nils-herrmann/clean_scirepeval huggingface dataset:
VirtualTable: a projection augmented reality game

VirtualTable is a projection augmented reality installation where users are engaged in an interactive tower defense game. The installation runs continuously and is designed to attract people to a table, which the game is projected onto. Any number of players can join the game for an optional period of time. The goal is to prevent the virtual stylized soot balls, spawning on one side of the table, from reaching the cheese. To stop them, the players can place any kind of object on the table, that then will become part of the game. Depending on the object, it will become either a wall, an obstacle for the soot balls, or a tower, that eliminates them within a physical range. The number of enemies is dependent on the number of objects in the field, forcing the players to use strategy and collaboration and not the sheer number of objects to win the game.
ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control

In this paper, we present a new humanoid robot currently being developed for applications in human-centered environments. In order for humanoid robots to enter human-centered environments, it is indispensable to equip them with manipulative, perceptive and communicative skills necessary for real-time interaction with the environment and humans. The goal of our work is to provide reliable and highly integrated humanoid platforms which on the one hand allow the implementation and tests of various research activities and on the other hand the realization of service tasks in a household scenario. We introduce the different subsystems of the robot. We present the kinematics, sensors, and the hardware and software architecture. We propose a hierarchically organized architecture and introduce the mapping of the functional features in this architecture into hardware and software modules. We also describe different skills related to real-time object localization and motor control, which have been realized and integrated into the entire control architecture
Opportunistic networking: data forwarding in disconnected mobile ad hoc networks

Opportunistic networks are one of the most interesting evolutions of MANETs. In opportunistic networks, mobile nodes are enabled to communicate with each other even if a route connecting them never exists. Furthermore, nodes are not supposed to possess or acquire any knowledge about the network topology, which (instead) is necessary in traditional MANET routing protocols. Routes are built dynamically, while messages are en route between the sender and the destination(s), and any possible node can opportunistically be used as next hop, provided it is likely to bring the message closer to the final destination. These requirements make opportunistic networks a challenging and promising research field. In this article we survey the most interesting case studies related to opportunistic networking and discuss and organize a taxonomy for the main routing and forwarding approaches in this challenging environment. We finally envision further possible scenarios to make opportunistic networks part of the next-generation Internet
Steganalysis using image quality metrics

We present techniques for steganalysis of images that have been potentially subjected to steganographic algorithms, both within the passive warden and active warden frameworks. Our hypothesis is that steganographic schemes leave statistical evidence that can be exploited for detection with the aid of image quality features and multivariate regression analysis. To this effect image quality metrics have been identified based on the analysis of variance (ANOVA) technique as feature sets to distinguish between cover-images and stego-images. The classifier between cover and stego-images is built using multivariate regression on the selected quality metrics and is trained based on an estimate of the original image. Simulation results with the chosen feature set and well-known watermarking and steganographic techniques indicate that our approach is able with reasonable accuracy to distinguish between cover and stego images.
Distributed Machine Learning via Sufficient Factor Broadcasting

Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology. When these models are applied to largescale ML problems starting at millions of samples and tens of thousands of classes, their parameter matrix can grow at an unexpected rate, resulting in high parameter synchronization costs that greatly slow down distributed learning. To address this issue, we propose a Sufficient Factor Broadcasting (SFB) computation model for efficient distributed learning of a large family of matrix-parameterized models, which share the following property: the parameter update computed on each data sample is a rank-1 matrix, i.e. the outer product of two “sufficient factors” (SFs). By broadcasting the SFs among worker machines and reconstructing the update matrices locally at each worker, SFB improves communication efficiency — communication costs are linear in the parameter matrix’s dimensions, rather than quadratic — without affecting computational correctness. We present a theoretical convergence analysis of SFB, and empirically corroborate its efficiency on four different matrix-parametrized ML models.
Scaling Clustering Algorithms to Large Databases

Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clustering framework applicable to a wide class of iterative clustering. We require at most one scan of the database. In this work, the framework is instantiated and numerically justified with the popular K-Means clustering algorithm. The method is based on identifying regions of the data that are compressible, regions that must be maintained in memory, and regions that are discardable. The algorithm operates within the confines of a limited memory buffer. Empirical results demonstrate that the scalable scheme outperforms a sampling-based approach. In our scheme, data resolution is preserved to the extent possible based upon the size of the allocated memory buffer and the fit of current clustering model to the data. The framework is naturally extended to update multiple clustering models simultaneously. We empirically evaluate on synthetic and publicly available data sets.
Bio-inspired computing with resistive memories — models, architectures and applications

