nils-herrmann/clean_scirepeval huggingface dataset:
A hybrid convolutional neural networks with extreme learning machine for WCE image classification

Wireless Capsule Endoscopy (WCE) is considered as a promising technology for non-invasive gastrointestinal disease examination. This paper studies the classification problem of the digestive organs for wireless capsule endoscopy (WCE) images aiming at saving the review time of doctors. Our previous study has proved the Convolutional Neural Networks (CNN)-based WCE classification system is able to achieve 95% classification accuracy in average, but it is difficult to further improve the classification accuracy owing to the variations of individuals and the complex digestive tract circumstance. Research shows that there are two possible approaches to improve classification accuracy: to extract more discriminative image features and to employ a more powerful classifier. In this paper, we propose to design a WCE classification system by a hybrid CNN with Extreme Learning Machine (ELM). In our approach, we construct the CNN as a data-driven feature extractor and the cascaded ELM as a strong classifier instead of the conventional used full-connection classifier in deep CNN classification system. Moreover, to improve the convergence and classification capability of ELM under supervision manner, a new initialization is employed. Our developed WCE image classification system is named as HCNN-NELM. With about 1 million real WCE images (25 examinations), intensive experiments are conducted to evaluate its performance. Results illustrate its superior performance compared to traditional classification methods and conventional CNN-based method, where about 97.25% classification accuracy can be achieved in average.
Smart Antennas — State of the Art

Preface It was in the summer of 2002 when the European Union launched the Sixth Framework Research Programme. One of the expected outcomes of this multibillion Euro initiative was Structuring, that is, tackling the fragmentation of European research on a large scale. The concept of the so-called Network of Excellence (NoE) was born, which was designed to strengthen scientific and technological excellence on a particular research topic. Without doubt Smart Antennas, which here stands for any multiantenna technique, is an important research topic in wireless communications and is characterized by severe fragmentation in Europe. Indeed, after the first steps were taken to establish a proposal, it quickly turned out that more than 100 institutions from academia and from industry were devoting much effort toward smart antenna research, so not only fragmentation but also a required critical mass of resources and of expertise were on hand. By the end of 2002, the idea of a Network of Excellence for Smart Antenna Technology (NESAT) took a rather concrete shape. A series of four meetings took place up until spring 2003, where European smart antenna experts further tightened their personal links. In addition, scientists from overseas were also invited to encourage discussions of a worldwide scope. Although the NESAT proposal failed in summer 2003 because of strong competition, there was immediate commitment among the network partners to demonstrate the state-of-the-art technology in smart antennas within the framework of a comprehensive book. This book is now in the readers' hands. It consists of six major parts, which are summarized below, and each part is split into several chapters. Most often a team of authors joining NESAT took responsibility for a single chapter, so the total number of authors is hard to count. This has made it difficult to equally thank all involved people, so we generally apologize for the absence of personal acknowledgments. Although the first multiantenna-based products have been commercially available in wireless communications for a few months, there is no doubt that smart antennas were, are, and especially will remain a hot topic in research, even beyond this decade. Hence, a book aiming at covering the state of the art of this technology is to be seen as a snapshot. We hope that this book will serve as a comprehensive survey reflecting the smart antenna research in the period from 2003 to 2005. Receiver In this first part a collection of different …
Attending to All Mention Pairs for Full Abstract Biological Relation Extraction

Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation types, particularly in biomedical text, are expressed across sentences or require a large context to disambiguate. We propose a model to consider all mention and entity pairs simultaneously in order to make a prediction. We encode full paper abstracts using an efficient self-attention encoder and form pairwise predictions between all mentions with a bi-affine operation. An entity-pair wise pooling aggregates mention pair scores to make a final prediction while alleviating training noise by performing within document multi-instance learning. We improve our model’s performance by jointly training the model to predict named entities and adding an additional corpus of weakly labeled data. We demonstrate our model’s effectiveness by achieving the state of the art on the Biocreative V Chemical Disease Relation dataset for models without KB resources, outperforming ensembles of models which use hand-crafted features and additional linguistic resources.
Meta-learning for time series forecasting and forecast combination

In research of time series forecasting, a lot of uncertainty is still related to the task of selecting an appropriate forecasting method for a problem. It is not only the individual algorithms that are available in great quantities; combination approaches have been equally popular in the last decades. Alone the question of whether to choose the most promising individual method or a combination is not straightforward to answer. Usually, expert knowledge is needed to make an informed decision, however, in many cases this is not feasible due to lack of resources like time, money and manpower. This work identifies an extensive feature set describing both the time series and the pool of individual forecasting methods. The applicability of different meta-learning approaches are investigated, first to gain knowledge on which model works best in which situation, later to improve forecasting performance. Results show the superiority of a rankingbased combination of methods over simple model selection approaches.
Skinning mesh animations

