nils-herrmann/clean_scirepeval huggingface dataset:
Numerical rating scale for self-report of pain intensity in children and adolescents: recent progress and further questions.

Until very recently there has been an anomaly in the assessment of the intensity of pediatric pain. The most commonly used self-report scale is the one that, up to now, has had the smallest amount of supportive research. This scale, the numerical rating scale (NRS), is administered by asking patients to say a number, usually from 0 to 10, to express the intensity of their pain. Compared with well-known published scales such as the Faces Pain Scale – Revised, Wong-Baker FACES Pain Rating Scale, Oucher, Coloured Analogue Scale, and Pieces of Hurt, the NRS has the great advantage of requiring only a verbal interaction between the clinician and child, without the necessity for paper or plastic materials which can raise concerns about purchase, storage, distribution, and infection control. The NRS is well established with adults (Dworkin et al., 2005). However, very few studies before 2009 have reported using the NRS with children and adolescents, or have provided data supporting the use of this scale. Stinson et al. (2006), in their landmark systematic review of self-report measures, wrote:
Design and analysis of millimeter wave series fed microstrip patch array for next generation wireless communication systems

A Millimeter-wave series-fed microstrip patch array antenna that operates over the band 27.6 GHz to 30 GHz is presented in this paper. The designed antenna shows a reflection coefficient less than -10 dB over the proposed band. The antenna has a fan beam radiation pattern with 3 dB beamwidth of 9 degrees in the E-plane and 66 degrees in the H-plane. The calculated gain of the proposed antenna is 15.4 dBi. The pulse dispersion effect is quantified by calculating the pulse fidelity factor (PFF) of a transmitted pulse having 500 MHz bandwidth with a center frequency of 28 GHz. The designed antenna exhibits more than 0.9 fidelity factor value, which makes it suitable for wireless data communication systems.
Analyzing (social media) networks with NodeXL

We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data sample drawn from an enterprise intranet social network. A sequence of NodeXL operations from data import to computation of network statistics and refinement of network visualization through sorting, filtering, and clustering functions is described. These operations reveal sociologically relevant differences in the patterns of interconnection among employee participants in the social media space. The tool and method can be broadly applied.
A Survey on Rainfall Prediction using Artificial Neural Network

Rainfall prediction is one of the most important and challenging task in the modern world. In general, climate and rainfall are highly non-linear and complicated phenomena, which require advanced computer modeling and simulation for their accurate prediction. An Artificial Neural Network (ANN) can be used to predict the behavior of such nonlinear systems. ANN has been successfully used by most of the researchers in this field for the last twenty-five years. This paper provides a survey of available literature of some methodologies employed by different researchers to utilize ANN for rainfall prediction. The survey also reports that rainfall prediction using ANN technique is more suitable than traditional statistical and numerical methods.
Clickstream Prediction Using Sequential Stream Mining Techniques with Markov Chains

As one of data mining tasks, sequential pattern mining provides valuable information about frequent patterns of users over time. For instance, frequent sequential patterns can be applicable to analyze user clickstreams for determination of web navigation patterns, genome sequences, and customer purchasing patterns. In many real-life situations, data to be mined are continuously changing. Moreover, these data are streaming at a high velocity, which leads to impracticality of storing all these data in memory. Hence, to handle these situations, we propose three stream mining algorithms to first find frequent sequential patterns. The algorithms then form statistical models, which are stored as Markov chains or transition matrices capturing frequent sequential patterns mined so far, to predict future user clickstream (e.g., the web page the user will visit next). Experimental results show the efficiency and prediction accuracy of our proposed Markov chain-based sequential stream mining algorithms in clickstream prediction.
On Linear Variational Surface Deformation Methods

This survey reviews the recent advances in linear variational mesh deformation techniques. These methods were developed for editing detailed high-resolution meshes like those produced by scanning real-world objects. The challenge of manipulating such complex surfaces is threefold: The deformation technique has to be sufficiently fast, robust, intuitive, and easy to control to be useful for interactive applications. An intuitive and, thus, predictable deformation tool should provide physically plausible and aesthetically pleasing surface deformations, which, in particular, requires its geometric details to be preserved. The methods that we survey generally formulate surface deformation as a global variational optimization problem that addresses the differential properties of the edited surface. Efficiency and robustness are achieved by linearizing the underlying objective functional such that the global optimization amounts to solving a sparse linear system of equations. We review the different deformation energies and detail preservation techniques that were proposed in recent years, together with the various techniques to rectify the linearization artifacts. Our goal is to provide the reader with a systematic classification and comparative description of the different techniques, revealing the strengths and weaknesses of each approach in common editing scenarios.
Using k-Poselets for Detecting People and Localizing Their Keypoints

