nils-herrmann/clean_scirepeval huggingface dataset:
Environmental Sound Classification Based on Multi-temporal Resolution CNN Network Combining with Multi-level Features

Motivated by the fact that characteristics of different sound classes are highly diverse in different temporal scales and hierarchical levels, a novel deep convolutional neural network (CNN) architecture is proposed for the environmental sound classification task. This network architecture takes raw waveforms as input, and a set of separated parallel CNNs are utilized with different convolutional filter sizes and strides, in order to learn feature representations with multi-temporal resolutions. On the other hand, the proposed architecture also aggregates hierarchical features from multi-level CNN layers for classification using direct connections between convolutional layers, which is beyond the typical single-level CNN features employed by the majority of previous studies. This network architecture also improves the flow of information and avoids vanishing gradient problem. The combination of multi-level features boosts the classification performance significantly. Comparative experiments are conducted on two datasets: the environmental sound classification dataset (ESC-50), and DCASE 2017 audio scene classification dataset. Results demonstrate that the proposed method is highly effective in the classification tasks by employing multi-temporal resolution and multi-level features, and it outperforms the previous methods which only account for single-level features.
How Is RAN Related to Reading Fluency? A Comprehensive Examination of the Prominent Theoretical Accounts

We examined the prominent theoretical explanations of the RAN-reading relationship in a relatively transparent language (Greek) in a sample of children (n = 286) followed from Grade 1 to Grade 2. Specifically, we tested the fit of eight different models, as defined by the type of reading performance predicted (oral vs. silent word reading fluency), the type of RAN tasks (non-alphanumeric vs. alphanumeric), and the RAN effects (direct vs. indirect). Working memory, attention, processing speed, and motor skills were used as "common cause" variables predicting both RAN and reading fluency and phonological awareness and orthographic processing were used as mediators of RAN's effects on reading fluency. The findings of both concurrent and longitudinal analyses indicated that RAN is a unique predictor of oral reading fluency, but not silent reading fluency. Using alphanumeric or non-alphanumeric RAN did not particularly affect the RAN-reading relationship. Both phonological awareness and orthographic processing partly mediated RAN's effects on reading fluency. Theoretical implications of these findings are discussed.
Physical and clinical performance of the mCT time-of-flight PET/CT scanner.

Time-of-flight (TOF) measurement capability promises to improve PET image quality. We characterized the physical and clinical PET performance of the first Biograph mCT TOF PET/CT scanner (Siemens Medical Solutions USA, Inc.) in comparison with its predecessor, the Biograph TruePoint TrueV. In particular, we defined the improvements with TOF. The physical performance was evaluated according to the National Electrical Manufacturers Association (NEMA) NU 2-2007 standard with additional measurements to specifically address the TOF capability. Patient data were analyzed to obtain the clinical performance of the scanner. As expected for the same size crystal detectors, a similar spatial resolution was measured on the mCT as on the TruePoint TrueV. The mCT demonstrated modestly higher sensitivity (increase by 19.7 ± 2.8%) and peak noise equivalent count rate (NECR) (increase by 15.5 ± 5.7%) with similar scatter fractions. The energy, time and spatial resolutions for a varying single count rate of up to 55 Mcps resulted in 11.5 ± 0.2% (FWHM), 527.5 ± 4.9 ps (FWHM) and 4.1 ± 0.0 mm (FWHM), respectively. With the addition of TOF, the mCT also produced substantially higher image contrast recovery and signal-to-noise ratios in a clinically-relevant phantom geometry. The benefits of TOF were clearly demonstrated in representative patient images.
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation

Recent neural models for data-to-text generation are mostly based on data-driven end-toend training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operationguided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed operations. Experiments on two sports datasets show our proposed method clearly improves the fidelity of the generated texts to the input structured data.
Vietnamese Open Information Extraction

Open information extraction (OIE) is the process to extract relations and their arguments automatically from textual documents without the need to restrict the search to predefined relations. In recent years, several OIE systems for the English language have been created but there is not any system for the Vietnamese language. In this paper, we propose a method of OIE for Vietnamese using a clause-based approach. Accordingly, we exploit Vietnamese dependency parsing using grammar clauses that strives to consider all possible relations in a sentence. The corresponding clause types are identified by their propositions as extractable relations based on their grammatical functions of constituents. As a result, our system is the first OIE system named vnOIE for the Vietnamese language that can generate open relations and their arguments from Vietnamese text with highly scalable extraction while being domain independent. Experimental results show that our OIE system achieves promising results with a precision of 83.71%.
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood

We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression to approximate the marginal likelihood for an arbitrary graph structure without invoking any assumptions about decomposability. The majority of the existing methods for learning Gaussian graphical models are either restricted to decomposable graphs or require specification of a tuning parameter that may have a substantial impact on learned structures. By combining a simple sparsity inducing prior for the graph structures with a default reference prior for the model parameters, we obtain a fast and easily applicable scoring function that works well for even high-dimensional data. We demonstrate the favourable performance of our approach by large-scale comparisons against the leading methods for learning non-decomposable Gaussian graphical models. A theoretical justification for our method is provided by showing that it yields a consistent estimator of the graph structure.
Radio Frequency ( RF ) Time-of-Flight Ranging for Wireless Sensor Networks

