nils-herrmann/clean_scirepeval huggingface dataset:
L2-Nonexpansive Neural Networks

This paper proposes a class of well-conditioned neural networks in which a unit amount of change in the inputs causes at most a unit amount of change in the outputs or any of the internal layers. We develop the known methodology of controlling Lipschitz constants to realize its full potential in maximizing robustness, with a new regularization scheme for linear layers, new ways to adapt nonlinearities and a new loss function. With MNIST and CIFAR-10 classifiers, we demonstrate a number of advantages. Without needing any adversarial training, the proposed classifiers exceed the state of the art in robustness against white-box L2-bounded adversarial attacks. Their outputs are quantitatively more meaningful than ordinary networks and indicate levels of confidence and generalization. They are also free of exploding gradients, among other desirable properties.
Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication.

We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.
Trichoscopy: a new method for diagnosing hair loss.

Videodermoscopy of hair and scalp (trichoscopy) is gaining popularity as a valuable tool in differential diagnosis of hair loss. This method allows viewing of the hair and scalp at X20 to X160 magnifications. Structures which may be visualized by trichoscopy include hair shafts of different types: vellus, terminal, micro-exclamation mark type, monilethrix, Netherton type, and pili annulati hairs. The number of hairs in one pilosebaceous unit may be assessed. It may be distinguished whether hair follicles are normal, empty, fibrotic ("white dots"), filled with hyperkeratotic plugs ("yellow dots") or containing cadaverized hair ("black dots"). Abnormalities of scalp skin color or structure which may be visualized by trichoscopy include honeycomb-type hyperpigmentation, perifollicular discoloration (hyperpigmentation), and scaling.
Working Memory Deficits can be Overcome : Impacts of Training and Medication on Working Memory in Children with ADHD

This study evaluated the impact of two interventions—a training program and stimulant medication—on working memory (WM) function in children with attention deficit hyperactivity disorder (ADHD). Twenty-five children aged between 8 and 11 years participated in training that taxed WM skills to the limit for a minimum of 20 days, and completed other assessments of WM and IQ before and after training, and with and without prescribed drug treatment. While medication significantly improved visuo-spatial memory performance, training led to substantial gains in all components of WM across untrained tasks. Training gains associated with the central executive persisted over a 6month period. IQ scores were unaffected by either intervention. These findings indicate that the WM impairments in children with ADHD can be differentially ameliorated by training and by stimulant medication.
Kinesthetic interaction: revealing the bodily potential in interaction design

Within the Human-Computer Interaction community there is a growing interest in designing for the whole body in interaction design. The attempts aimed at addressing the body have very different outcomes spanning from theoretical arguments for understanding the body in the design process, to more practical examples of designing for bodily potential. This paper presents Kinesthetic Interaction as a unifying concept for describing the body in motion as a foundation for designing interactive systems. Based on the theoretical foundation for Kinesthetic Interaction, a conceptual framework is introduced to reveal bodily potential in relation to three design themes --- kinesthetic development, kinesthetic means and kinesthetic disorder; and seven design parameters --- engagement, sociality, movability, explicit motivation, implicit motivation, expressive meaning and kinesthetic empathy. The framework is a tool to be utilized when analyzing existing designs, as well as developing designs exploring new ways of designing kinesthetic interactions.
Visual odometry for ground vehicle applications

We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual estimates can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results obtained with a stereo-head mounted on an autonomous ground vehicle. We give examples of camera trajectories estimated in real-time purely from images over previously unseen distances (600 meters) and periods of time .
Level set methods and dynamic implicit surfaces

What do you do to start reading level set methods and dynamic implicit surfaces 1st edition? Searching the book that you love to read first or find an interesting book that will make you want to read? Everybody has difference with their reason of reading a book. Actuary, reading habit must be from earlier. Many people may be love to read, but not a book. It's not fault. Someone will be bored to open the thick book with small words to read. In more, this is the real condition. So do happen probably with this level set methods and dynamic implicit surfaces 1st edition.
RDF-4X: a scalable solution for RDF quads store in the cloud