A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.
Forward Integrity For Secure Audit Logs

In this paper, we de ne the forward integrity security property, motivate its appropriateness as a systems security requirement, and demonstrate designs that achieve this property. Applications include secure audit logs (e.g., syslogd data) for intrusion detection or accountability, communications security, and authenticating partial results of computation for mobile agents. We prove security theorems on our forward integrity message authentication scheme, and discuss the secure audit log application in detail.
Attitudes -1 The Construction of Attitudes

Attitudes have long been considered a central concept of social psychology. In fact, early writers have defined social psychology as the scientific study of attitudes (e.g., Thomas & Znaniecki, 1918) and in 1954 Gordon Allport noted, "This concept is probably the most distinctive and indispensable concept in contemporary American social psychology" (p. 43). As one may expect of any concept that has received decades of attention, the concept of attitudes has changed over the years (see Allport, 1954, for an early review). The initial definitions were broad and encompassed cognitive, affective, motivational, and behavioral components. For example, Allport (1935) defined an attitude as "a mental and neural state of readiness, organized through experience, exerting a directive and dynamic influence upon the individual's response to all objects and situations with which it is related" (p. 810). A decade later, Krech and Crutchfield (1948) wrote, "An attitude can be defined as an enduring organization of motivational, emotional, perceptual, and cognitive processes with respect to some aspect of the individual's world" (p. 152). These definitions emphasized the enduring nature of attitudes and their close relationship to individuals' defined attitudes simply in terms of the probability that a person will show a specified behavior in a specified situation. In subsequent decades, the attitude concept lost much of its breadth and was largely reduced to its evaluative component. In the succinct words of Daryl Bem, "Attitudes are likes and dislikes" attitudes as "a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor" (p. 1). Along the way, many functions that were initially ascribed to attitudes have been reassigned to other cognitive structures and the accumulating body of empirical findings drew many of the classic assumptions into question. A growing body of literature suggests that attitudes may be much less enduring and stable than has traditionally been assumed. As we review below, self-reports of attitudes are highly context-dependent and can be profoundly influenced by minor changes in question wording, question format or question order. Attitudes-3 their assessment is subject to contextual influences. For other researchers, the same findings indicate that all we assess in attitude measurement are evaluative judgments that respondents construct at the time they are asked, based on whatever information happens to be accessible (e.g., Schwarz & Strack, 1991). From this perspective, the traditional attitude concept may not be particularly useful and we may learn more …
Improving performance aligning business analytics with process orientation

How to align business analytics with process orientation initiatives and how management can influence a synergistic accomplishment of these two elements in order to streamline organizational performance is a foremost managerial issue. By means of a quantitative research effort, a study was designed to investigate the relationships among these constructs. Empirical data were collected in 2011 from a survey of 368 large and mid-sized industrial and service companies headquartered in Brazil. A quantitative analysis vailable online 10 January 2013 eywords: usiness process orientation nalytical indicators erformance was performed, and both the reliability and validity of the scales were evaluated prior to using structural equation modeling. The results demonstrate that the direct effects among BPO, analytical indicators and performance can be taken as statistically significant. The findings also demonstrate that BPO and analytical indicators can be taken as predictors of performance. The paper discusses these results and their managerial implications, as well as some research paths for future studies on the topic. tructural equation modeling
Word Embedding based Correlation Model for Question/Answer Matching

The large scale of Q&A archives accumulated in community based question answering (CQA) servivces are important information and knowledge resource on the web. Question and answer matching task has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, a Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding. Given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs, while it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method’s promising
CLASSIFIER-TO-GENERATOR ATTACK: ESTIMATION

Suppose a deep classification model is trained with samples that need to be kept private for privacy or confidentiality reasons. In this setting, can an adversary obtain the private samples if the classification model is given to the adversary? We call this reverse engineering against the classification model the Classifier-toGenerator (C2G) Attack. This situation arises when the classification model is embedded into mobile devices for offline prediction (e.g., object recognition for the automatic driving car and face recognition for mobile phone authentication). For C2G attack, we introduce a novel GAN, PreImageGAN. In PreImageGAN, the generator is designed to estimate the the sample distribution conditioned by the preimage of classification model f , P (X|f(X) = y), where X is the random variable on the sample space and y is the probability vector representing the target label arbitrary specified by the adversary. In experiments, we demonstrate PreImageGAN works successfully with hand-written character recognition and face recognition. In character recognition, we show that, given a recognition model of hand-written digits, PreImageGAN allows the adversary to extract alphabet letter images without knowing that the model is built for alphabet letter images. In face recognition, we show that, when an adversary obtains a face recognition model for a set of individuals, PreImageGAN allows the adversary to extract face images of specific individuals contained in the set, even when the adversary has no knowledge of the face of the individuals.
Introduction to spiking neural networks: Information processing, learning and applications.

