nils-herrmann/clean_scirepeval huggingface dataset:
Bayesian Non-negative Matrix Factorization

We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates. We discuss how the Gibbs sampler can be used for model order selection by estimating the marginal likelihood, and compare with the Bayesian information criterion. For computing the maximum a posteriori estimate we present an iterated conditional modes algorithm that rivals existing state-of-the-art NMF algorithms on an image feature extraction problem.
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution

This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The proposed approach includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide the estimation process. Another key component is the unnatural bi-l0-l2-norm regularization imposed on the super-resolved, sharp image and the blur-kernel, which is shown to be quite beneficial for estimating the blur-kernel accurately. The numerical optimization is implemented by coupling the splitting augmented Lagrangian and the conjugate gradient (CG). Using the pre-estimated blur-kernel, we finally reconstruct the SR image by a very simple non-blind SR method that uses a natural image prior. The proposed approach is demonstrated to achieve better performance than the recent method by Michaeli and Irani  in both terms of the kernel estimation accuracy and image SR quality.
Thumbs up? Sentiment Classification using Machine Learning Techniques

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.
FlexiStickers: photogrammetric texture mapping using casual images

Texturing 3D models using casual images has gained importance in the last decade, with the advent of huge databases of images. We present a novel approach for performing this task, which manages to account for the 3D geometry of the photographed object. Our method overcomes the limitation of both the constrained-parameterization approach, which does not account for the photography effects, and the photogrammetric approach, which cannot handle arbitrary images. The key idea of our algorithm is to formulate the mapping estimation as a Moving-Least-Squares problem for recovering local camera parameters at each vertex. The algorithm is realized in a FlexiStickers application, which enables fast interactive texture mapping using a small number of constraints.
K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-histogram, a new efficient algorithm for clustering categorical data. The k-histogram algorithm extends the k-means algorithm to categorical domain by replacing the means of clusters with histograms, and dynamically updates histograms in the clustering process. Experimental results on real datasets show that k-histogram algorithm can produce better clustering results than k-modes algorithm, the one related with our work most closely.
E-ethical leadership for virtual project teams

This paper presents a review of current literature on ethical theories as they relate to ethical leadership in the virtual business environment (e-ethics) and virtual project leadership. Ethical theories are reviewed in relation to virtual project management, such as participative management, Theory Y, and its relationship to utilitarianism; Kantian ethics, motivation, and trust; communitarian ethics, ethic of care and egalitarianism; Stakeholder Theory; and the use of political tactics. Challenges to e-ethical leadership are presented and responses to these issues discussed. The conclusion presents four propositions for future research. The purpose of this paper is to identify secondary literature on e-ethics and how this new area of business ethics may affect the leaders of virtual project teams.
Using the Wiktionary Graph Structure for Synonym Detection

This paper presents our work on using the graph structure of Wiktionary for synonym detection. We implement semantic relatedness metrics using both a direct measure of information flow on the graph and a comparison of the list of vertices found to be “close” to a given vertex. Our algorithms, evaluated on ESL 50, TOEFL 80 and RDWP 300 data sets, perform better than or comparable to existing semantic relatedness measures.
Deep Learning meets Semantic Web: A feasibility study with the Cardiovascular Disease Ontology and PubMed citations

Automatic identification of gene and protein names from biomedical publications can help curators and researchers to keep up with the findings published in the scientific literature. As of today, this is a challenging task related to information retrieval, and in the realm of Big Data Analytics.  To investigate the feasibility of using word embed-dings (i.e. distributed word representations) from Deep Learning algorithms together with terms from the Cardiovascular Disease Ontology (CVDO) as a step to identifying omics information encoded in the biomedical literature.  Word embeddings were generated using the neural language models CBOW and Skip-gram with an input of more than 14 million PubMed citations (titles and abstracts) corresponding to articles published between 2000 and 2016. Then the abstracts of selected papers from the sysVASC systematic review were manually annotated with gene/protein names. We set up two experiments that used the word embeddings to produce term variants for gene/protein names: the first experiment used the terms manually annotated from the papers; the second experiment enriched/expanded the annotated terms using terms from the human-readable labels of key classes (gene/proteins) from the CVDO ontology. CVDO is formalised in the W3C Web Ontology Language (OWL) and contains 172,121 UniProt Knowledgebase protein classes related to human and 86,792 UniProtKB protein classes related to mouse. The hypothesis is that by enriching the original annotated terms, a better context is provided, and therefore, it is easier to obtain suitable (full and/or partial) term variants for gene/protein names from word embed-dings.  From the papers manually annotated, a list of 107 terms (gene/protein names) was acquired. As part of the word embeddings generated from CBOW and Skip-gram, a lexicon with more than 9 million terms was created. Using the cosine similarity metric, a list of the 12 top-ranked terms was generated from word embeddings for query terms present in the generated lexicon. Domain experts evaluated a total of 1968 pairs of terms and classified the retrieved terms as: TV (term variant); PTV (partial term variant); and NTV (non term variant, meaning none of the previous two categories). In experiment I, Skip-gram finds the double amount of (full and/or partial) term variants for gene/protein names as compared with CBOW. Using Skip-gram, the weighted Cohen's Kappa inter-annotator agreement for two domain experts was 0.80 for the first experiment and 0.74 for the second experiment. In the first experiment, suitable (full and/or partial) term variants were found …
Facility location models for distribution system design

The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational performance. This paper reviews some of the contributions to the current state-of-the-art. In particular, continuous location models, network location models, mixed-integer programming models, and applications are summarized.
Fall Detection in Homes of Older Adults Using the Microsoft Kinect

A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.
The "Reading the Mind in the Eyes" Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism.