The traditional Von Neumann architecture has constrained the potential for applying massively parallel architecture to embedded high performance computing where we must optimize the size, weight and power of the system. Inspired by highly parallel biological systems, such as the human brain, the neuromorphic architecture offers a promising novel computing paradigm for compact and energy efficient platforms. The discovery of memristor devices provided the element we need with unprecedented efficiency in realizing such a computing architecture. There are still many challenges left to meet our goal of a fully functional bio-inspired computer. Here we will discuss our research in memristor crossbar based architecture, adaptation of this architecture for cogent confabulation models, and potential applications of the bio-inspired computer.
A tractable model of reciprocity and fairness

We introduce a parametric model of other-regarding preferences in which my emotional state determines the marginal rate of substitution between my own and others’ payoffs, and thus my subsequent choices. In turn, my emotional state responds to relative status and to the kindness or unkindness of others’ choices. Structural estimations of this model with six existing data sets demonstrate that other-regarding preferences depend on status, reciprocity, and perceived property rights.
Double Refinement Network for Efficient Indoor Monocular Depth Estimation

Monocular Depth Estimation is an important problem of Computer Vision that may be solved with Neural Networks and Deep Learning nowadays. Though recent works in this area have shown significant improvement in accuracy, state-of-the-art methods require large memory and time resources. The main purpose of this paper is to improve performance of the latest solutions with no decrease in accuracy. To achieve this, we propose a Double Refinement Network architecture. We evaluate the results using the standard benchmark RGB-D dataset NYU Depth v2. The results are equal to the current state-of-the-art, while frames per second rate of our approach is significantly higher (up to 15 times speedup per image with batch size 1), RAM per image is significantly lower.
Hand detection using multiple proposals

We describe a two-stage method for detecting hands and their orientation in unconstrained images. The first stage uses three complementary detectors to propose hand bounding boxes. Each bounding box is then scored by the three detectors independently, and a second stage classifier learnt to compute a final confidence score for the proposals using these features. We make the following contributions: (i) we add context-based and skin-based proposals to a sliding window shape based detector to increase recall; (ii) we develop a new method of non-maximum suppression based on super-pixels; and (iii) we introduce a fully annotated hand dataset for training and testing. We show that the hand detector exceeds the state of the art on two public datasets, including the PASCAL VOC 2010 human layout challenge.
The (R) Evolution of social media in software engineering

Software developers rely on media to communicate, learn, collaborate, and coordinate with others. Recently, social media has dramatically changed the landscape of software engineering, challenging some old assumptions about how developers learn and work with one another. We see the rise of the social programmer who actively participates in online communities and openly contributes to the creation of a large body of crowdsourced socio-technical content. 
 In this paper, we examine the past, present, and future roles of social media in software engineering. We provide a review of research that examines the use of different media channels in software engineering from 1968 to the present day. We also provide preliminary results from a large survey with developers that actively use social media to understand how they communicate and collaborate, and to gain insights into the challenges they face. We find that while this particular population values social media, traditional channels, such as face-to-face communication, are still considered crucial. We synthesize findings from our historical review and survey to propose a roadmap for future research on this topic. Finally, we discuss implications for research methods as we argue that social media is poised to bring about a paradigm shift in software engineering research.
Rank-Sparsity Incoherence for Matrix Decomposition

Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components. Such a problem arises in a number of applications in model and system identification and is intractable to solve in general. In this paper we consider a convex optimization formulation to splitting the specified matrix into its components by minimizing a linear combination of the l1 norm and the nuclear norm of the components. We develop a notion of rank-sparsity incoherence, expressed as an uncertainty principle between the sparsity pattern of a matrix and its row and column spaces, and we use it to characterize both fundamental identifiability as well as (deterministic) sufficient conditions for exact recovery. Our analysis is geometric in nature with the tangent spaces to the algebraic varieties of sparse and low-rank matrices playing a prominent role. When the sparse and low-rank matrices are drawn from certain natural random ensembles, we show that the sufficient conditions for exact recovery are satisfied with high probability. We conclude with simulation results on synthetic matrix decomposition problems.
Sex reassignment surgery for transsexuals.

The purpose of this article is to report on the most recent findings on the current surgical treatments for sex reassignment surgery for both male-to-female and female-to-male transsexuals.



