nils-herrmann/clean_scirepeval huggingface dataset:
Semi-supervised sequence tagging with bidirectional language models

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pretrained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.
An Improved Model of Semantic Similarity Based on Lexical Co-Occurrence

The lexical semantic system is an important component of human language and cognitive processing. One approach to modeling semantic knowledge makes use of hand-constructed networks or trees of interconnected word senses (Miller, Beckwith, Fellbaum, Gross, & Miller, 1990; Jarmasz & Szpakowicz, 2003). An alternative approach seeks to model word meanings as high-dimensional vectors, which are derived from the cooccurrence of words in unlabeled text corpora (Landauer & Dumais, 1997; Burgess & Lund, 1997a). This paper introduces a new vector-space method for deriving word-meanings from large corpora that was inspired by the HAL and LSA models, but which achieves better and more consistent results in predicting human similarity judgments. We explain the new model, known as COALS, and how it relates to prior methods, and then evaluate the various models on a range of tasks, including a novel set of semantic similarity ratings involving both semantically and morphologically related terms.
Natural Language Processing with Python Steven Bird, Ewan Klein, and Edward Loper (University of Melbourne, University of Edinburgh, and BBN Technologies) Sebastopol, CA: O'Reilly Media, 2009, xx+482 pp; paperbound, ISBN 978-0-596-51649-9, $44.99; on-line free of charge at nltk.org/book

This book comes with “batteries included” (a reference to the phrase often used to explain the popularity of the Python programming language). It is the companion book to an impressive open-source software library called the Natural Language Toolkit (NLTK), written in Python. NLTK combines language processing tools (tokenizers, stemmers, taggers, syntactic parsers, semantic analyzers) and standard data sets (corpora and tools to access the corpora in an efficient and uniform manner). Although the book builds on the NLTK library, it covers only a relatively small part of what can be done with it. The combination of the book with NLTK, a growing system of carefully designed, maintained, and documented code libraries, is an extraordinary resource that will dramatically influence the way computational linguistics is taught. The book attempts to cater to a large audience: It is a textbook on computational linguistics for science and engineering students; it also serves as practical documentation for the NLTK library, and it finally attempts to provide an introduction to programming and algorithm design for humanities students. I have used the book and its earlier on-line versions to teach advanced undergraduate and graduate students in computer science in the past eight years. The book adopts the following approach:
Airport detection from remote sensing images using transferable convolutional neural networks

This paper presents a method for airport detection from optical satellite images using deep convolutional neural networks (CNN). To achieve fast detection with high accuracy, region proposal by searching adjacent parallel line segments has been applied to select candidate fields with potential runways. These proposals were further classified by a CNN model transfer learned from AlexNet to identify the final airport regions from other confusing classes. The proposed method has been tested on a remote sensing dataset consisting of 120 airports. Experiments showed that the proposed method could recognize airports from a large complex area in seconds with an accuracy of 84.1%.
Herbal medicine for sports: a review

The use of herbal medicinal products and supplements has increased during last decades. At present, some herbs are used to enhance muscle strength and body mass. Emergent evidence suggests that the health benefits from plants are attributed to their bioactive compounds such as Polyphenols, Terpenoids, and Alkaloids which have several physiological effects on the human body. At times, manufacturers launch numerous products with banned ingredient inside with inappropriate amounts or fake supplement inducing harmful side effect. Unfortunately up to date, there is no guarantee that herbal supplements are safe for anyone to use and it has not helped to clear the confusion surrounding the herbal use in sport field especially. Hence, the purpose of this review is to provide guidance on the efficacy and side effect of most used plants in sport. We have identified plants according to the following categories: Ginseng, alkaloids, and other purported herbal ergogenics such as Tribulus Terrestris, Cordyceps Sinensis. We found that most herbal supplement effects are likely due to activation of the central nervous system via stimulation of catecholamines. Ginseng was used as an endurance performance enhancer, while alkaloids supplementation resulted in improvements in sprint and cycling intense exercises. Despite it is prohibited, small amount of ephedrine was usually used in combination with caffeine to enhance muscle strength in trained individuals. Some other alkaloids such as green tea extracts have been used to improve body mass and composition in athletes. Other herb (i.e. Rhodiola, Astragalus) help relieve muscle and joint pain, but results about their effects on exercise performance are missing.
Developing human nature: "development to" versus "development from?".

