nils-herrmann/clean_scirepeval huggingface dataset:
Using Word Embeddings

In this paper, we investigate the application of machine learning techniques and word embeddings to the task of Recognizing Textual Entailment (RTE) in Social Media. We look at a manually labeled dataset (Lendvai et al., 2016) consisting of user generated short texts posted on Twitter (tweets) and related to four recent media events (the Charlie Hebdo shooting, the Ottawa shooting, the Sydney Siege, and the German Wings crash) and test to what extent neural techniques and embeddings are able to distinguish between tweets that entail or contradict each other or that claim unrelated things. We obtain comparable results to the state of the art in a train-test setting, but we show that, due to the noisy aspect of the data, results plummet in an evaluation strategy crafted to better simulate a real-life train-test scenario.
A Planar Switchable 3-D-Coverage Phased Array Antenna and Its User Effects for 28-GHz Mobile Terminal Applications

This paper introduces a planar switchable 3-D-coverage phased array for 28-GHz mobile terminal applications. In order to realize 3-D-coverage beam scan with a simple planar array, chassis surface waves are efficiently excited and controlled by three identical slot subarrays. Three subarrays switch their beams to three distinct regions. Each subarray works as a phased array to steer the beam within each region. Large coverage efficiency is achieved. (e.g., 80% of the space sphere has the realized gain of over 8 dBi.) The proposed antenna covers a bandwidth of over 2 GHz in the band of 28 GHz. User effects on the switchable array are also studied in both data mode and talk mode (voice) at 28 GHz. In talk mode, good directivity and beam switching can be realized by placing the switchable array at the top of the chassis (close to the index finger). And the user shadowing can be significantly reduced by placing it at the bottom of the chassis (close to the palm). In data mode, the switchable array, mounted at the top, achieves less body loss and larger coverage than at the bottom. The proposed antenna is fabricated and measured. The array at the top in talk mode is measured with a real human. The measurements align well with simulations.
Biology of Bone Tissue: Structure, Function, and Factors That Influence Bone Cells

Bone tissue is continuously remodeled through the concerted actions of bone cells, which include bone resorption by osteoclasts and bone formation by osteoblasts, whereas osteocytes act as mechanosensors and orchestrators of the bone remodeling process. This process is under the control of local (e.g., growth factors and cytokines) and systemic (e.g., calcitonin and estrogens) factors that all together contribute for bone homeostasis. An imbalance between bone resorption and formation can result in bone diseases including osteoporosis. Recently, it has been recognized that, during bone remodeling, there are an intricate communication among bone cells. For instance, the coupling from bone resorption to bone formation is achieved by interaction between osteoclasts and osteoblasts. Moreover, osteocytes produce factors that influence osteoblast and osteoclast activities, whereas osteocyte apoptosis is followed by osteoclastic bone resorption. The increasing knowledge about the structure and functions of bone cells contributed to a better understanding of bone biology. It has been suggested that there is a complex communication between bone cells and other organs, indicating the dynamic nature of bone tissue. In this review, we discuss the current data about the structure and functions of bone cells and the factors that influence bone remodeling.
Discrete Signal Processing on Graphs: Sampling Theory

We propose a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory. We show that perfect recovery is possible for graph signals bandlimited under the graph Fourier transform. The sampled signal coefficients form a new graph signal, whose corresponding graph structure preserves the first-order difference of the original graph signal. For general graphs, an optimal sampling operator based on experimentally designed sampling is proposed to guarantee perfect recovery and robustness to noise; for graphs whose graph Fourier transforms are frames with maximal robustness to erasures as well as for Erdös-Rényi graphs, random sampling leads to perfect recovery with high probability. We further establish the connection to the sampling theory of finite discrete-time signal processing and previous work on signal recovery on graphs. To handle full-band graph signals, we propose a graph filter bank based on sampling theory on graphs. Finally, we apply the proposed sampling theory to semi-supervised classification of online blogs and digit images, where we achieve similar or better performance with fewer labeled samples compared to previous work.
Dynamic Word Embeddings

