nils-herrmann/clean_scirepeval huggingface dataset:
Semantic metadata interoperability in digital libraries: a constructivist grounded theory approach

This paper highlights problems of semantic metadata interoperability in digital libraries. The prevalence of a plethora of standards and a lack of semantic interoperability can partly be attributed to the absence of theoretical foundations to underpin current metadata approaches and solutions. Contemporary metadata standards and interoperability approaches are mainly top-down and hierarchical, and, hence, fail to take into account the diversity of cultural, linguistic and local perspectives that abound. To overcome this, it is proposed that a social constructivist approach should be adopted by libraries and other cultural heritage institutions when archiving information objects that need to be enriched with metadata, thereby reflecting the diversity of views and perspectives that can be held by their users. Following on Charmaz , a constructivist grounded theory method is employed to investigate how library professionals and library users view metadata standards, collaborative metadata approaches and semantic web technologies in relation to semantic metadata interoperability. This method allows an active interplay between the researcher and the participants who can be either Library and Information Science researchers, librarians or library users. Following the completion first phase of data collection, preliminary reflections are presented, with emphasis on how Library and Information Science professionals view current metadata practices, especially as used in academic library contexts. However, as the study is ongoing one, it is too early to generate theoretical categories and conclusions.
A Variational U-Net for Conditional Appearance and Shape Generation

Deep generative models have demonstrated great performance in image synthesis. However, results deteriorate in case of spatial deformations, since they generate images of objects directly, rather than modeling the intricate interplay of their inherent shape and appearance. We present a conditional U-Net  for shape-guided image generation, conditioned on the output of a variational autoencoder for appearance. The approach is trained end-to-end on images, without requiring samples of the same object with varying pose or appearance. Experiments show that the model enables conditional image generation and transfer. Therefore, either shape or appearance can be retained from a query image, while freely altering the other. Moreover, appearance can be sampled due to its stochastic latent representation, while preserving shape. In quantitative and qualitative experiments on COCO [20], DeepFashion [21, 23], shoes [43], Market-1501 [47] and handbags  the approach demonstrates significant improvements over the state-of-the-art.
Active learning for deep semantic parsing

P is computed by a Seq2Seq model with attention, requires utterance x but not logical form y. ● Active learning score = linear combination of features using weights from binary classifier. ○ Predict if Forward S2S selects utterances. ○ Trained on ATIS dev corpus. ● Binary classifier to predict Forward S2S using ○ RNN LF language model ○ Backward S2S model ● Margins between the best and second best hypotheses ● Source token frequency ● Utterance log loss ● Encoder and decoder last hidden states Backward Classifier
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attentions from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate the content deliveries and improve the quality of mobile services. In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication. And thus, we design the “In-Edge AI” framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. “In-Edge AI” is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to the mobile communication systems. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of “In-Edge AI”.
What memory is for.

Let's start from scratch in thinking about what memory is for, and consequently, how it works. Suppose that memory and conceptualization work in the service of perception and action. In this case, conceptualization is the encoding of patterns of possible physical interaction with a three-dimensional world. These patterns are constrained by the structure of the environment, the structure of our bodies, and memory. Thus, how we perceive and conceive of the environment is determined by the types of bodies we have. Such a memory would not have associations. Instead, how concepts become related (and what it means to be related) is determined by how separate patterns of actions can be combined given the constraints of our bodies. I call this combination "mesh." To avoid hallucination, conceptualization would normally be driven by the environment, and patterns of action from memory would play a supporting, but automatic, role. A significant human skill is learning to suppress the overriding contribution of the environment to conceptualization, thereby allowing memory to guide conceptualization. The effort used in suppressing input from the environment pays off by allowing prediction, recollective memory, and language comprehension. I review theoretical work in cognitive science and empirical work in memory and language comprehension that suggest that it may be possible to investigate connections between topics as disparate as infantile amnesia and mental-model theory.
Minnorm training: an algorithm for training over-parameterized deep neural networks

