nils-herrmann/clean_scirepeval huggingface dataset:
Mapping the knowledge domain and the theme evolution of appropriability research between 1986 and 2016: a scientometric review

The scholars in the research domains of innovation and strategic management concerned about the appropriability for about 30 years or more. They focused on appropriability research and constantly evolving. In this paper, we analyze 30 years (1986–2016) literature on appropriability studies from Web of Science Core Collection database. A cited reference clustering map of different periods and terms co-occurrence map have been generated using bibliometric analysis and content analysis. Based on this, we study the evolutionary trajectory, mechanisms and theoretical architecture of appropriability research and explore further research directions. The results indicate that the essence of the appropriability research evolution is the perception changes in opening and sharing, value creation and value growth, and future research is focusing on role of appropriability in the platform governance, generative appropriability and the evolution of the problem-solving mechanisms.
Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks

Resource-efficient convolution neural networks enable not only the intelligence on edge devices but also opportunities in system-level optimization such as scheduling. In this work, we aim to improve the performance of resource-constrained filter pruning by merging two sub-problems commonly considered, i.e., (i) how many filters to prune for each layer and (ii) which filters to prune given a per-layer pruning budget, into a global filter ranking problem. Our framework entails a novel algorithm, dubbed layer-compensated pruning, where meta-learning is involved to determine better solutions. We show empirically that the proposed algorithm is superior to prior art in both effectiveness and efficiency. Specifically, we reduce the accuracy gap between the pruned and original networks from 0.9% to 0.7% with 8x reduction in time needed for meta-learning, i.e., from 1 hour down to 7 minutes. To this end, we demonstrate the effectiveness of our algorithm using VGG, ResNet, and MobileNetV2 networks under CIFAR-10, ImageNet, and Bird-200 datasets.
BigSim: a parallel simulator for performance prediction of extremely large parallel machines

We present a parallel simulator - BigSim - for predicting performance of machines with a very large number of processors. The simulator provides the ability to make performance predictions for machines such as BlueGene/L, based on actual execution of real applications. We present this capability using case-studies of some application benchmarks. Such a simulator is useful to evaluate the performance of specific applications on such machines even before they are built. A sequential simulator may be too slow or infeasible. However, a parallel simulator faces problems of causality violations. We describe our scheme based on ideas from parallel discrete event simulation and utilize inherent determinacy of many parallel applications. We also explore techniques for optimizing such parallel simulations of machines with large number of processors on existing machines with fewer number of processors.
Analyzing Sentiments Expressed on Twitter by UK Energy Company Consumers

Automatic sentiment analysis provides an effective way to gauge public opinion on any topic of interest. However, most sentiment analysis tools require a general sentiment lexicon to automatically classify sentiments or opinion in a text. One of the challenges presented by using a general sentiment lexicon is that it is insensitive to the domain since the scores assigned to the words are fixed. As a result, while one general sentiment lexicon might perform well in one domain, the same lexicon might perform poorly in another domain. Most sentiment lexica will need to be adjusted to suit the specific domain to which it is applied. In this paper, we present results of sentiment analysis expressed on Twitter by UK energy consumers. We optimised the accuracy of the sentiment analysis results by combining functions from two sentiment lexica. We used the first lexicon to extract the sentiment-bearing terms and negative sentiments since it performed well in detecting these. We then used a second lexicon to classify the rest of the data. Experimental results show that this method improved the accuracy of the results compared to the common practice of using only one lexicon.
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities

The softmax representation of probabilities for categorical variables plays a prominent role in modern machine learning with numerous applications in areas such as large scale classification, neural language modeling and recommendation systems. However, softmax estimation is very expensive for large scale inference because of the high cost associated with computing the normalizing constant. Here, we introduce an efficient approximation to softmax probabilities which takes the form of a rigorous lower bound on the exact probability. This bound is expressed as a product over pairwise probabilities and it leads to scalable estimation based on stochastic optimization. It allows us to perform doubly stochastic estimation by subsampling both training instances and class labels. We show that the new bound has interesting theoretical properties and we demonstrate its use in classification problems.
Traffic Sign Classification Using Deep Inception Based Convolutional Networks

