nils-herrmann/clean_scirepeval huggingface dataset:
A Fully-Integrated 3-Level DC-DC Converter for Nanosecond-Scale DVFS

On-chip DC-DC converters have the potential to offer fine-grain power management in modern chip-multiprocessors. This paper presents a fully integrated 3-level DC-DC converter, a hybrid of buck and switched-capacitor converters, implemented in 130 nm CMOS technology. The 3-level converter enables smaller inductors (1 nH) than a buck, while generating a wide range of output voltages compared to a 1/2 mode switched-capacitor converter. The test-chip prototype delivers up to 0.85 A load current while generating output voltages from 0.4 to 1.4 V from a 2.4 V input supply. It achieves 77% peak efficiency at power density of 0.1 W/mm2 and 63% efficiency at maximum power density of 0.3 W/mm2. The converter scales output voltage from 0.4 V to 1.4 V (or vice-versa) within 20 ns at a constant 450 mA load current. A shunt regulator reduces peak-to-peak voltage noise from 0.27 V to 0.19 V under pseudo-randomly fluctuating load currents. Using simulations across a wide range of design parameters, the paper compares conversion efficiencies of the 3-level, buck and switched-capacitor converters.
GamePad: A Learning Environment for Theorem Proving

In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving at a human level of abstraction. We use GamePad to synthesize proofs for a simple algebraic rewrite problem and train baseline models for a formalization of the Feit-Thompson theorem. We address position evaluation (i.e., predict the number of proof steps left) and tactic prediction (i.e., predict the next proof step) tasks, which arise naturally in human-level theorem proving.
Enterprise Knowledge Management and Emerging Technologies

Improving management of information and knowledge in organizations has long been a major objective, but efforts to address it often foundered. Knowledge typically resides in structured documents, informal discussions that may or may not persist online, and in tacit form. Terminology differences and dispersed contextual information hinder efforts to use formal representations. Features of dynamic emerging technologies — unstructured tagging, web-logs, and search — show strong promise in overcoming past obstacles. They exploit digital representations of less formal language and could greatly increase the value of such representations.
Mapping the Spatial Distribution of Forest Fire Danger Using GIS

A geographical information system (CIS) is proposed as a suitable tool for mapping the spatial distribution of forest fire danger. Using a region severely affected by forest fires in Central Spain as the study area, topography, meteorological data, fuel models and human-caused risk were mapped and incorporated within a GIS. Three danger maps were generated: probability of ignition, fuel hazard and human risk, and all of them were overlaid in an integrated fire danger map, based upon the criteria established by the Spanish Forest Service. CIS make it possible to improve our knowledge of the geographical distribution of fire danger, which is crucial for suppression planning (particularly when hotshot crews are involved) and for elaborating regional fire defence plans.
On the Meaning and Use of Kurtosis

For symmetric unimodal distributions, positive kurtosis indicates heavy tails and peakedness relative to the normal distribution, whereas negative kurtosis indicates light tails and flatness. Many textbooks, however, describe or illustrate kurtosis incompletely or incorrectly. In this article, kurtosis is illustrated with well-known distributions, and aspects of its interpretation and misinterpretation are discussed. The role of kurtosis in testing univariate and multivariate normality; as a measure of departures from normality; in issues of robustness, outliers, and bimodality; in generalized tests and estimators, as well as limitations of and alternatives to the kurtosis measure [32, are discussed.
Predicting Billboard Success Using Data-Mining in P2P Networks

Peer to Peer networks are the leading cause for music piracy but also used for music sampling prior to purchase. In this paper we investigate the relations between music file sharing and sales (both physical and digital)using large Peer-to-Peer query database information. We compare file sharing information on songs to their popularity on the Billboard Hot 100 and the Billboard Digital Songs charts, and show that popularity trends of songs on the Billboard have very strong correlation (0.88-0.89) to their popularity on a Peer-to-Peer network. We then show how this correlation can be utilized by common data mining algorithms to predict a song's success in the Billboard in advance, using Peer-to-Peer information.
Implementing A Data Mining Solution To Customer Segmentation For Decayable Products – A Case Study For A Textile Firm

In this paper is developed a model to cluster customers who need to production change continuously. Mere mathematical models to cluster customers lead to ignoring the environment factors in model. Therefore, for Adjusting model to reality, we should use environment factors in model building. The proposed model is flexible against changes of environment and it causes to resulted model can use for clustering the customers of decay able productions. Proposed model perform on customer's information of a textile firm.
Flipper: a new method of digital document navigation