In this work, we propose a new training method for finding minimum weight norm solutions in over-parameterized neural networks (NNs). This method seeks to improve training speed and generalization performance by framing NN training as a constrained optimization problem wherein the sum of the norm of the weights in each layer of the network is minimized, under the constraint of exactly fitting training data. It draws inspiration from support vector machines (SVMs), which are able to generalize well, despite often having an infinite number of free parameters in their primal form, and from recent theoretical generalization bounds on NNs which suggest that lower norm solutions generalize better. To solve this constrained optimization problem, our method employs Lagrange multipliers that act as integrators of error over training and identify ‘support vector’-like examples. The method can be implemented as a wrapper around gradient based methods and uses standard back-propagation of gradients from the NN for both regression and classification versions of the algorithm. We provide theoretical justifications for the effectiveness of this algorithm in comparison to early stopping and L2regularization using simple, analytically tractable settings. In particular, we show faster convergence to the max-margin hyperplane in a shallow network (compared to vanilla gradient descent); faster convergence to the minimum-norm solution in a linear chain (compared to L2-regularization); and initialization-independent generalization performance in a deep linear network. Finally, using the MNIST dataset, we demonstrate that this algorithm can boost test accuracy and identify difficult examples in real-world datasets.
Design and Implementation of a Path Finding Robot Using Flood Fill Algorithm

Autonomous robot is a robot that can perform certain work independently without the human help. Autonomous of navigation is one of the capabilities of autonomous robot to move from one point to another. Implementation of Autonomous robot navigation to explore an unknown environment, requires the robot to explore and map the environment and seek the path to reach a certain point. Path Finding Robot is a mobile robot which moves using wheels with differential steering type. This path finding robot is designed to solve a maze environment that has a size of 5 x 5 cells and it is based on the flood-fill algorithm. Detection of walls and opening in the maze were done using ultrasonic range-finders. The robot was able to learn the maze, find all possible routes and solve it using the shortest one. This robot also use wall follower algorithms to correct the position of the robot against the side wall maze, so the robot can move straight. After several experiments, the robot can explore and map of maze and find the shortest path to destination point with a success rate of 70%.
A coherent framework for fingerprint analysis: are fingerprints Holograms?

We propose a coherent mathematical model for human fingerprint images. Fingerprint structure is represented simply as a hologram - namely a phase modulated fringe pattern. The holographic form unifies analysis, classification, matching, compression, and synthesis of fingerprints in a self-consistent formalism. Hologram phase is at the heart of the method; a phase that uniquely decomposes into two parts via the Helmholtz decomposition theorem. Phase also circumvents the infinite frequency singularities that always occur at minutiae. Reliable analysis is possible using a recently discovered two-dimensional demodulator. The parsimony of this model is demonstrated by the reconstruction of a fingerprint image with an extreme compression factor of 239.
Cognitive linguistics.

Cognitive linguistics is one of the fastest growing and influential perspectives on the nature of language, the mind, and their relationship with sociophysical (embodied) experience. It is a broad theoretical and methodological enterprise, rather than a single, closely articulated theory. Its primary commitments are outlined. These are the Cognitive Commitment-a commitment to providing a characterization of language that accords with what is known about the mind and brain from other disciplines-and the Generalization Commitment-which represents a dedication to characterizing general principles that apply to all aspects of human language. The article also outlines the assumptions and worldview which arises from these commitments, as represented in the work of leading cognitive linguists.  further resources related to this article, please visit the WIREs website.
Comparison of strategies for assessing nutritional adequacy in elite female athletes' dietary intake.

This study aimed to compare strategies for assessing nutritional adequacy in the dietary intake of elite female athletes.



Dietary intake was assessed using an adapted food-frequency questionnaire in 72 elite female athletes from a variety of sports. Nutritional adequacy was evaluated and compared using mean intake; the proportion of participants with intakes below Australian nutrient reference values (NRV), U.S. military dietary reference intakes (MDRI), and current sports nutrition recommendations; and probability estimates of nutrient inadequacy.