For male-to-female sex reassignment surgery, researches aim to refine the inverted peno-scrotal flap surgical technique in order to increase the neovaginal length and width, prevent complications and ameliorate the cosmetic outcome.Bowel segment vaginoplasty, used mainly for secondary cases, is becoming more and more common.For female-to-male sex reassignment surgery, the radial forearm flap is the only procedure that has proved to fulfil most of the patient expectations; however, because of a high complications rate, long recovery time and a large number of the required operations, and the absence of guarantee that patients will be satisfied, the free radial forearm flap is not the best option for every patient. Alternatives such as abdominal/groin flap, thigh flap and metaidoioplasty are very much used.



In order to provide the best care for transsexual patients, centres performing sex reassignment surgery should co-operate and implement both the refinements and the new techniques offered and/or requested.
Learning Semantic Representations of Users and Products for Document Level Sentiment Classification

Neural network methods have achieved promising results for sentiment classification of text. However, these models only use semantics of texts, while ignoring users who express the sentiment and products which are evaluated, both of which have great influences on interpreting the sentiment of text. In this paper, we address this issue by incorporating userand productlevel information into a neural network approach for document level sentiment classification. Users and products are modeled using vector space models, the representations of which capture important global clues such as individual preferences of users or overall qualities of products. Such global evidence in turn facilitates embedding learning procedure at document level, yielding better text representations. By combining evidence at user-, productand documentlevel in a unified neural framework, the proposed model achieves state-of-the-art performances on IMDB and Yelp datasets1.
Multiagent Systems Engineering

This paper describes the Multiagent Systems Engineering (MaSE) methodology. MaSE is a general purpose, methodology for developing heterogeneous multiagent systems. MaSE uses a number of graphically based models to describe system goals, behaviors, agent types, and agent communication interfaces. MaSE also provides a way to specify architecture-independent detailed definition of the internal agent design. An example of applying the MaSE methodology is also presented.
Design of the Neutrosophic Membership Valued Fuzzy-PID Controller and Rotation Angle Control of a Permanent Magnet Direct Current Motor

In this paper, we propose a method based on the fuzzy logic controller (FLC) method, created by using neutrosophic membership values. This method is named as proportional integral derivative-neutrosophic valued fuzzy logic controller (PID-NFLC). We conducted a performance simulation test to compare the PID-NFLC with classical FLC for control of the rotation angle of a permanent magnet direct current (PMDC) motor. Rotation angle error and change of this error were used as the input variables of the FLC and NFLC units. In the suggested neutrosophic valued FLC (NFLC), placement of the input variables was evaluated on the universal set by using neutrosophic membership function approach. Error and error change were evaluated in two different NFLC unit separately from each other. Then, crisp outputs of the NFLC units are applied to PID controller. So, PID-NFLC was obtained. The proposed method and conventional FLC were tested in the SIMULINK. According to the test results, our suggested method is own lesser overshoot ratio and more fast rising time characteristics than the classical FLC. In addition, evaluation of error change, separately from the error in the proposed method is providing flexibility in the PMDC motor position control process.
Algorithmic Complexity in Coding Theory and the Minimum Distance Problem

We startwithan overviewof algorithmiccomplexity problemsin coding theory We then show that the problemof computing the minimumdiktanceof a binaryIinwr code is NP-hard,and the correspondingdeci~”onproblemis W-complete. Thisconstitutes a proof of the conjecture Bedekamp, McEliece,vanTilborg, dating back to 1978. Extensionsand applicationsof this result to other problemsin codingtheqv are discussed.
Trip-Point Bit-Line Precharge Sensing Scheme for Single-Ended SRAM

A trip-point bit-line precharge (TBP) sensing scheme is proposed for high-speed single-ended static random-access memory (SRAM). This TBP scheme mitigates the issues of limited performance, power, sensing margin, and area found in the previous single-ended SRAM sensing schemes by biasing the bit-line to a slightly larger value than the trip point of the sense amplifier. Simulation results show that the TBP sensing scheme can reduce the sensing time by 58.5% and 10% compared with the domino and ac-coupled sensing schemes, respectively. Further, compared with the ac-coupled sensing scheme, the proposed scheme offers 10% lower sensing power, 36% lesser area, and a 60 mV lower value of the minimum supply voltage for the target sensing yield.
Cliques in Hyperbolic Random Graphs