Evolutionary psychology (EP) emphasizes that socio-cultural experiences are constrained by the characteristics of the individual. Therefore, cultural experience acts on a nervous system biased to respond to in particular ways (i.e., human nature). For EP, evolutionary lineage and adaptive significance explain the development of such human nature (a "development to" approach). Gottlieb, and others in Developmental psychobiology (DPB), have explained the development of "instinctive" behavior patterns of a wide variety of animals using a "development from" approach. The DPB approach does not contradict a human nature that reflects an evolutionary heritage with adaptive value. We present three examples that demonstrate how a DPB approach to development accounts both for the expression of patterns specific to the individual as well as patterns that are species-typical (human nature, for humans) without shifting explanatory constructs and frameworks.
Model-based engineering for microservice architectures using Enterprise Integration Patterns for inter-service communication

Microservices need to communicate via inter-service communication mechanism. This leads to service integration that requires the aspect of messaging and event handling to be considered during the specification of microservices. Therefore, the application of Enterprise Integration Patterns (EIP) in the context of the design of microservice architectures seems to be a promising approach for microservice architecture engineering. This paper presents a model-based approach for microservice architectural design and service integration through formal models using the UML and UML profiles. A domain model (bounded contexts) is used as a starting point. UML component diagrams are extended so that microservices can be modeled. Enterprise integration patterns are applied to the microservice diagrams providing a precise microservice specification that can be used for further simulations, transformations, or code generation tasks.
Crossmodal interactions: lessons from synesthesia.

Synesthesia is a condition in which stimulation in one modality also gives rise to a perceptual experience in a second modality. In two recent studies we found that the condition is more common than previously reported; up to 5% of the population may experience at least one type of synesthesia. Although the condition has been traditionally viewed as an anomaly (e.g., breakdown in modularity), it seems that at least some of the mechanisms underlying synesthesia do reflect universal crossmodal mechanisms. We review here a number of examples of crossmodal correspondences found in both synesthetes and nonsynesthetes including pitch-lightness and vision-touch interaction, as well as cross-domain spatial-numeric interactions. Additionally, we discuss the common role of spatial attention in binding shape and color surface features (whether ordinary or synesthetic color). Consistently with behavioral and neuroimaging data showing that chromatic-graphemic (colored-letter) synesthesia is a genuine perceptual phenomenon implicating extrastriate cortex, we also present electrophysiological data showing modulation of visual evoked potentials by synesthetic color congruency.
A comparative study on temporal database models: A survey

Time is implicit in most of the database management applications. To handle time varying data for every application using ad hoc methods is a cumbersome task. Temporal database provides an efficient solution, provides a framework for such a problem. This paper briefly introduces temporal databases and surveys the work done in the field of temporal database models. First the paper introduces the conventional database which we use today and how temporal database differs from it. We examine different time domains employed to implement such database and classification of temporal database. Finally, the paper discusses various proposed and implemented temporal models and future work in the field of temporal database.
The neural basis of loss aversion in decision-making under risk.

People typically exhibit greater sensitivity to losses than to equivalent gains when making decisions. We investigated neural correlates of loss aversion while individuals decided whether to accept or reject gambles that offered a 50/50 chance of gaining or losing money. A broad set of areas (including midbrain dopaminergic regions and their targets) showed increasing activity as potential gains increased. Potential losses were represented by decreasing activity in several of these same gain-sensitive areas. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions, including the ventral striatum and prefrontal cortex.
AIAA 2003-1978 Deployable Tensegrity

The tensegrity concept has long been considered as a basis for lightweight and compact packaging deployable structures, but very few studies are available. This paper presents a complete design study of a deployable tensegrity mast with all the steps involved: initial formfinding, structural analysis, manufacturing and deployment. Closed-form solutions are used for the formfinding. A manufacturing procedure in which the cables forming the outer envelope of the mast are constructed by two-dimensional weaving is used. The deployment of the mast is achieved through the use of self-locking hinges. A stiffness comparison between the tensegrity mast and an articulated truss mast shows that the tensegrity mast is weak in bending.
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations

