as a biological process. After all, explanations or demonstrations always become self evident once they are understood and accepted, and the purpose of this essay is the expansion of understanding in all dimensions of human existence.
Investigation of Rectenna Array Configurations for Enhanced RF Power Harvesting

RF power harvesting enables controllable and simultaneous wireless power delivery to many RF devices. Devices built with this unique technology can be sealed, embedded within structures, or made mobile, thus eliminating additional service for a battery. A key component of this technology is the "rectenna," which is composed of antennas and rectifying circuitry to convert RF energy into dc power. Typically, multiple rectenna elements are used to generate the dc power for reliable device operation. This letter compares two different rectenna architectures for maximum RF-to-dc power conversion efficiency. A simple rectenna design ex ample containing a 2 × 2 planar antenna array will be presented to demonstrate such RF power harvesting technology. The parameter, Rectenna Topology Indicator (RTI), is introduced for performance comparison.