Most complex real world networks display scale-free features. This characteristic motivated the study of numerous random graph models with a power-law degree distribution. There is, however, no established and simple model which also has a high clustering of vertices as typically observed in real data. Hyperbolic random graphs bridge this gap. This natural model has recently been introduced by Krioukov et al. ( 2010) and has shown theoretically and empirically to fulfill all typical properties of real world networks, including power-law degree distribution and high clustering. We study cliques in hyperbolic random graphs G and present new results on the expected number of k-cliques $$\mathbb {E}\left[ K_k\right] $$ EKk and the size of the largest clique $$\omega (G)$$ ω(G) . We observe that there is a phase transition at power-law exponent $$\beta = 3$$ β=3 . More precisely, for $$\beta \in (2,3)$$ β∈(2,3) we prove $$\mathbb {E}\left[ K_k\right] =n^{k (3-\beta )/2} \varTheta (k)^{-k}$$ EKk=nk(3-β)/2Θ(k)-k and $$\omega (G)=\varTheta (n^{(3-\beta )/2})$$ ω(G)=Θ(n(3-β)/2) , while for $$\beta \geqslant 3$$ β⩾3 we prove $$\mathbb {E}\left[ K_k\right] =n \, \varTheta (k)^{-k} $$ EKk=nΘ(k)-k and $$\omega (G)=\varTheta (\log (n)/ \log \log n)$$ ω(G)=Θ(log(n)/loglogn) . Furthermore, we show that for $$\beta \geqslant 3$$ β⩾3 , cliques in hyperbolic random graphs can be computed in time $$\mathcal {O}(n)$$ O(n) . If the underlying geometry is known, cliques can be found with worst-case runtime $$\mathcal {O}(m \cdot n^{2.5})$$ O(m·n2.5) for all values of $$\beta $$ β .
Modern Cryptography: Theory and Practice

Want to get experience? Want to get any ideas to create new things in your life? Read modern cryptography theory and practice now! By reading this book as soon as possible, you can renew the situation to get the inspirations. Yeah, this way will lead you to always think more and more. In this case, this book will be always right for you. When you can observe more about the book, you will know why you need this.
2011 terminology of the vulva of the International Federation for Cervical Pathology and Colposcopy.

This study aimed to present the clinical and colposcopic terminology of the vulva (including the anus) of the International Federation of Cervical Pathology and Colposcopy.



The terminology has been developed by the International Federation of Cervical Pathology and Colposcopy Nomenclature Committee during 2009-2011.



The terminology is part of a comprehensive terminology of the lower genital tract, allowing for standardization of nomenclature by colposcopists, clinicians, and researchers taking care of women with lesions in these areas. The terminology includes basic definitions and normal findings that are important for the clinician lacking experience with management of vulvar disease. This terminology introduces definitions for abnormal findings recently accepted by the International Society for the Study of Vulvovaginal Disease and includes patterns to identify malignancy.



The terminology differs from past terminologies in that it includes colposcopic patterns and anal colposcopy. Nevertheless, the role of the colposcope in the management of vulvar disease is limited.
Torque ripple improvement for synchronous reluctance motor using an asymmetric flux barrier arrangement

An interior permanent-magnet synchronous motor (IPMSM) is a highly efficient motor and operates in a wide speed range; therefore, it is used in many industrial and home appliance applications. However, the torque ripple of synchronous motors such as the IPMSM and synchronous reluctance motor is very large. The variation of magnetic resistance between the flux barriers and teeth causes the torque ripple. In this paper, flux barriers are asymmetrically designed so that the relative positions between the outer edges of the flux barriers and the teeth do not correspond. As a result, torque ripple can be reduced dramatically.
Network motifs: simple building blocks of complex networks.

Complex networks are studied across many fields of science. To uncover their structural design principles, we defined "network motifs," patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.
Applying a flexible microactuator to robotic mechanisms

A flexible microactuator (FMA) driven by an electropneumatic (or electrohydraulic) system has been developed. It has three degrees of freedom-pitch, yaw, and stretch-making it suitable for robotic mechanisms such as fingers, arms, or legs. It is made of fiber-reinforced rubber, and the mechanism is very simple, enabling miniature robots without conventional link mechanisms to be designed. Serially connected FMAs act as a miniature robot manipulator. The kinematics and control algorithm for this type of robot are presented. FMAs combined in parallel act as a multifingered robot hand, with each FMA representing a finger. An algorithm for the cooperative control for such FMAs, the stable region for holding, and its performance have been developed.<<ETX>>