We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2vec (Mikolov et al., 2013b). These embedding vectors are connected in time through a latent diffusion process. We describe two scalable variational inference algorithms—skipgram smoothing and skip-gram filtering—that allow us to train the model jointly over all times; thus learning on all data while simultaneously allowing word and context vectors to drift. Experimental results on three different corpora demonstrate that our dynamic model infers word embedding trajectories that are more interpretable and lead to higher predictive likelihoods than competing methods that are based on static models trained separately on time slices.
Urban scene segmentation with laser-constrained CRFs

Robots typically possess sensors of different modalities, such as colour cameras, inertial measurement units, and 3D laser scanners. Often, solving a particular problem becomes easier when more than one modality is used. However, while there are undeniable benefits to combine sensors of different modalities the process tends to be complicated. Segmenting scenes observed by the robot into a discrete set of classes is a central requirement for autonomy as understanding the scene is the first step to reason about future situations. Scene segmentation is commonly performed using either image data or 3D point cloud data. In computer vision many successful methods for scene segmentation are based on conditional random fields (CRF) where the maximum a posteriori (MAP) solution to the segmentation can be obtained by inference. In this paper we devise a new CRF inference method for scene segmentation that incorporates global constraints, enforcing the sets of nodes are assigned the same class label. To do this efficiently, the CRF is formulated as a relaxed quadratic program whose MAP solution is found using a gradient-based optimisation approach. The proposed method is evaluated on images and 3D point cloud data gathered in urban environments where image data provides the appearance features needed by the CRF, while the 3D point cloud data provides global spatial constraints over sets of nodes. Comparisons with belief propagation, conventional quadratic programming relaxation, and higher order potential CRF show the benefits of the proposed method.
Measuring factors affecting implementation of health innovations: a systematic review of structural, organizational, provider, patient, and innovation level measures

Two of the current methodological barriers to implementation science efforts are the lack of agreement regarding constructs hypothesized to affect implementation success and identifiable measures of these constructs. In order to address these gaps, the main goals of this paper were to identify a multi-level framework that captures the predominant factors that impact implementation outcomes, conduct a systematic review of available measures assessing constructs subsumed within these primary factors, and determine the criterion validity of these measures in the search articles.



We conducted a systematic literature review to identify articles reporting the use or development of measures designed to assess constructs that predict the implementation of evidence-based health innovations. Articles published through 12 August 2012 were identified through MEDLINE, CINAHL, PsycINFO and the journal Implementation Science. We then utilized a modified five-factor framework in order to code whether each measure contained items that assess constructs representing structural, organizational, provider, patient, and innovation level factors. Further, we coded the criterion validity of each measure within the search articles obtained.



Our review identified 62 measures. Results indicate that organization, provider, and innovation-level constructs have the greatest number of measures available for use, whereas structural and patient-level constructs have the least. Additionally, relatively few measures demonstrated criterion validity, or reliable association with an implementation outcome (e.g., fidelity).



In light of these findings, our discussion centers on strategies that researchers can utilize in order to identify, adapt, and improve extant measures for use in their own implementation research. In total, our literature review and resulting measures compendium increases the capacity of researchers to conceptualize and measure implementation-related constructs in their ongoing and future research.
Emergence of Digital Twins

use tools and technologies to “map” data and information from atoms to bits. It is gaining momentum because the “map” can act as a “compass” to reveal the status of atoms (things, devices, components, parts, machines), hence, visibility and real-time transparency. Adoption of digital proxies or digital duplicates faces hurdles due to lack of semantic interoperability between architectures, standards and ontologies. Technologies necessary for automated discovery are in short supply. Progression of the field depends on convergence of information technology, operational technology and protocol-agnostic telecommunications. Making sense of the data, ability to curate data, and perform data analytics, at the edge (or mist, rather than in the fog or cloud) is key to value. Delivering algorithm engines to the edge, are crucial for edge analytics, when latency is critical. The confluence of these, and other factors, may chart the future path for Digital Twins. The number of unknown unknowns, and the known unknowns, in this process, makes it imperative to create global infrastructures and organize groups, to pursue the development of fundamental building blocks, and new ideas through research.
A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking

This paper introduces a novel deep learning framework including a lexicon-based approach for sentencelevel prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory network (BiLSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking. Twitter; Sentiment classification, Opinion mining.
Knowledge transfer frameworks: an extension incorporating knowledge repositories and knowledge administration

While theories abound concerning knowledge transfer in organisations, little empirical work has been undertaken to assess any possible relationship between repositories of knowledge and those responsible for the use of knowledge. This paper develops a knowledge transfer framework based on an empirical analysis of part of the UK operation of a Fortune 100 corporation, which extends existing knowledge transfer theory. The proposed framework integrates knowledge storage and knowledge administration within a model of effective knowledge transfer. This integrated framework encompasses five components: the actors engaged in the transfer of knowledge, the typology of organisational knowledge that is transferred between the actors, the mechanisms by which the knowledge transfer is carried out, the repositories where explicit knowledge is retained and the knowledge administrator equivalent whose function is to manage and maintain knowledge. The paper concludes that a ‘hybridisation’ of knowledge transfer approach, revealed by the framework, offers some promise in organisational applications.
Weaving the web: the original design and ultimate destiny of the world wide web by its inventor [Book Review]

Book Publisher: New York: Harper Collins Publishers, 1999, 226 pp., including index. Tim Berners-Lee’s claim to be the inventor of the World Wide Web has not been disputed. While a consulting physicist at CERN Laboratory in Geneva, Switzerland, Berners-Lee developed three specifications central to the Web: the URL scheme for giving every “page” a standard address; the HTTP protocol for accessing those pages; and the HTML language for formatting hypertext documents. By 1990, the first Web server was up and running on his desktop computer at CERN.
A dirty dozen: twelve p-value misconceptions.

The P value is a measure of statistical evidence that appears in virtually all medical research papers. Its interpretation is made extraordinarily difficult because it is not part of any formal system of statistical inference. As a result, the P value's inferential meaning is widely and often wildly misconstrued, a fact that has been pointed out in innumerable papers and books appearing since at least the 1940s. This commentary reviews a dozen of these common misinterpretations and explains why each is wrong. It also reviews the possible consequences of these improper understandings or representations of its meaning. Finally, it contrasts the P value with its Bayesian counterpart, the Bayes' factor, which has virtually all of the desirable properties of an evidential measure that the P value lacks, most notably interpretability. The most serious consequence of this array of P-value misconceptions is the false belief that the probability of a conclusion being in error can be calculated from the data in a single experiment without reference to external evidence or the plausibility of the underlying mechanism.
#TagSpace: Semantic Embeddings from Hashtags

We describe a convolutional neural network that learns feature representations for short textual posts using hashtags as a supervised signal. The proposed approach is trained on up to 5.5 billion words predicting 100,000 possible hashtags. As well as strong performance on the hashtag prediction task itself, we show that its learned representation of text (ignoring the hashtag labels) is useful for other tasks as well. To that end, we present results on a document recommendation task, where it also outperforms a number of baselines.
What Do We Know About Creativity ?

Creativity has been defined in many different ways by different authors. This article explores these different definitions of creativity; the relationship between creativity and intelligence, and those factors which affect creativity, such as convergent and divergent thinking. In addition, the article explores the importance of computer technology for testing ideas and the importance of reflective thinking and the evaluation of thoughts. It concludes with a synthesis of the basic attributes of highly creative students and present some ideas of what scholars have said about strategies we can use to enhance creativity in students. Although originality and creative imagination are private, guidance and training can substantially increase the learner’s output.
Scaling Semantic Parsers with On-the-Fly Ontology Matching

We consider the challenge of learning semantic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety of topics and include many phrases whose meaning is difficult to represent in a fixed target ontology. For example, even simple phrases such as ‘daughter’ and ‘number of people living in’ cannot be directly represented in Freebase, whose ontology instead encodes facts about gender, parenthood, and population. In this paper, we introduce a new semantic parsing approach that learns to resolve such ontological mismatches. The parser is learned from question-answer pairs, uses a probabilistic CCG to build linguistically motivated logicalform meaning representations, and includes an ontology matching model that adapts the output logical forms for each target ontology. Experiments demonstrate state-of-the-art performance on two benchmark semantic parsing datasets, including a nine point accuracy improvement on a recent Freebase QA corpus.
The effect of author set size and data size in authorship attribution