We extend approaches for skinning characters to the general setting of skinning deformable mesh animations. We provide an automatic algorithm for generating progressive skinning approximations, that is particularly efficient for pseudo-articulated motions. Our contributions include the use of nonparametric mean shift clustering of high-dimensional mesh rotation sequences to automatically identify statistically relevant bones, and robust least squares methods to determine bone transformations, bone-vertex influence sets, and vertex weight values. We use a low-rank data reduction model defined in the undeformed mesh configuration to provide progressive convergence with a fixed number of bones. We show that the resulting skinned animations enable efficient hardware rendering, rest pose editing, and deformable collision detection. Finally, we present numerous examples where skins were automatically generated using a single set of parameter values.
Efficient Organized Point Cloud Segmentation with Connected Components

Segmentation is an important step in many perception tasks, such as object detection and recognition. We present an approach to organized point cloud segmentation and its application to plane segmentation, and euclidean clustering for tabletop object detection. The proposed approach is efficient and enables real-time plane segmentation for VGA resolution RGB-D data. Timing results are provided for indoor datasets, and applications to tabletop object segmentation and mapping with planar landmarks are discussed.
Experiential attributes and consumer judgments

Traditionally, marketers have focused on functional and meaningful product differentiation and have shown that such differentiation is important because consumers engage in a deliberate reasoning process (Chernev, 2001; Shafir et al., 1993; Simonson, 1989). However, nowadays products in many categories are functionally highly similar, and it is difficult for consumers to differentiate products based on functional attributes. An alternative way of differentiating is to emphasize non-functional product characteristics or certain aspects of the judgment context. For example, the VW New Beetle brand has used unique colors and shapes very prominently. Apple Computers has used a smiley face that appeared on the screen of computers when they were powered up as well as translucent colors to differentiate, for example, its iMac and iPod lines from competitive products. In addition, Apple Computers has integrated the colors and shapes of the product design with the design of its websites and the so-called AppleStores. Similar approaches focusing on colors, shapes or affective stimuli have been used for other global brands as well and for local brands in all sorts of product categories, including commodities like water and salt. Here we refer to such attributes, which have emerged in marketing as key differentiators, as ‘experiential attributes’ (Schmitt, 1999). Specifically, experiential attributes consist of non-verbal stimuli that include sensory cues such as colors (Bellizzi et al., 1983; Bellizzi and Hite, 1992; Degeratu et al., 2000; Gorn et al., 1997; Meyers-Levy and Peracchio, 1995) and shapes (Veryzer and Hutchinson, 1998) as well as affective cues such as mascots that may appear on products, packaging or contextually as part of ads (Holbrook and Hirschman, 1982; Keller, 1987). Experiential attributes are also used in logos (Henderson et al., 2003), and as part of the judgment context, for example, as backgrounds on websites (Mandel and Johnson, 2002) and in shopping environments (Spies et al., 1997). Unlike functional attributes, experiential attributes are not utilitarian (Zeithaml, 1988). Instead, experiential attributes may result in positive ‘feelings and experiences’ (Schwarz and Clore, 1996; Winkielman et al., 2003). Yet, how exactly do consumers process experiential attributes? How can consumers use them to reach a decision among alternatives? Moreover, are there different ways of processing experiential attributes? In this chapter, we examine how experiential attributes are processed and how they are of value in consumer decision-making. We distinguish two ways of processing experiential features: deliberate processing, which is similar to the way functional attributes are processed, and fluent processing, which occurs without much deliberation. We identify judgment contexts in which consumers process experiential
SIMULATOR SICKNESS QUESTIONNAIRE: TWENTY YEARS LATER

The present study used simulator sickness questionnaire data from nine different studies to validate and explore the work of the most widely used simulator sickness index. The ability to predict participant dropouts as a result of simulator sickness symptoms was also evaluated. Overall, participants experiencing nausea and nausea-related symptoms were the most likely to fail to complete simulations. Further, simulation specific factors that increase the discrepancy between visual and vestibular perceptions are also related to higher participant study dropout rates. As a result, it is suggested that simulations minimize turns, curves, stops, et cetera, if possible, in order to minimize participant simulation sickness symptoms. The present study highlights several factors to attend to in order to minimize elevated participant simulation sickness.
Dilated Convolutions for Modeling Long-Distance Genomic Dependencies