A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviours expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision, this work is exploring two complementary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and non-linear differential equations, to extract patterns from high-dimensional data generated from experiments, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations. The latter is aligned in spirit with the emerging field of probabilistic numerics.
A deep-learning-based approach for automated wagon component inspection

Inspecting objects in the industry aims to guarantee product quality allowing problems to be corrected and damaged products to be discarded. Inspection is also widely used in railway maintenance, where wagon components need to be checked due to efficiency and safety concerns. In some organizations, hundreds of wagons are inspected visually by a human inspector, which leads to quality issues and safety risks for the inspectors. This paper describes a wagon component inspection approach using Deep Learning techniques to detect a particular damaged component: the shear pad. We compared our approach for convolutional neural networks with the state of art classification methods to distinguish among three shear pads conditions: absent, damaged, and undamaged shear pad. Our results are very encouraging showing empirical evidence that our approach has better performance than other classification techniques.
Analyzing facebook privacy settings: user expectations vs. reality

The sharing of personal data has emerged as a popular activity over online social networking sites like Facebook. As a result, the issue of online social network privacy has received significant attention in both the research literature and the mainstream media. Our overarching goal is to improve defaults and provide better tools for managing privacy, but we are limited by the fact that the full extent of the privacy problem remains unknown; there is little quantification of the incidence of incorrect privacy settings or the difficulty users face when managing their privacy.
 In this paper, we focus on measuring the disparity between the desired and actual privacy settings, quantifying the magnitude of the problem of managing privacy. We deploy a survey, implemented as a Facebook application, to 200 Facebook users recruited via Amazon Mechanical Turk. We find that 36% of content remains shared with the default privacy settings. We also find that, overall, privacy settings match users' expectations only 37% of the time, and when incorrect, almost always expose content to more users than expected. Finally, we explore how our results have potential to assist users in selecting appropriate privacy settings by examining the user-created friend lists. We find that these have significant correlation with the social network, suggesting that information from the social network may be helpful in implementing new tools for managing privacy.
Ambiguity representation and resolution in spoken dialogue systems

Spoken natural language often contains ambiguities that must be addressed by a spoken dialogue system. In this work, we present the internal semantic representation and resolution strategy of a dialogue system designed to understand ambiguous input. These mechanisms are domain independent; almost all task-specific knowledge is represented in parameterizable data structures. The system derives candidate descriptions of what the user said from raw input data, context-tracking and scoring. These candidates are chosen on the basis of a pragmatic analysis of responses elicited by an extensive implicit and explicit confirmation dialogue strategy, combined with specific error correction capabilities available to the user. This new ambiguity resolution strategy greatly improves dialogue interaction, eliminating about half of the errors in dialogues from a travel reservation task.
Image Classification Using Convolutional Neural Networks

Deep Learning has emerged as a new area in machine learning and is applied to a number of signal and image applications.The main purpose of the work presented in this paper, is to apply the concept of a Deep Learning algorithm namely, Convolutional neural networks (CNN) in image classification. The algorithm is tested on various standard datasets, like remote sensing data of aerial images (UC Merced Land Use Dataset) and scene images from SUN database. The performance of the algorithm is evaluated based on the quality metric known as Mean Squared Error (MSE) and classification accuracy. The graphical representation of the experimental results is given on the basis of MSE against the number of training epochs. The experimental result analysis based on the quality metrics and the graphical representation proves that the algorithm (CNN) gives fairly good classification accuracy for all the tested datasets.
Surgical manual of the Korean Gynecologic Oncology Group: classification of hysterectomy and lymphadenectomy