Resource Description Framework (RDF) represents a flexible and concise model for representing the metadata of resources on the web. Over the past years, with the increasing amount of RDF data, efficient and scalable RDF data management has become a fundamental challenge to achieve the Semantic Web vision. However, multiple approaches for RDF storage have been suggested, ranging from simple triple stores to more advanced techniques like vertical partitioning on the predicates or centralized approaches. Unfortunately, it is still a challenge to store a huge quantity of RDF quads due, in part, to the query processing for RDF data. This paper proposes a scalable solution for RDF data management that uses Apache Accumulo. We focus on introducing storage methods and indexing techniques that scale to billions of quads across multiple nodes, while providing fast and easy access to the data through conventional query mechanisms such as SPARQL. Our performance evaluation shows that in most cases our approach works well against large RDF datasets.
Fixed point implementation of Kalman filtering for AC drives : a case study using TMS 320 F 24 x DSP

The real-time digital implementation of the Kalman filter requires a very fast signal processor specialised and optimised to perform complex mathematical calculations and manipulate a large amount of data. In fact, the algorithm is computationally intensive, and all of the steps involved require vector and/or matrix operation. In this paper the analysis of an actual fixed point implementation of the Kalman filter is discussed based on a last generation μC DSP (TMS320F240). The considered case study refers to rotor speed and position estimation in AC drives when using low resolution position transducers. It is shown that the adopted processor is suitable for an efficient and relatively simple implementation of the filter allowing to increase the resolution in speed calculation with respect to the classical differentiation solution.  As known, the Kalman filter is an optimal recursive algorithm which provides the minimum variance state estimation for a time-varying linear system. It is able to tolerate system modelling and measurement errors, which are considered as noise processes in the state estimation. It processes all available measurements regardless of their precision, to provide a quick and accurate estimate of the variables of interest, also achieving a fast convergence. Its extension to non-linear systems, the Extended Kalman Filter (EKF), does not assure the minimum variance estimate and no convergence proof can be given. Nevertheless, the approach behaves well in most situations, as demonstrated by numerous applications (e.g. [1][2]). For a straightforward application of this algorithm, the non-linear discrete-time state equations of the system are written in the following form: ( ) ( ) k k k k k k k k , , , , v u x h y w u x f x + = + = + k k 1 R vv í Q ww w T T = = = = ) E( ) cov( ) E( ) cov( (1) where xk is the system state vector, yk is the system output, uk is the system input, wk and vk are zeromean white Gaussian additive noises with covariance Q and R, respectively (independent from the system state xk). The vector wk takes into account the system disturbances and model inaccuracies, while v represents the measurement noise. A block diagram of the EKF for the system (1) is given in Fig. 1 together with the list of the steps of a recursive implementation . The filter provides a first estimate of x ( x ~ , prediction) based on the model equations supposing that the model noise is zero. Then the measurements and noise models are used to generate the sub-optimal estimate x̂ . P ~ and P̂ are, respectively, the prediction and the estimation error covariance matrices. In the following sections a brief review of the basic applications of the EKF to the estimation of the state variables of electrical drives is reported. I) ) , ~ ( ~ ) , ˆ ( ~ u x h y u x f = x = x x x x f F ˆ ) ( = ∂ ∂ = x x x x h H ˆ ) ( = ∂ ∂ = II) Q F P F P + = T ˆ ~ III) [ ] 1 T T ~ ~ − + = R H P H H P K IV) ) ~ ( ~ ˆ y y K x = x − + V) P KH P P ~ ~ ˆ − = y x̂ u
Colors and emotions: preferences and combinations.

Within three age groups (7-year-old children, 11-year-old children, and adults), preferences for colors and emotions were established by means of two distinct paired-comparison tasks. In a subsequent task, participants were asked to link colors to emotions by selecting an appropriate color. It was hypothesized that the number of times that each color was tied to a specific emotion would be predictable from the separate preferences for colors and emotions. Within age groups, participants had consistent preferences for colors and emotions, but preferences differed from one age group to another. Especially in the youngest group, the pattern of combinations between colors and emotions appeared to be meaningfully related to the preference order for colors and emotions.
Comparison of Architectural Design Decisions for Resource-Constrained Self-Driving Cars - A Multiple Case-Study