Mean energy intake was 10,551 +/- 3,836 kJ/day with macronutrient distribution 18% protein, 31% fat, and 46% carbohydrate, consistent with Australian acceptable macronutrient distribution ranges. Mean protein intake (1.6 g . kg(-1) . d(-1)) was consistent with (>1.2 g . kg(-1) . d(-1)), and carbohydrate intake (4.5 g . kg(-1) . d(-1)), below, current sports nutrition recommendations (>5 g . kg(-1) . d(-1)), with 30% and 65% of individuals not meeting these levels, respectively. Mean micronutrient intake met the relevant NRV and MDRI except for vitamin D and folate. A proportion of participants failed to meet the estimated average requirement for folate (48%), calcium (24%), magnesium (19%), and iron (4%). Probability estimates of inadequacy identified intake of folate (44%), calcium (22%), iron (19%), and magnesium (15%) as inadequate.



Interpretation of dietary adequacy is complex and varies depending on whether the mean, proportion of participants below the relevant NRV, or statistical probability estimate of inadequacy is used. Further research on methods to determine dietary adequacy in athlete populations is required.
Exercise-induced muscle damage in humans.

Exercise-induced muscle injury in humans frequently occurs after unaccustomed exercise, particularly if the exercise involves a large amount of eccentric (muscle lengthening) contractions. Direct measures of exercise-induced muscle damage include cellular and subcellular disturbances, particularly Z-line streaming. Several indirectly assessed markers of muscle damage after exercise include increases in T2 signal intensity via magnetic resonance imaging techniques, prolonged decreases in force production measured during both voluntary and electrically stimulated contractions (particularly at low stimulation frequencies), increases in inflammatory markers both within the injured muscle and in the blood, increased appearance of muscle proteins in the blood, and muscular soreness. Although the exact mechanisms to explain these changes have not been delineated, the initial injury is ascribed to mechanical disruption of the fiber, and subsequent damage is linked to inflammatory processes and to changes in excitation-contraction coupling within the muscle. Performance of one bout of eccentric exercise induces an adaptation such that the muscle is less vulnerable to a subsequent bout of eccentric exercise. Although several theories have been proposed to explain this "repeated bout effect," including altered motor unit recruitment, an increase in sarcomeres in series, a blunted inflammatory response, and a reduction in stress-susceptible fibers, there is no general agreement as to its cause. In addition, there is controversy concerning the presence of sex differences in the response of muscle to damage-inducing exercise. In contrast to the animal literature, which clearly shows that females experience less damage than males, research using human studies suggests that there is either no difference between men and women or that women are more prone to exercise-induced muscle damage than are men.
Internet of things for smart agriculture: Technologies, practices and future direction

The advent of Internet of Things (IoT) has shown a new direction of innovative research in agricultural domain. Being at nascent stage, IoT needs to be widely experimented so as to get widely applied in various agricultural applications. In this paper, I review various potential IoT applications, and the specific issues and challenges associated with IoT deployment for improved farming. To focus on the specific requirements the devices, and wireless communication technologies associated with IoT in agricultural and farming applications are analyzed comprehensively. Investigations are made on those sensor enabled IoT systems that provide intelligent and smart services towards smart agriculture. Various case studies are presented to explore the existing IoT based solutions performed by various organizations and individuals and categories according to their deployment parameters. Related difficulties in these solutions, while identifying the factors for improvement and future road map of work using the IoT are also highlighted.
Abaqus Implementation of Extended Finite Element Method Using a Level Set Representation for Three-Dimensional Fatigue Crack Growth and Life Predictions