Page flipping is an important part of paper-based document navigation. However this affordance of paper document has not been fully transferred to digital documents. In this paper we present Flipper, a new digital document navigation technique inspired by paper document flipping. Flipper combines speed-dependent automatic zooming (SDAZ)  and rapid serial visual presentation (RSVP) , to let users navigate through documents at a wide range of speeds. It is particularly well adapted to rapid visual search. User studies show Flipper is faster than both conventional scrolling and SDAZ and is well received by users.
Dyadic projected spatial augmented reality

Mano-a-Mano is a unique spatial augmented reality system that combines dynamic projection mapping, multiple perspective views and device-less interaction to support face to face, or dyadic, interaction with 3D virtual objects. Its main advantage over more traditional AR approaches, such as handheld devices with composited graphics or see-through head worn displays, is that users are able to interact with 3D virtual objects and each other without cumbersome devices that obstruct face to face interaction. We detail our prototype system and a number of interactive experiences. We present an initial user experiment that shows that participants are able to deduce the size and distance of a virtual projected object. A second experiment shows that participants are able to infer which of a number of targets the other user indicates by pointing.
Climbing and pole line hardware installation robot for construction of distribution lines

This paper presents a new robot prototype for the construction of Hydro-Québec's overhead distribution lines. It details the main sub-systems of the robot, such as the climbing system (for climbing poles), the crossarm manipulation system (which handles crossarms and selects tools for drilling holes and inserting bolts), and the bolting system (for fixing crossarms and braces on poles). The robot is made to ascend and descend a pole by teleoperation, and the installation of a pre-assembled crossarm (with brace, pins, insulators and clamps) at the top of a 40-foot pole is performed in full automatic mode. Tests have demonstrated the efficiency of this robotized construction method in installing the line hardware of a three-phase distribution line on a 40-foot pole in 25 minutes.
Implementing tablet PCs in schools: Students' attitudes and opinions

In this study, the attitudes, expectations and views of 206 students in four high schools within the scope of the FAT_ IH project in Turkey were assessed regarding tablet PC technology after six months of a pilot plan that included the distribution of tablet PCs to students. The research questions of this study are whether there is a meaningful difference between tablet PC use by male and female students and the effect of computer and Internet by students on attitudes toward tablet PC use. Qualitative and quantitative data collection tools were used in the research. The Computer Attitude Measure for Young students (CAMYS) developed by Teo and Noyes (2008) was used in evaluating the students’ attitudes toward the tablet PC usage. Interviews were conducted with eight teachers at pilot schools concerning the integration of tablet PCs into their classes; the positive and negative dimensions of tablet PCs were analyzed. The findings indicate that students have a positive attitude toward tablet PCs. There was not a meaningful difference between the attitudes of male and female students toward tablet PCs. The length of computer and Internet by the students did not affect their attitudes toward tablet PCs. The ways that teachers used tablet PCs in classes, the positive and negative aspects of tablet PC usage and the students’ expectations of tablet PCs were discussed in the study.
GGSA: A Grouping Gravitational Search Algorithm for data clustering

Gravitational Search Algorithm (GSA) is a stochastic population-based metaheuristic designed for solving continuous optimization problems. It has a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. In this paper, we adapt the structure of GSA for solving the data clustering problems, the process of grouping data into clusters such that the data in each cluster share a high degree of similarity while being very dissimilar to data from other clusters. The proposed algorithm, which is called Grouping GSA (GGSA), differs from the standard GSA in two important aspects. First, a special encoding scheme, called grouping encoding, is used in order to make the relevant structures of clustering problems become parts of solutions. Second, given the encoding, special GSA updating equations suitable for the solutions with grouping encoding are used. The performance of the proposed algorithm is evaluated through several benchmark datasets from the well-known UCI Machine Learning Repository. Its Engineering Applications of Artificial Intelligence 2014; 36:114–121. performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly Algorithm (FA), and nine other well-known classical classification techniques from the literature. The simulation results indicate that GGSA can effectively be used for multivariate data clustering.
Toward Reconstructing Surfaces With Arbitrary Isotropic Reflectance : A Stratified Photometric Stereo Approach