s10579-009-9111-2], for instance in social networks, will likely involve large sets of candidate authors and only limited data per author. In this article, we present the results of a systematic study of two important parameters in supervised machine learning that significantly affect performance in computational authorship attribution: (1) the number of candidate authors (i.e. the number of classes to be learned), and (2) the amount of training data available per candidate author (i.e. the size of the training data). We also investigate the robust-ness of different types of lexical and linguistic features to the effects of author set size and data size. The approach we take is an operationalization of the standard text categorization model, using memory-based learning for discriminating between the candidate authors. We performed authorship attribution experiments on a set of three benchmark corpora in which the influence of topic could be controlled. The short text fragments of e-mail length present the approach with a true challenge. Results show that, as expected, authorship attribution accuracy deteriorates as the number of candidate authors increases and size of training data decreases, although the machine learning approach continues performing significantly above chance. Some feature types (most notably character n-grams) are robust to changes in author set size and data size, but no robust individual features emerge.
Market Size , Trade , and Productivity ∗

We develop a monopolistically competitive model of trade with Þrm heterogeneity in terms of productivity differences and endogenous differences in the toughness of competition across markets in terms of the number and average productivity of competing Þrms. We show that countries with higher market potential attract a larger number of sellers, and hence more product variety. These sellers, on average, are more productive, are larger, and set lower prices than Þrms selling in smaller markets. Thus, consumers in bigger markets beneÞt both from lower prices and more product variety. We also describe the effect of market size on the variance of these Þrm performance measures. We then study the effects of reciprocal, unilateral, and preferential trade liberalization.
A research paper recommender system using a Dynamic Normalized Tree of Concepts model for user modelling

The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time.
ON THE USE OF THE MASS MEDIA FOR IMPORTANT THINGS *

The mass media are ranked with respect to their perceived helpfulness in satisfying clusters of needs arising from social roles and individual dispositions. For example, integration into the sociopolitical order is best served by newspaper; while "knowing oneself " is best served by books. Cinema and books are more helpful as means of "escape" than is television. Primary relations, holidays and other cultural activities are often more important than the mass media in satisfying needs. Television is the least specialized medium, serving many different personal and political needs. The "interchangeability" of the media over a variety of functions orders televisions, radio, newspapers, books, and cinema in a circumplex. We speculate about which attributes of the media explain the social and psychological needs they serve best. The data, drawn from an Israeli survey, are presented as a basis for cross-cultural comparison. Disciplines Communication | Social and Behavioral Sciences This journal article is available at ScholarlyCommons: http://repository.upenn.edu/asc_papers/267 ON THE USE OF THE MASS MEDIA FOR IMPORTANT THINGS * ELIHU KATZ MICHAEL GUREVITCH
Accurate and efficient map matching for challenging environments

We present the SnapNet, a system that provides accurate real-time map matching for cellular-based trajectories. Such coarse-grained trajectories introduce new challenges to map matching including (1) input locations that are far from the actual road segment (errors in the orders of kilometers), (2) back-and-forth transitions, and (3) highly sparse input data. SnapNet addresses these challenges by applying extensive preprocessing steps to remove the noisy locations and to handle the data sparseness. At the core of SnapNet is a novel incremental HMM algorithm that combines digital map hints and a number of heuristics to reduce the noise and provide real-time estimation. Evaluation of SnapNet in different cities covering more than 100km distance shows that it can achieve more than 90% accuracy under noisy coarse-grained input location estimates. This maps to over 97% and 34% enhancement in precision and recall respectively when compared to traditional HMM map matching algorithms. Moreover, SnapNet has a low latency of 1.2ms per location estimate.
Teaching Reinforcement Learning with Mario: An Argument and Case Study

Integrating games into the computer science curriculum has been gaining acceptance in recent years, particularly when used to improve student engagement in introductory courses. This paper argues that games can also be useful in upper level courses, such as general artificial intelligence and machine learning. We provide a case study of using a Mario game in a machine learning class to provide one successful data point where both content-specific and general learning outcomes were successfully achieved.