Self-Driving cars are getting more and more attention with public demonstration from all important automotive OEMs but also from companies, which do not have a long history in the automotive industry. Fostered by large international competitions in the last decade, several automotive OEMs have already announced to bring this technology to the market around 2020.  International competitions like the 2007 DARPA Urban Challenge did not focus on efficient usage of resources to realize the self-driving vehicular functionality. Since the automotive industry is very cost-sensitive, realizing reliable and robust selfdriving functionality is challenging when expensive and sophisticated sensors mounted very visibly on the vehicle’s roof for example cannot be used. Therefore, the goal for this study is to investigate how architectural design decisions of recent self-driving vehicular technology consider resource-efficiency.  In a multiple case study, the architectural design decisions derived for resourceconstrained self-driving miniature cars for the international competition CaroloCup are compared with architectural designs from recent real-scale self-driving cars.  Scaling down available resources for realizing self-driving vehicular technology puts additional constraints on the architectural design; especially reusability of software components in platform-independent algorithmic concepts are prevailing.  Software frameworks like the robotic operating system (ROS) enable fast prototypical solutions; however, architectural support for resource-constrained devices is limited. Here, architectural design drivers as realized in AUTOSAR are more suitable.
A modified Covariance Matrix Adaptation Evolution Strategy with adaptive penalty function and restart for constrained optimization

In the last decades, a number of novel meta-heuristics and hybrid algorithms have been proposed to solve a great variety of optimization problems. Among these, constrained optimization problems are considered of particular interest in applications from many different domains. The presence of multiple constraints can make optimization problems particularly hard to solve, thus imposing the use of specific techniques to handle fitness landscapes which generally show complex properties. In this paper, we introduce a modified Covariance Matrix Adaptation Evolution Strategy (CMA-ES) specifically designed for solving constrained optimization problems. The proposed method makes use of the restart mechanism typical of most modern variants of CMA-ES, and handles constraints by means of an adaptive penalty function. This novel CMA-ES scheme presents competitive results on a broad set of benchmark functions and engineering problems, outperforming most state-of-the-art algorithms as for both efficiency and constraint handling.
Benchmarking question answering systems

The necessity of making the Semantic Web more accessible for lay users, alongside the uptake of interactive systems and smart assistants for the Web, have spawned a new generation of RDF-based question answering systems. However, fair evaluation of these systems remains a challenge due to the different type of answers that they provide. Hence, repeating current published experiments or even benchmarking on the same datasets remains a complex and time-consuming task. We present a novel online benchmarking platform for question answering (QA) that relies on the FAIR principles to support the fine-grained evaluation of question answering systems. We detail how the platform addresses the fair benchmarking platform of question answering systems through the rewriting of URIs and URLs. In addition, we implement different evaluation metrics, measures, datasets and pre-implemented systems as well as methods to work with novel formats for interactive and non-interactive benchmarking of question answering systems. Our analysis of current frameworks shows that most of the current frameworks are tailored towards particular datasets and challenges but do not provide generic models. In addition, while most frameworks perform well in the annotation of entities and properties, the generation of SPARQL queries from annotated text remains a challenge.
Review: Semiconductor Piezoresistance for Microsystems

Piezoresistive sensors are among the earliest micromachined silicon devices. The need for smaller, less expensive, higher performance sensors helped drive early micromachining technology, a precursor to microsystems or microelectromechanical systems (MEMS). The effect of stress on doped silicon and germanium has been known since the work of Smith at Bell Laboratories in 1954. Since then, researchers have extensively reported on microscale, piezoresistive strain gauges, pressure sensors, accelerometers, and cantilever force/displacement sensors, including many commercially successful devices. In this paper, we review the history of piezoresistance, its physics and related fabrication techniques. We also discuss electrical noise in piezoresistors, device examples and design considerations, and alternative materials. This paper provides a comprehensive overview of integrated piezoresistor technology with an introduction to the physics of piezoresistivity, process and material selection and design guidance useful to researchers and device engineers.
Software Unit Test Coverage and Adequacy

Objective measurement of test quality is one of the key issues in software testing. It has been a major research focus for the last two decades. Many test criteria have been proposed and studied for this purpose. Various kinds of rationales have been presented in support of one criterion or another. We survey the research work in this area. The notion of adequacy criteria is examined together with its role in software dynamic testing. A review of criteria classification is followed by a summary of the methods for comparison and assessment of criteria.
Disease recognition in Mango crop using modified rotational kernel transform features