The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
Sampling-based algorithms for optimal motion planning

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM∗ and RRT∗, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.
Natural Evolution Strategies

This paper presents natural evolution strategies (NES), a novel algorithm for performing real-valued dasiablack boxpsila function optimization: optimizing an unknown objective function where algorithm-selected function measurements constitute the only information accessible to the method. Natural evolution strategies search the fitness landscape using a multivariate normal distribution with a self-adapting mutation matrix to generate correlated mutations in promising regions. NES shares this property with covariance matrix adaption (CMA), an evolution strategy (ES) which has been shown to perform well on a variety of high-precision optimization tasks. The natural evolution strategies algorithm, however, is simpler, less ad-hoc and more principled. Self-adaptation of the mutation matrix is derived using a Monte Carlo estimate of the natural gradient towards better expected fitness. By following the natural gradient instead of the dasiavanillapsila gradient, we can ensure efficient update steps while preventing early convergence due to overly greedy updates, resulting in reduced sensitivity to local suboptima. We show NES has competitive performance with CMA on unimodal tasks, while outperforming it on several multimodal tasks that are rich in deceptive local optima.
SiRiUS: Securing Remote Untrusted Storage

This paper presents SiRiUS, a secure file system designed to be layered over insecure network and P2P file systems such as NFS, CIFS, OceanStore, and Yahoo! Briefcase. SiRiUS assumes the network storage is untrusted and provides its own read-write cryptographic access control for file level sharing. Key management and revocation is simple with minimal out-of-band communication. File system freshness guarantees are supported by SiRiUS using hash tree constructions. SiRiUS contains a novel method of performing file random access in a cryptographic file system without the use of a block server. Extensions to SiRiUS include large scale group sharing using the NNL key revocation construction. Our implementation of SiRiUS performs well relative to the underlying file system despite using cryptographic operations.
An Efficient Multiplication Algorithm Using Nikhilam Method

Multiplication is one of the most important operation in computer arithmetic. Many integer operations such as squaring, division and computing reciprocal require same order of time as multiplication whereas some other operations such as computing GCD and residue operation require at most a factor of logn time more than multiplication. We propose an integer multiplication algorithm using Nikhilam method of Vedic mathematics which can be used to multiply two binary numbers efficiently.
seL4: formal verification of an OS kernel

Complete formal verification is the only known way to guarantee that a system is free of programming errors.
 We present our experience in performing the formal, machine-checked verification of the seL4 microkernel from an abstract specification down to its C implementation. We assume correctness of compiler, assembly code, and hardware, and we used a unique design approach that fuses formal and operating systems techniques. To our knowledge, this is the first formal proof of functional correctness of a complete, general-purpose operating-system kernel. Functional correctness means here that the implementation always strictly follows our high-level abstract specification of kernel behaviour. This encompasses traditional design and implementation safety properties such as the kernel will never crash, and it will never perform an unsafe operation. It also proves much more: we can predict precisely how the kernel will behave in every possible situation.
 seL4, a third-generation microkernel of L4 provenance, comprises 8,700 lines of C code and 600 lines of assembler. Its performance is comparable to other high-performance L4 kernels.
Grey Wolf Optimizer

This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these wellknown meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.
APTEEN: A Hybrid Protocol for Efficient Routing and Comprehensive Information Retrieval in Wireless Sensor Networks

Wireless sensor networks with thousands of tiny sensor nodes, are expected to find wide applicability and increasing deployment in coming years, as they enable reliable monitoring and analysis of the environment. In this paper, we propose a hybrid routing protocol (APTEEN) which allows for comprehensive information retrieval. The nodes in such a network not only react to time-critical situations, but also give an overall picture of the network at periodic intervals in a very energy efficient manner. Such a network enables the user to request past, present and future data from the network in the form of historical, one-time and persistent queries respectively. We evaluated the performance of these protocols and observe that these protocols are observed to outperform existing protocols in terms of energy consumption and longevity of the network.
On-Line Fingerprint Verification

Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it is incapable of meeting today’s increasing performance requirements. An automatic fingerprint identification system (AFIS) is widely needed. It plays a very important role in forensic and civilian applications such as criminal identification, access control, and ATM card verification. This paper describes the design and implementation of an on-line fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al., which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an on-line inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of on-line verification with high accuracy.