The Surgery Treatment Modality Committee of the Korean Gynecologic Oncologic Group (KGOG) has determined to develop a surgical manual to facilitate clinical trials and to improve communication between investigators by standardizing and precisely describing operating procedures. The literature on anatomic terminology, identification of surgical components, and surgical techniques were reviewed and discussed in depth to develop a surgical manual for gynecologic oncology. The surgical procedures provided here represent the minimum requirements for participating in a clinical trial. These procedures should be described in the operation record form, and the pathologic findings obtained from the procedures should be recorded in the pathologic report form. Here, we focused on radical hysterectomy and lymphadenectomy, and we developed a KGOG classification for those conditions.
Mapping spatial frames of reference onto time: A review of theoretical accounts and empirical findings

When speaking and reasoning about time, people around the world tend to do so with vocabulary and concepts borrowed from the domain of space. This raises the question of whether the cross-linguistic variability found for spatial representations, and the principles on which these are based, may also carry over to the domain of time. Real progress in addressing this question presupposes a taxonomy for the possible conceptualizations in one domain and its consistent and comprehensive mapping onto the other-a challenge that has been taken up only recently and is far from reaching consensus. This article aims at systematizing the theoretical and empirical advances in this field, with a focus on accounts that deal with frames of reference (FoRs). It reviews eight such accounts by identifying their conceptual ingredients and principles for space-time mapping, and it explores the potential for their integration. To evaluate their feasibility, data from some thirty empirical studies, conducted with speakers of sixteen different languages, are then scrutinized. This includes a critical assessment of the methods employed, a summary of the findings for each language group, and a (re-)analysis of the data in view of the theoretical questions. The discussion relates these findings to research on the mental time line, and explores the psychological reality of temporal FoRs, the degree of cross-domain consistency in FoR adoption, the role of deixis, and the sources and extent of space-time mapping more generally.
Learning Bayesian Networks with the bnlearn Package

bnlearn is an R package (R Team 2009) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package (Gentry et al. 2009).
Clinical Decision Support Systems for Heart Disease Using Data Mining Approach

Now a day’s business is growing at a very rapid pace and a lot of information is generated. The more information we have, based on internal experiences or from external sources, the better our decisions would be. Business executives are faced with the same dilemmas when they make decisions. They need the best tools available to help them. Decision support system helps the managers to take better and quick decision by using historical and current data. By combining massive amounts of data with sophisticated analytical models and tools, and by making the system easy to use, they provide a much better source of information to use in the decision-making process. Health care is also one of the domains which get a lot of benefits and researches with the advent and progress in data mining. Data mining in medicine can resolve this problem and can provide promising results. It plays a vital role in extracting useful knowledge and making scientific decision for diagnosis and treatment of disease. Treatment records of millions of patients have been recorded and many tools and algorithms are applied to understand and analyze the data. Heart failure is a common disease which is difficult to diagnose. To aid physicians in diagnosing heart failure, a decision support system has been proposed. A classification based methods in health care is used to diagnose based on certain parameters to diagnosis if the patient have certain disease or not. The purpose is to explore the aspects of Clinical Decision Support Systems and to figure out the most optimal methodology that can be used in Clinical Decision Support Systems to provide the best solutions and diagnosis to medical problems.
Millimeter-Wave Shaped-Beam Substrate Integrated Conformal Array Antenna

A millimeter-wave shaped-beam substrate integrated conformal array antenna is demonstrated in this paper. After discussing the influence of conformal shape on the characteristics of a substrate integrated waveguide (SIW) and a radiating slot, an array mounted on a cylindrical surface with a radius of 20 mm, i.e., 2.3 λ, is synthesized at the center frequency of 35 GHz. All components, including a 1-to-8 divider, a phase compensated network and an 8 × 8 slot array are fabricated in a single dielectric substrate together. In measurement, it has a - 27.4 dB sidelobe level (SLL) beam in H-plane and a flat-topped fan beam with -38° ~ 37° 3 dB beamwidth in E-plane at the center frequency of 35 GHz. The cross polarization is lower than -41.7 dB at the beam direction. Experimental results agree well with simulations, thus validating our design. This SIW scheme is able to solve the difficulty of integration between conformal array elements and a feed network in millimeter-wave frequency band, while avoid radiation leakage and element-to-element parasitic cross-coupling from the feed network.