We consider the problem of reconstructing the shape of a surface with an arbitrary, spatially varying isotropic bidirectional reflectance distribution function (BRDF), and introduce a novel, stratified photometric stereo method. By using a particular configuration of lights, it is possible to use symmetry in the image measurements resulting from BRDF isotropy to estimate at each point a plane containing the surface normal. For differentiable surfaces, this allows us to recover the isocontours of the depth map, but not the actual depth associated with each contour. The isocontour structure provides topological information about the surface (critical points, Reeb graph, etc.). By using additional cues in the image data or imposing additional constraints on the surface (e.g., shadows, specular highlights, Helmholtz reciprocity, uniform BRDF), the unknown height of each isocontour can be estimated and the metric structure is resolved. We validate this technique on real and synthetic data by successfully recovering the isocontours of the depth map from images.
Automatic Single Document Text Summarization Using Key Concepts in Documents

Many previous research studies on extractive text summarization consider a subset of words in a document as keywords and use a sentence ranking function that ranks sentences based on their similarities with the list of extracted keywords. But the use of key concepts in automatic text summarization task has received less attention in literature on summarization. The proposed work uses key concepts identified from a document for creating a summary of the document. We view single-word or multi-word keyphrases of a document as the important concepts that a document elaborates on. Our work is based on the hypothesis that an extract is an elaboration of the important concepts to some permissible extent and it is controlled by the given summary length restriction. In other words, our method of text summarization chooses a subset of sentences from a document that maximizes the important concepts in the final summary. To allow diverse information in the summary, for each important concept, we select one sentence that is the best possible elaboration of the concept. Accordingly, the most important concept will contribute first to the summary, then to the second best concept, and so on. To prove the effectiveness of our proposed summarization method, we have compared it to some state-of-the art summarization systems and the results show that the proposed method outperforms the existing systems to which it is compared. Automatic Text Summarization, Key Concepts, Keyphrase Extraction
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization

We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs). The motivation is that when the discriminatorD spreads out its model capacity in the right way, the learning signals given to the generator G are more informative and diverse. These in turn help G to explore better and discover the real data manifold while avoiding large unstable jumps due to the erroneous extrapolation made by D . Our regularizer guides the rectifier discriminator D to better allocate its model capacity, by encouraging the binary activation patterns on selected internal layers of D to have a high joint entropy. Experimental results on both synthetic data and real datasets demonstrate improvements in stability and convergence speed of the GAN training, as well as higher sample quality. The approach also leads to higher classification accuracies in semi-supervised learning.
Happiness level prediction with sequential inputs via multiple regressions

This paper presents our solution submitted to the Emotion Recognition in the Wild (EmotiW 2016) group-level happiness intensity prediction sub-challenge. The objective of this sub-challenge is to predict the overall happiness level given an image of a group of people in a natural setting. We note that both the global setting and the faces of the individuals in the image influence the group-level happiness intensity of the image. Hence the challenge lies in building a solution that incorporates both these factors and also considers their right combination. Our proposed solution incorporates both these factors as a combination of global and local information. We use a convolutional neural network to extract discriminative face features, and a recurrent neural network to selectively memorize the important features to perform the group-level happiness prediction task. Experimental evaluations show promising performance improvements, resulting in Root Mean Square Error (RMSE) reduction of about 0.5 units on the test set compared to the baseline algorithm that uses only global information.
Management of Information Security: Challenges and Research Directions

Over the past decade management of information systems security has emerged to be a challenging task. Given the increased dependence of businesses on computer-based systems and networks, vulnerabilities of systems abound. Clearly, exclusive reliance on either the technical or the managerial controls is inadequate. Rather, a multifaceted approach is needed. In this paper, based on a panel presented at the 2007 Americas Conference on Information Systems held in Keystone, Colorado, we provide examples of failures in information security, identify challenges for the management of information systems security, and make a case that these challenges require new theory development via examining reference disciplines. We identify these disciplines, recognize applicable research methodologies, and discuss desirable properties of applicable theories.
Neural Networks of the Mouse Neocortex

Numerous studies have examined the neuronal inputs and outputs of many areas within the mammalian cerebral cortex, but how these areas are organized into neural networks that communicate across the entire cortex is unclear. Over 600 labeled neuronal pathways acquired from tracer injections placed across the entire mouse neocortex enabled us to generate a cortical connectivity atlas. A total of 240 intracortical connections were manually reconstructed within a common neuroanatomic framework, forming a cortico-cortical connectivity map that facilitates comparison of connections from different cortical targets. Connectivity matrices were generated to provide an overview of all intracortical connections and subnetwork clusterings. The connectivity matrices and cortical map revealed that the entire cortex is organized into four somatic sensorimotor, two medial, and two lateral subnetworks that display unique topologies and can interact through select cortical areas. Together, these data provide a resource that can be used to further investigate cortical networks and their corresponding functions.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling

We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling 1. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which improves accuracy, and (iii) it is easy to visualise the effect of feature activation(s) in the pixel label space at any depth. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. The decoders help map low resolution feature maps at the output of the encoder stack to full input image size feature maps. This addresses an important drawback of recent deep learning approaches which have adopted networks designed for object categorization for pixel wise labelling. These methods lack a mechanism to map deep layer feature maps to input dimensions. They resort to ad hoc methods to upsample features, e.g. by replication. This results in noisy predictions and also restricts the number of pooling layers in order to avoid too much upsampling and thus reduces spatial context. SegNet overcomes these problems by learning to map encoder outputs to image pixel labels. We test the performance of SegNet on outdoor RGB scenes from CamVid, KITTI and indoor scenes from the NYU dataset. Our results show that SegNet achieves state-of-the-art performance even without use of additional cues such as depth, video frames or post-processing with CRF models.
Changing your mind: on the contributions of top-down and bottom-up guidance in visual search for feature singletons.

Observers, searching for targets among distractor items, guide attention with a mix of top-down information--based on observers' knowledge--and bottom-up information--stimulus-based and largely independent of that knowledge. There are 2 types of top-down guidance: explicit information (e.g., verbal description) and implicit priming by preceding targets (top-down because it implies knowledge of previous searches). Experiments 1 and 2 separate bottom-up and top-down contributions to singleton search. Experiment 3 shows that priming effects are based more strongly on target than on distractor identity. Experiments 4 and 5 show that more difficult search for one type of target (color) can impair search for other types (size, orientation). Experiment 6 shows that priming guides attention and does not just modulate response.
Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances

This paper evaluates the four leading techniques proposed in the literature for construction of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods are reviewed and their performance for generating high-quality PIs is compared. PI-based measures are proposed and applied for the objective and quantitative assessment of each method's performance. A selection of 12 synthetic and real-world case studies is used to examine each method's performance for PI construction. The comparison is performed on the basis of the quality of generated PIs, the repeatability of the results, the computational requirements and the PIs variability with regard to the data uncertainty. The obtained results in this paper indicate that: 1) the delta and Bayesian methods are the best in terms of quality and repeatability, and 2) the MVE and bootstrap methods are the best in terms of low computational load and the width variability of PIs. This paper also introduces the concept of combinations of PIs, and proposes a new method for generating combined PIs using the traditional PIs. Genetic algorithm is applied for adjusting the combiner parameters through minimization of a PI-based cost function subject to two sets of restrictions. It is shown that the quality of PIs produced by the combiners is dramatically better than the quality of PIs obtained from each individual method.
DnaSP, DNA polymorphism analyses by the coalescent and other methods

DnaSP is a software package for the analysis of DNA polymorphism data. Present version introduces several new modules and features which, among other options allow: (1) handling big data sets (approximately 5 Mb per sequence); (2) conducting a large number of coalescent-based tests by Monte Carlo computer simulations; (3) extensive analyses of the genetic differentiation and gene flow among populations; (4) analysing the evolutionary pattern of preferred and unpreferred codons; (5) generating graphical outputs for an easy visualization of results.


AVAILABILITY
The software package, including complete documentation and examples, is freely available to academic users from:
A Detailed Heat and Fluid Flow Analysis of an Internal Permanent Magnet Synchronous Machine by Means of Computational Fluid Dynamics

This paper presents a comprehensive computational fluid dynamics (CFD) model of a radial flux permanent magnet synchronous machine with interior magnets. In the CFD model, the water jacket cooling and a simplified model of the topology of the distributed stator winding are considered. The heat sources of the CFD model are determined from a finite-element analysis of the machine. The numerically determined temperature distributions of the machine are compared with measurement results from sensors located both in the stator and rotor. The particular focus of this paper is the analysis of the temperatures and the heat flow in the air gap and from the stator winding heads and the rotor to the inner air. Different operating conditions and two particular rotor designs with different inner air flow configurations are investigated. The potential of improving the thermal utilization of a rotor design with fan blades attached to the mounting plates of the rotor is shown.