We consider the task of detecting regulatory elements in the human genome directly from raw DNA. Past work has focused on small snippets of DNA, making it difficult to model long-distance dependencies that arise from DNA’s 3-dimensional conformation. In order to study long-distance dependencies, we develop and release a novel dataset for a larger-context modeling task. Using this new data set we model long-distance interactions using dilated convolutional neural networks, and compare them to standard convolutions and recurrent neural networks. We show that dilated convolutions are effective at modeling the locations of regulatory markers in the human genome, such as transcription factor binding sites, histone modifications, and DNAse hypersensitivity sites.
Forensics investigation challenges in cloud computing environments

Cloud computing discusses about sharing any imaginable entity such as process units, storage devices or software. The provided service is utterly economical and expandable. Cloud computing attractive benefits entice huge interest of both business owners and cyber thefts. Consequently, the “computer forensic investigation” step into the play to find evidences against criminals. As a result of the new technology and methods used in cloud computing, the forensic investigation techniques face different types of issues while inspecting the case. The most profound challenges are difficulties to deal with different rulings obliged on variety of data saved in different locations, limited access to obtain evidences from cloud and even the issue of seizing the physical evidence for the sake of integrity validation or evidence presentation. This paper suggests a simple yet very useful solution to conquer the aforementioned issues in forensic investigation of cloud systems. Utilizing TPM in hypervisor, implementing multi-factor authentication and updating the cloud service provider policy to provide persistent storage devices are some of the recommended solutions. Utilizing the proposed solutions, the cloud service will be compatible to the current digital forensic investigation practices; alongside it brings the great advantage of being investigable and consequently the trust of the client.
Why users keep answering questions in online question answering communities: A theoretical and empirical investigation

This study theorized and validated a model of knowledge sharing continuance in a special type of online community, the online question answering (Q&A) community, in which knowledge exchange is reflected mainly by asking and answering specific questions. We created a model that integrated knowledge sharing factors and knowledge self-efficacy into the expectation confirmation theory. The hypotheses derived ontinuance intention to answer questions enefits of answering questions atisfaction onfirmation nowledge self-efficacy from this model were empirically validated using an online survey conducted among users of a famous online Q&A community in China, “Yahoo! Answers China”. The results suggested that users’ intention to continue sharing knowledge (i.e., answering questions) was directly influenced by users’ ex-post feelings as consisting of two dimensions: satisfaction, and knowledge self-efficacy. Based on the obtained results, we also found that knowledge self-efficacy and confirmation mediated the relationship between benefits and satisfaction.
High-Efficiency Single-Phase AC–AC Converters Without Commutation Problem

This paper proposes single-phase direct pulsewidth modulation (PWM) buck-, boost-, and buck-boost-type ac-ac converters. The proposed converters are implemented with a series-connected freewheeling diode and MOSFET pair, which allows to minimize the switching and conduction losses of the semiconductor devices and resolves the reverse-recovery problem of body diode of MOSFET. The proposed converters are highly reliable because they can solve the shoot-through and dead-time problems of traditional ac-ac converters without voltage/current sensing module, lossy resistor-capacitor (RC) snubbers, or bulky coupled inductors. In addition, they can achieve high obtainable voltage gain and also produce output voltage waveforms of good quality because they do not use lossy snubbers. Unlike the recently developed switching cell (SC) ac-ac converters, the proposed ac-ac converters have no circulating current and do not require bulky coupled inductors; therefore, the total losses, current stresses, and magnetic volume are reduced and efficiency is improved. Detailed analysis and experimental results are provided to validate the novelty and merit of the proposed converters.
Anxiety and cognitive performance: attentional control theory.

Attentional control theory is an approach to anxiety and cognition representing a major development of Eysenck and Calvo's (1992) processing efficiency theory. It is assumed that anxiety impairs efficient functioning of the goal-directed attentional system and increases the extent to which processing is influenced by the stimulus-driven attentional system. In addition to decreasing attentional control, anxiety increases attention to threat-related stimuli. Adverse effects of anxiety on processing efficiency depend on two central executive functions involving attentional control: inhibition and shifting. However, anxiety may not impair performance effectiveness (quality of performance) when it leads to the use of compensatory strategies (e.g., enhanced effort; increased use of processing resources). Directions for future research are discussed.
Adversarial Examples for Evaluating Reading Comprehension Systems

In the SQuaD reading comprehension task systems are given a paragraph from Wikipedia and have to answer a question about it. The answer is guaranteed to be contained within the paragraph. There are 107,785 such paragraph-question-answer tuples in the dataset. Human performance on this task achieves 91.2% accuracy (F1), and the current state-of-the-art system obtains a respectably close 84.7%. Not so fast though! If we adversarially add a single sentence to those paragraphs, in such a way that the added sentences do not contradict the correct answer, nor do they confuse humans, the accuracy of the published models studied plummets from an average of 75% to just 36%.
A Convex Formulation for Learning Task Relationships in Multi-Task Learning

Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning. This formulation can be viewed as a novel generalization of the regularization framework for single-task learning. Besides modeling positive task correlation, our method, called multi-task relationship learning (MTRL), can also describe negative task correlation and identify outlier tasks based on the same underlying principle. Under this regularization framework, the objective function of MTRL is convex. For efficiency, we use an alternating method to learn the optimal model parameters for each task as well as the relationships between tasks. We study MTRL in the symmetric multi-task learning setting and then generalize it to the asymmetric setting as well. We also study the relationships between MTRL and some existing multi-task learning methods. Experiments conducted on a toy problem as well as several benchmark data sets demonstrate the effectiveness of MTRL.
All-atom empirical potential for molecular modeling and dynamics studies of proteins.

New protein parameters are reported for the all-atom empirical energy function in the CHARMM program. The parameter evaluation was based on a self-consistent approach designed to achieve a balance between the internal (bonding) and interaction (nonbonding) terms of the force field and among the solvent-solvent, solvent-solute, and solute-solute interactions. Optimization of the internal parameters used experimental gas-phase geometries, vibrational spectra, and torsional energy surfaces supplemented with ab initio results. The peptide backbone bonding parameters were optimized with respect to data for N-methylacetamide and the alanine dipeptide. The interaction parameters, particularly the atomic charges, were determined by fitting ab initio interaction energies and geometries of complexes between water and model compounds that represented the backbone and the various side chains. In addition, dipole moments, experimental heats and free energies of vaporization, solvation and sublimation, molecular volumes, and crystal pressures and structures were used in the optimization. The resulting protein parameters were tested by applying them to noncyclic tripeptide crystals, cyclic peptide crystals, and the proteins crambin, bovine pancreatic trypsin inhibitor, and carbonmonoxy myoglobin in vacuo and in crystals. A detailed analysis of the relationship between the alanine dipeptide potential energy surface and calculated protein φ, χ angles was made and used in optimizing the peptide group torsional parameters. The results demonstrate that use of ab initio structural and energetic data by themselves are not sufficient to obtain an adequate backbone representation for peptides and proteins in solution and in crystals. Extensive comparisons between molecular dynamics simulations and experimental data for polypeptides and proteins were performed for both structural and dynamic properties. Energy minimization and dynamics simulations for crystals demonstrate that the latter are needed to obtain meaningful comparisons with experimental crystal structures. The presented parameters, in combination with the previously published CHARMM all-atom parameters for nucleic acids and lipids, provide a consistent set for condensed-phase simulations of a wide variety of molecules of biological interest.
Summarizing Unconstrained Videos Using Salient Montages

We present a novel method to summarize unconstrained videos using salient montages (i.e., a “melange” of frames in the video as shown in <xref ref-type="fig" rid="fig1">Fig. 1</xref>, by finding “montageable moments” and identifying the salient people and actions to depict in each montage. Our method aims at addressing the increasing need for generating concise visualizations from the large number of videos being captured from portable devices. Our main contributions are (1) the process of finding salient people and moments to form a montage, and (2) the application of this method to videos taken “in the wild” where the camera moves freely. As such, we demonstrate results on head-mounted cameras, where the camera moves constantly, as well as on videos downloaded from YouTube. In our experiments, we show that our method can reliably detect and track humans under significant action and camera motion. Moreover, the predicted salient people are more accurate than results from state-of-the-art video salieny method <xref ref-type="bibr" rid="ref1">[1]</xref> . Finally, we demonstrate that a novel “montageability” score can be used to retrieve results with relatively high precision which allows us to present high quality montages to users.
Grid cells in pre- and parasubiculum

Allocentric space is mapped by a widespread brain circuit of functionally specialized cell types located in interconnected subregions of the hippocampal-parahippocampal cortices. Little is known about the neural architectures required to express this variety of firing patterns. In rats, we found that one of the cell types, the grid cell, was abundant not only in medial entorhinal cortex (MEC), where it was first reported, but also in pre- and parasubiculum. The proportion of grid cells in pre- and parasubiculum was comparable to deep layers of MEC. The symmetry of the grid pattern and its relationship to the theta rhythm were weaker, especially in presubiculum. Pre- and parasubicular grid cells intermingled with head-direction cells and border cells, as in deep MEC layers. The characterization of a common pool of space-responsive cells in architecturally diverse subdivisions of parahippocampal cortex constrains the range of mechanisms that might give rise to their unique functional discharge phenotypes.