In 1997 in this Journal we published the "Reading the Mind in the Eyes" Test, as a measure of adult "mentalising". Whilst that test succeeded in discriminating a group of adults with Asperger syndrome (AS) or high-functioning autism (HFA) from controls, it suffered from several psychometric problems. In this paper these limitations are rectified by revising the test. The Revised Eyes Test was administered to a group of adults with AS or HFA (N = 15) and again discriminated these from a large number of normal controls (N = 239) drawn from different samples. In both the clinical and control groups the Eyes Test was inversely correlated with the Autism Spectrum Quotient (the AQ), a measure of autistic traits in adults of normal intelligence. The Revised Eyes Test has improved power to detect subtle individual differences in social sensitivity.
Efficient matrix completion for seismic data reconstruction

Despite recent developments in improved acquisition, seismic data often remains undersampled along source and receiver coordinates, resulting in incomplete data for key applications such as migration and multiple prediction. We interpret the missing-trace interpolation problem in the context of matrix completion and outline three practical principles for using low-rank optimization techniques to recover seismic data. Specifically, we strive for recovery scenarios wherein the original signal is low rank and the subsampling scheme increases the singular values of the matrix. We employ an optimization program that restores this low rank structure to recover the full volume. Omitting one or more of these principles can lead to poor interpolation results, as we show experimentally. In light of this theory, we compensate for the high-rank behavior of data in the source-receiver domain by employing the midpoint-offset transformation for 2D data and a source-receiver permutation for 3D data to reduce the overall singular values. Simultaneously, in order to work with computationally feasible algorithms for large scale data, we use a factorization-based approach to matrix completion, which significantly speeds up the computations compared to repeated singular value decompositions without reducing the recovery quality. In the context of our theory and experiments, we also show that windowing the data too aggressively can have adverse effects on the recovery quality. To overcome this problem, we carry out our interpolations for each frequency independently while working with the entire frequency slice. The result is a computationally efficient, theoretically motivated framework for interpolating missing-trace data. Our tests on realistic twoand three-dimensional seismic data sets show that our method compares favorably, both in terms of computational speed and recovery quality, to existing curvelet-based and tensor-based techniques. Geophysics This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
Stereo Panorama with a Single Camera

Full panoramic images, covering 360 degrees, can be created either by using panoramic cameras or by mosaicing together many regular images. Creating panoramic views instereo, where one panorama is generated for the left eye, and another panorama is generated for the right eye is more problematic. Earlier attempts to mosaic images from a rotating pair of stereo cameras faced severe problems of parallax and of scale changes. A new family of multiple viewpoint image projections, the Circular Projections, is developed. Two panoramic images taken using such projections can serve as a panoramic stereo pair. A system is described to generates a stereo panoramic image using circular projections from images or video taken by a single rotating camera. The system works in real-time on a PC. It should be noted that the stereo images are created without computation of 3D structure, and the depth effects are created only in the viewer's brain.
X-Band SIW cavity-backed patch antenna for radar applications

A Substrate Integrated Waveguide (SIW) cavity-backed microstrip patch antenna for radar applications operating in X Band is designed. In order to increase the bandwidth, the SIW technology is used to build a circular cavity which surrounds a modified rectangular patch. The modified patch has truncated corners and two cuts in the lateral sides. Accurate electromagnetic investigation has been performed using CST Microwave Studio© in terms of radiation pattern, bandwidth, and matching. The prototype employs a Taconic RF-35 substrate with εr= 3.5 and thickness of 1.52 mm. A good agreement between simulations and experiments is obtained. The proposed antenna is a promising candidate for a low-cost implementation of radar antenna arrays.
Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis

This paper approaches novel methods to segment the nucleus and cytoplasm of white blood cells (WBC). This information is the basis to perform higher level tasks such as automatic differential counting, which plays an important role in the diagnosis of different diseases. We explore the image simplification and contour regularization resulting from the application of the selfdual multiscale morphological toggle (SMMT), an operator with scale-space properties. To segment the nucleus, the image preprocessing with SMMT has shown to be essential to ensure the accuracy of two well-known image segmentations techniques, namely, watershed transform and Level-Set methods. To identify the cytoplasm region, we propose two different schemes, based on granulometric analysis and on morphological transformations. The proposed methods have been successfully applied to a large number of images, showing promising segmentation and classification results for varying cell appearance and image quality, encouraging future works.
Generalized fisheye views