Soft computing in the field of agriculture science is being employed with computer vision techniques in order to detect the diseases in crops to increase the overall yield. A Modified Rotation Kernel Transformation(MRKT) based directional feature extraction scheme is presents to resolve the issues occurring due to shape, color or other deceptive features during plant disease recognition. The MRKT based scheme is used to calculate the directional features and histograms for plant parts like leaf and fruit of Mango crop. These histograms and the directional feature set with use of artificial neural network lead to a better recognition technique of Anthracnose disease which occurs in form of black spots on fruits and leaves of mango plant. The results obtained using proposed MRKT directional feature set have shows the better results with accuracy up to 98%.
Self processes in interdependent relationships Partner affirmation and the Michelangelo phenomenon

This essay reviews theory and research regarding the “Michelangelo phenomenon,” which describes the manner in which close partners shape one another’s dispositions, values, and behavioral tendencies. Individuals are more likely to exhibit movement toward their ideal selves to the degree that their partners exhibit affirming perception and behavior, exhibiting confidence in the self ’s capacity and enacting behaviors that elicit key features of the self ’s ideal. In turn, movement towards the ideal self yields enhanced personal well-being and couple well-being. We review empirical evidence regarding this phenomenon and discuss self and partner variables that contribute to the process.
A cluster centers initialization method for clustering categorical data

The k-modes algorithm Initialization method Initial cluster centers Density Distance a b s t r a c t The leading partitional clustering technique, k-modes, is one of the most computationally efficient clustering methods for categorical data. However, the performance of the k-modes clustering algorithm which converges to numerous local minima strongly depends on initial cluster centers. Currently, most methods of initialization cluster centers are mainly for numerical data. Due to lack of geometry for the categorical data, these methods used in cluster centers initialization for numerical data are not applicable to categorical data. This paper proposes a novel initialization method for categorical data which is implemented to the k-modes algorithm. The method integrates the distance and the density together to select initial cluster centers and overcomes shortcomings of the existing initialization methods for categorical data. Experimental results illustrate the proposed initialization method is effective and can be applied to large data sets for its linear time complexity with respect to the number of data objects. Clustering is a process of grouping a set of objects into clusters so that the objects in the same cluster have high similarity but are very dissimilar with objects in other clusters. Various types of clustering methods have been proposed and developed, see, for instance (Jain & Dubes, 1988). Clustering algorithms in the literature can generally be classified into two types: hierarchical clustering and partitional clustering. Hierarchical clustering algorithms, essentially heuristic procedures, produce a hierarchy of partitions of the set of observations according to an agglomerative strategy or to a divisive one. Partitional clustering algorithms, in general, assume a given number of clusters and, essentially, seek the optimization of an objective function measuring the homogeneity within the clusters and/or the separation between the clusters. is a well known partitional clustering algorithm which is widely used in real world applications such as marketing research and data mining to cluster very large data sets due to their efficiency. extended the k-means algorithm to propose the k-modes algorithm whose extensions have removed the numeric-only limitation of the k-means algorithm and enable the k-means clustering process to be used to efficiently cluster large categorical data sets from real world databases. Since first published, the k-modes algorithm has become a popular technique in solving categorical data clustering problems in different application domains (Andreopoulos, An, & Wang, 2005). The k-means algorithm and the k-modes algorithm use alternating minimization methods to …
Looking at Vehicles in the Night : Detection & Dynamics of Rear Lights

Existing nighttime vehicle detection methods use color as the primary cue for detecting vehicles. However, complex road and ambient lighting conditions, and camera configurations can influence the effectiveness of such explicit rule and threshold based methods. In this paper, there are three main contributions. First, we present a novel method to detect vehicles during nighttime involving both learned classifiers and explicit rules, which can operate in the presence of varying ambient lighting conditions. The proposed method which is titled as VeDANt (Vehicle Detection using Active-learning during Nighttime) employs a modified form of active learning for training Adaboost classifiers with Haar-like features using gray-level input images. The hypothesis windows are then further verified using the proposed techniques involving perspective geometries and color information of the taillights. Second, VeDANt is extended to analyze the dynamics of the vehicles during nighttime by detecting three taillight activities braking, turning left and turning right. Third, we release three new and fully annotated LISA-Night datasets with over 5000 frames for evaluation and benchmarking, which capture a variety of complex traffic and lighting conditions. Such comprehensively annotated and complex public datasets is a first in the area of nighttime vehicle detection. We show that VeDANt is able to detect vehicles during nighttime with over 98% accuracy and less than 1% false detections.