A three-dimensional extended finite element method (X-FEM) coupled with a narrow band fast marching method (FMM) is developed and implemented in the Abaqus finite element package for curvilinear fatigue crack growth and life prediction analysis of metallic structures. Given the level set representation of arbitrary crack geometry, the narrow band FMM provides an efficient way to update the level set values of its evolving crack front. In order to capture the plasticity induced crack closure effect, an element partition and state recovery algorithm for dynamically allocated Gauss points is adopted for efficient integration of historical state variables in the near-tip plastic zone. An element-based penalty approach is also developed to model crack closure and friction. The proposed technique allows arbitrary insertion of initial cracks, independent of a base 3D model, and allows non-self-similar crack growth pattern without conforming to the existing mesh or local remeshing. Several validation examples are presented to demonstrate the extraction of accurate stress intensity factors for both static and growing cracks. Fatigue life prediction of a flawed helicopter lift frame under the ASTERIX spectrum load is presented to demonstrate the analysis procedure and capabilities of the method.
An Introduction to Security API Analysis

A security API is an Application Program Interface that allows untrusted code to access sensitive resources in a secure way. Examples of security APIs include the interface between the tamper-resistant chip on a smartcard (trusted) and the card reader (untrusted), the interface between a cryptographic Hardware Security Module, or HSM (trusted) and the client machine (untrusted), and the Google maps API (an interface between a server, trusted by Google, and the rest of the Internet). The crucial aspect of a security API is that it is designed to enforce a policy, i.e. no matter what sequence of commands in the interface are called, and no matter what the parameters, certain security properties should continue to hold. This means that if the less trusted code turns out to be malicious (or just faulty), the carefully designed API should prevent compromise of critical data. Designing such an interface is extremely tricky even for experts. A number of security flaws have been found in APIs in use in deployed systems in the last decade. In this tutorial paper, we introduce the subject of security API analysis using formal techniques. This approach has recently proved highly successful both in finding new flaws and verifying security properties of improved designs. We will introduce the main techniques, many of which have been adapted from language-based security and security protocol verification, by means of two case studies: cryptographic key management, and Personal Identification Number (PIN) processing in the cash machine network. We will give plenty of examples of API attacks, and highlight the areas where more research is needed.
AllenNLP: A Deep Semantic Natural Language Processing Platform

Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research, which addresses these issues with easyto-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field.
Evolutionary computing in recommender systems: a review of recent research

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition

Rapidly expanding volume of publications in the biomedical domain makes it increasingly difficult for a timely evaluation of the latest literature. That, along with a push for automated evaluation of clinical reports, present opportunities for effective natural language processing methods. In this study we target the problem of named entity recognition, where texts are processed to annotate terms that are relevant for biomedical studies. Terms of interest in the domain include gene and protein names, and cell lines and types. Here we report on a pipeline built on Embeddings from Language Models (ELMo) and a deep learning package for natural language processing (AllenNLP). We trained context-aware token embeddings on a dataset of biomedical papers using ELMo, and incorporated these embeddings in the LSTM-CRF model used by AllenNLP for named entity recognition. We show these representations improve named entity recognition for different types of biomedical named entities. We also achieve a new state of the art in gene mention detection on the BioCreative II gene mention shared task.
Metarepresentation in Linguistic Communication *

This paper is designed to illustrate and consider the relations between three types of metarepresentational ability used in verbal comprehension: the ability to metarepresent attributed thoughts, the ability to metarepresent attributed utterances, and the ability to metarepresent abstract, non-attributed representations (e.g. sentence types, utterance types, propositions). Aspects of these abilities have been separ at ly considered in the literatures on “theory of mind”, Gricean pragmatics and quotation. The aim of this paper is to show how the results of these separate strands of research might be integrated with an empirically plausible pragmatic theory.
Image Style Classification Based on Learnt Deep Correlation Features

This paper presents a comprehensive study of deep correlation features on image style classification. Inspired by that, correlation between feature maps can effectively describe image texture, and we design various correlations and transform them into style vectors, and investigate classification performance brought by different variants. In addition to intralayer correlation, interlayer correlation is proposed as well, and its effectiveness is verified. After showing the effectiveness of deep correlation features, we further propose a learning framework to automatically learn correlations between feature maps. Through extensive experiments on image style classification and artist classification, we demonstrate that the proposed learnt deep correlation features outperform several variants of convolutional neural network features by a large margin, and achieve the state-of-the-art performance.