In many contexts, humans often represent their own “neighborhood” in great detail, yet only major landmarks further away. This suggests that such views (“fisheye views”) might be useful for the computer display of large information structures like programs, data bases, online text, etc. This paper explores fisheye views presenting, in turn, naturalistic studies, a general formalism, a specific instantiation, a resulting computer program, example displays and an evaluation.
Understanding of Antecedents to Achieve Customer Trust and Customer Intention to Purchase E-Commerce in Social Media , an Empirical Assessment

2016 Revised Mar 27, 2017 Accepted  2017 This study aims to analyze empirically three factors antecedents of trust they are system quality, information quality, and service quality. Customer trust is used in determining customer intention to purchase of e-commerce in social media (facebook). A number of respondents were 451. The results of this study concluded that three factors antecedents of trust directly had a positive impact to customer trust and indirectly had positive impact on customer intention to purchase in e-commerce transactions on social media.
An overview of audio information retrieval

The problem of audio information retrieval is familiar to anyone who has returned from vacation to find an answering machine full of messages. While there is not yet an “AltaVista” for the audio data type, many workers are finding ways to automatically locate, index, and browse audio using recent advances in speech recognition and machine listening. This paper reviews the state of the art in audio information retrieval, and presents recent advances in automatic speech recognition, word spotting, speaker and music identification, and audio similarity with a view towards making audio less “opaque”. A special section addresses intelligent interfaces for navigating and browsing audio and multimedia documents, using automatically derived information to go beyond the tape recorder metaphor.
Fundamental limits of RSS fingerprinting based indoor localization

Indoor localization has been an active research field for decades, where the received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization techniques such as the recently proposed one building the fingerprint database with crowd-sourcing. While efforts have been dedicated to improve the accuracy and efficiency of localization, the fundamental limits of RSS fingerprinting based methodology itself is still unknown in a theoretical perspective. In this paper, we present a general probabilistic model to shed light on a fundamental question: how good the RSS fingerprinting based indoor localization can achieve? Concretely, we present the probability that a user can be localized in a region with certain size, given the RSS fingerprints submitted to the system. We reveal the interaction among the localization accuracy, the reliability of location estimation and the number of measurements in the RSS fingerprinting based location determination. Moreover, we present the optimal fingerprints reporting strategy that can achieve the best accuracy for given reliability and the number of measurements, which provides a design guideline for the RSS fingerprinting based indoor localization facilitated by crowdsourcing paradigm.
SemEval-2007 Task 02: Evaluating Word Sense Induction and Discrimination Systems

Word Sense Disambiguation (WSD) is a key enabling-technology. Supervised WSD techniques are the best performing in public evaluations, but need large amounts of hand-tagging data. Existing hand-annotated corpora like SemCor (Miller et al., 1993), which is annotated with WordNet senses (Fellbaum, 1998) allow for a small improvement over the simple most frequent sense heuristic, as attested in the all-words track of the last Senseval competition (Snyder and Palmer, 2004). In theory, larger amounts of training data (SemCor has approx. 500M words) would improve the performance of supervised WSD, but no current project exists to provide such an expensive resource. Another problem of the supervised approach is that the inventory and distribution of senses changes dramatically from one domain to the other, requiring additional hand-tagging of corpora (Martı́nez and Agirre, 2000; Koeling et al., 2005). Supervised WSD is based on the “fixed-list of senses” paradigm, where the senses for a target word are a closed list coming from a dictionary or lexicon. Lexicographers and semanticists have long warned about the problems of such an approach, where senses are listed separately as discrete entities, and have argued in favor of more complex representations, where, for instance, senses are dense regions in a continuum (Cruse, 2000). Unsupervised Word Sense Induction and Discrimination (WSID, also known as corpus-based unsupervised systems) has followed this line of thinking, and tries to induce word senses directly from the corpus. Typical WSID systems involve clustering techniques, which group together similar examples. Given a set of induced clusters (which represent word uses or senses1), each new occurrence of the target word will be compared to the clusters and the most similar cluster will be selected as its sense. One of the problems of unsupervised systems is that of managing to do a fair evaluation. Most of current unsupervised systems are evaluated in-house, with a brief comparison to a re-implementation of a former system, leading to a proliferation of unsupervised systems with little ground to compare among them. The goal of this task is to allow for comparison across sense-induction and discrimination systems, and also to compare these systems to other supervised and knowledge-based systems. The paper is organizes as follows. Section 2 presents the evaluation framework used in this task. Section 3 presents the systems that participated in the task, and the official results. Finally, Section 4 draws the conclusions.
Definition Modeling: Learning to define word embeddings in natural language

Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings’ semantics. We introduce definition modeling, the task of generating a definition for a given word and its embedding. We present several definition model architectures based on recurrent neural networks, and experiment with the models over multiple data sets. Our results show that a model that controls dependencies between the word being defined and the definition words performs significantly better, and that a characterlevel convolution layer designed to leverage morphology can complement word-level embeddings. Finally, an error analysis suggests that the errors made by a definition model may provide insight into the shortcomings of word embeddings.