In this work, we propose a novel deep network for traffic sign classification that achieves outstanding performance on GTSRB surpassing all previous methods. Our deep network consists of spatial transformer layers and a modified version of inception module specifically designed for capturing local and global features together. This features adoption allows our network to classify precisely intraclass samples even under deformations. Use of spatial transformer layer makes this network more robust to deformations such as translation, rotation, scaling of input images. Unlike existing approaches that are developed with hand-crafted features, multiple deep networks with huge parameters and data augmentations, our method addresses the concern of exploding parameters and augmentations. We have achieved the state-of-the-art performance of 99.81% on GTSRB dataset.
Using WordNet-Based Context Vectors To Estimate The Semantic Relatedness Of Concepts

In this paper, we introduce a WordNetbased measure of semantic relatedness by combining the structure and content of WordNet with co–occurrence information derived from raw text. We use the co–occurrence information along with the WordNet definitions to build gloss vectors corresponding to each concept in WordNet. Numeric scores of relatedness are assigned to a pair of concepts by measuring the cosine of the angle between their respective gloss vectors. We show that this measure compares favorably to other measures with respect to human judgments of semantic relatedness, and that it performs well when used in a word sense disambiguation algorithm that relies on semantic relatedness. This measure is flexible in that it can make comparisons between any two concepts without regard to their part of speech. In addition, it can be adapted to different domains, since any plain text corpus can be used to derive the co–occurrence information.
A Radiating Near-Field Patch Rectenna for Wireless Power Transfer to Medical Implants at 2.4 GHz

A radiating near-field method of recharging and activating medical implants using a 2.4-GHz rectifying patch antenna (rectenna) is designed and tested. Traditional near-field charging uses magnetically coupled coils, but these are highly sensitive to misalignments between the transmitter and receiver. In contrast, the proposed design employs the principles of wireless power transfer using radiating antennas. These antennas provide a misalignment-insensitive power delivery method, even when the receive antenna footprint is small (27.5 mm × 19.75 mm). A misalignment analysis is performed up to 15 cm, showing a maximum loss of 7.5 dB. As a proof-of-concept demonstration, a rectenna receiver was fabricated consisting of a patch antenna attached to a radio frequency (RF) rectifier. This integrated rectifier is a voltage quadrupling circuit that provides RF–DC rectification with efficiency of 40% at 0 dBm. For validation, a real-time actuation of a medical drug pump is demonstrated using only wirelessly transmitted power with no additional power storage elements.
Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling

The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework. Here a variant of a fully convolutional network is proposed to predict a pixel-wise semantic labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii) background. The geometric cues are exploited using a state-of-the-art detection approach that predicts obstacles from stereo input images via model-based statistical hypothesis tests. We present a principled Bayesian framework to fuse the semantic and stereo-based detection results. The mid-level Stixel representation is used to describe obstacles in a flexible, compact and robust manner. We evaluate our new obstacle detection system on the Lost and Found dataset, which includes very challenging scenes with obstacles of only 5 cm height. Overall, we report a major improvement over the state-of-the-art, with a performance gain of 27.4%. In particular, we achieve a detection rate of over 90% for distances of up to 50 m. Our system operates at 22 Hz on our self-driving platform.
On Fast Dropout and its Applicability to Recurrent Networks

Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data. Recent work on advancing the state of the art has been focused on the optimization or modelling of RNNs, mostly motivated by adressing the problems of the vanishing and exploding gradients. The control of overfitting has seen considerably less attention. This paper contributes to that by analyzing fast dropout, a recent regularization method for generalized linear models and neural networks from a back-propagation inspired perspective. We show that fast dropout implements a quadratic form of an adaptive, per-parameter regularizer, which rewards large weights in the light of underfitting, penalizes them for overconfident predictions and vanishes at minima of an unregularized training loss. The derivatives of that regularizer are exclusively based on the training error signal. One consequence of this is the absence of a global weight attractor, which is particularly appealing for RNNs, since the dynamics are not biased towards a certain regime. We positively test the hypothesis that this improves the performance of RNNs on four musical data sets.
Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis

Higher-order tensors and their decompositions are abundantly present in domains such as signal processing (e.g., higher-order statistics  and sensor array processing ), scientific computing (e.g., discretized multivariate functions [3]?[6]), and quantum information theory (e.g., representation of quantum many-body states ). In many applications, the possibly huge tensors can be approximated well by compact multilinear models or decompositions. Tensor decompositions are more versatile tools than the linear models resulting from traditional matrix approaches. Compared to matrices, tensors have at least one extra dimension. The number of elements in a tensor increases exponentially with the number of dimensions, and so do the computational and memory requirements. The exponential dependency (and the problems that are caused by it) is called the curse of dimensionality. The curse limits the order of the tensors that can be handled. Even for a modest order, tensor problems are often large scale. Large tensors can be handled, and the curse can be alleviated or even removed by using a decomposition that represents the tensor instead of using the tensor itself. However, most decomposition algorithms require full tensors, which renders these algorithms infeasible for many data sets. If a tensor can be represented by a decomposition, this hypothesized structure can be exploited by using compressed sensing (CS) methods working on incomplete tensors, i.e., tensors with only a few known elements.
A Simple Introduction to Maximum Entropy Models for Natural Language Processing

Many problems in natural language processing can be viewed as lin guistic classi cation problems in which linguistic contexts are used to pre dict linguistic classes Maximum entropy models o er a clean way to com bine diverse pieces of contextual evidence in order to estimate the proba bility of a certain linguistic class occurring with a certain linguistic con text This report demonstrates the use of a particular maximum entropy model on an example problem and then proves some relevant mathemat ical facts about the model in a simple and accessible manner This report also describes an existing procedure called Generalized Iterative Scaling which estimates the parameters of this particular model The goal of this report is to provide enough detail to re implement the maximum entropy models described in Ratnaparkhi Reynar and Ratnaparkhi Ratnaparkhi and also to provide a simple explanation of the max imum entropy formalism  Many problems in natural language processing NLP can be re formulated as statistical classi cation problems in which the task is to estimate the probability of class a occurring with context b or p a b Contexts in NLP tasks usually include words and the exact context depends on the nature of the task for some tasks the context b may consist of just a single word while for others b may consist of several words and their associated syntactic labels Large text corpora usually contain some information about the cooccurrence of a s and b s but never enough to completely specify p a b for all possible a b pairs since the words in b are typically sparse The problem is then to nd a method for using the sparse evidence about the a s and b s to reliably estimate a probability model p a b Consider the Principle of Maximum Entropy Jaynes Good which states that the correct distribution p a b is that which maximizes en tropy or uncertainty subject to the constraints which represent evidence i e the facts known to the experimenter Jaynes discusses its advan tages in making inferences on the basis of partial information we must use that probability distribution which has maximum entropy sub ject to whatever is known This is the only unbiased assignment we can make to use any other would amount to arbitrary assumption of information which by hypothesis we do not have More explicitly if A denotes the set of possible classes and B denotes the set of possible contexts p should maximize the entropy H p X
Combining Bilingual and Comparable Corpora for Low Resource Machine Translation

Statistical machine translation (SMT) performance suffers when models are trained on only small amounts of parallel data. The learned models typically have both low accuracy (incorrect translations and feature scores) and low coverage (high out-of-vocabulary rates). In this work, we use an additional data resource, comparable corpora, to improve both. Beginning with a small bitext and corresponding phrase-based SMT model, we improve coverage by using bilingual lexicon induction techniques to learn new translations from comparable corpora. Then, we supplement the model’s feature space with translation scores estimated over comparable corpora in order to improve accuracy. We observe improvements between 0.5 and 1.7 BLEU translating Tamil, Telugu, Bengali, Malayalam, Hindi, and Urdu into English.
LIBSVM: A library for support vector machines

LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Causal protein-signaling networks derived from multiparameter single-cell data.

Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
Case Study of a Computer Based Examination System.

Electronic supported assessment or e-Assessment is a field of growing importance, but it has yet to make a significant impact in the Australian higher education sector (Byrnes & Ellis, 2006). Current computer based assessment models focus on the assessment of knowledge rather than deeper understandings, using multiple choice type questions, and blocking access to more sophisticated software tools. This study explored a new system based on a customised version of an open source live CD, based on Ubuntu which was used with three groups of pre-service teachers (N=270). Students had divided opinions about using computers or paper for their examinations, but prior exposure to computer based assessment was a highly significant factor for preferring the computer medium. Reflecting upon their experience, students found the noise of computer keyboards a distraction during the eExamination and preferred fewer on-screen windows. The new system allowed eExaminations to be taken securely on student owned laptop computers and was supervised by invigilators without specialist information technology skills. The system has been made available for other researchers to use at
Tumor-associated copy number changes in the circulation of patients with prostate cancer identified through whole-genome sequencing

Patients with prostate cancer may present with metastatic or recurrent disease despite initial curative treatment. The propensity of metastatic prostate cancer to spread to the bone has limited repeated sampling of tumor deposits. Hence, considerably less is understood about this lethal metastatic disease, as it is not commonly studied. Here we explored whole-genome sequencing of plasma DNA to scan the tumor genomes of these patients non-invasively. We wanted to make whole-genome analysis from plasma DNA amenable to clinical routine applications and developed an approach based on a benchtop high-throughput platform, that is, Illuminas MiSeq instrument. We performed whole-genome sequencing from plasma at a shallow sequencing depth to establish a genome-wide copy number profile of the tumor at low costs within 2 days. In parallel, we sequenced a panel of 55 high-interest genes and 38 introns with frequent fusion breakpoints such as the TMPRSS2-ERG fusion with high coverage. After intensive testing of our approach with samples from 25 individuals without cancer we analyzed 13 plasma samples derived from five patients with castration resistant (CRPC) and four patients with castration sensitive prostate cancer (CSPC). The genome-wide profiling in the plasma of our patients revealed multiple copy number aberrations including those previously reported in prostate tumors, such as losses in 8p and gains in 8q. High-level copy number gains in the AR locus were observed in patients with CRPC but not with CSPC disease. We identified the TMPRSS2-ERG rearrangement associated 3-Mbp deletion on chromosome 21 and found corresponding fusion plasma fragments in these cases. In an index case multiregional sequencing of the primary tumor identified different copy number changes in each sector, suggesting multifocal disease. Our plasma analyses of this index case, performed 13 years after resection of the primary tumor, revealed novel chromosomal rearrangements, which were stable in serial plasma analyses over a 9-month period, which is consistent with the presence of one metastatic clone. The genomic landscape of prostate cancer can be established by non-invasive means from plasma DNA. Our approach provides specific genomic signatures within 2 days which may therefore serve as 'liquid biopsy'.
QuickTime VR: an image-based approach to virtual environment navigation

Traditionally, virtual reality systems use 3D computer graphics to model and render virtual environments in real-time. This approach usually requires laborious modeling and expensive special purpose rendering hardware. The rendering quality and scene complexity are often limited because of the real-time constraint. This paper presents a new approach which uses 360-degree cylindrical panoramic images to compose a virtual environment. The panoramic image is digitally warped on-the-fly to simulate camera panning and zooming. The panoramic images can be created with computer rendering, specialized panoramic cameras or by "stitching" together overlapping photographs taken with a regular camera. Walking in a space is currently accomplished by "hopping" to different panoramic points. The image-based approach has been used in the commercial product QuickTime VR, a virtual reality extension to Apple Computer's QuickTime digital multimedia framework. The paper describes the architecture, the file format, the authoring process and the interactive players of the VR system. In addition to panoramic viewing, the system includes viewing of an object from different directions and hit-testing through orientation-independent hot spots.