Position information of nodes within Wireless Sensor Networks (WSNs) is often a requirement in order to make use of the data recorded by the sensors themselves. On deployment the nodes normally have no prior knowledge of their position and thus a locationing mechanism is required to determine their positions. In this paper, we describe a method to determine the point-to-point range between sensor nodes as part of the locationing process. A two-way Time-of-Flight (TOF) ranging scheme is presented using narrow-band RF. The frequency difference between the transceivers involved with the point-to-point measurement is used to obtain a sub-clock TOF phase offset measurement in order to achieve high resolution TOF measurements. The ranging algorithm has been developed and prototyped on a TI CC2430 development kit with no additional hardware being required. Performance results have been obtained for the line-of-sight (LOS), non-line-of-sight (NLOS) and indoor condition. Accuracy is typically better than 7.0m RMS for the LOS condition over 250.0m and 15.8m RMS for the NLOS condition over 120.0m using a one-hundred sample average. Indoor accuracy is measured to 1.7m RMS using a 1000 sample average over 8.0m. Ranging error is linear and does not increase with increased transmitter-receiver distance. Our TOA ranging scheme demonstartes a novel system where resolution and accuracy are time dependent in comparison to alternative frequency dependent methods using narrowband RF.
Barrier-Restoring Therapies in Atopic Dermatitis: Current Approaches and Future Perspectives

Atopic dermatitis is a multifactorial, chronic relapsing, inflammatory disease, characterized by xerosis, eczematous lesions, and pruritus. The latter usually leads to an "itch-scratch" cycle that may compromise the epidermal barrier. Skin barrier abnormalities in atopic dermatitis may result from mutations in the gene encoding for filaggrin, which plays an important role in the formation of cornified cytosol. Barrier abnormalities render the skin more permeable to irritants, allergens, and microorganisms. Treatment of atopic dermatitis must be directed to control the itching, suppress the inflammation, and restore the skin barrier. Emollients, both creams and ointments, improve the barrier function of stratum corneum by providing it with water and lipids. Studies on atopic dermatitis and barrier repair treatment show that adequate lipid replacement therapy reduces the inflammation and restores epidermal function. Efforts directed to develop immunomodulators that interfere with cytokine-induced skin barrier dysfunction, provide a promising strategy for treatment of atopic dermatitis. Moreover, an impressive proliferation of more than 80 clinical studies focusing on topical treatments in atopic dermatitis led to growing expectations for better therapies.
Relationship Between Psychological Empowerment and Productivity of Medical Librarians

Psychological empowerment is really important and has remarkable effect on different organizational variables such as job satisfaction, organizational commitment, productivity, etc. So the aim of this study was to investigate the relationship between psychological empowerment and productivity of Librarians in Isfahan Medical University.



This was correlational research. Data were collected through two questionnaires. Psychological empowerment questionnaire and the manpower productivity questionnaire of Gold. Smith Hersey which their content validity was confirmed by experts and their reliability was obtained by using Cronbach's Alpha coefficient, 0.89 and 0.9 respectively. Due to limited statistical population, did not used sampling and review was taken via census. So 76 number of librarians were evaluated. Information were reported on both descriptive and inferential statistics (correlation coefficient tests Pearson, Spearman, T-test, ANOVA), and analyzed by using the SPSS19 software.



In our study, the trust between partners and efficacy with productivity had the highest correlation. Also there was a direct relationship between psychological empowerment and the productivity of labor (r =0.204). In other words, with rising of mean score of psychological empowerment, the mean score of efficiency increase too.



The results showed that if development programs of librarian's psychological empowerment increase in order to their productivity, librarians carry out their duties with better sense. Also with using the capabilities of librarians, the development of creativity with happen and organizational productivity will increase.
Real-Time Dense Geometry from a Handheld Camera

We present a novel variational approach to estimate dense depth maps from multiple images in real-time. By using robust penalizers for both data term and regularizer, our method preserves discontinuities in the depth map. We demonstrate that the integration of multiple images substantially increases the robustness of estimated depth maps to noise in the input images. The integration of our method into recently published algorithms for camera tracking allows dense geometry reconstruction in real-time using a single handheld camera. We demonstrate the performance of our algorithm with real-world data.
System Software in Post K Supercomputer

The next flagship supercomputer in Japan, replacement of K supercomputer, is being designed toward general operation in 2020. Compute nodes, based on a manycore architecture, connected by a 6-D mesh/torus network is considered. A three level hierarchical storage system is taken into account. A heterogeneous operating system, Linux and a light-weight kernel, is designed to build suitable environments for applications. It cannot be possible without codesign of applications that the system software is designed to make maximum utilization of compute and storage resources. After a brief introduction of the post K supercomputer architecture, the design issues of the system software will be presented. Two big-data applications, genome processing and meteorological and global environmental predictions will be sketched out as target applications in the system software design. Then, it will be presented how these applications' demands affect the system software.
3 D Point Clouds in Archaeology : Advances in Acquisition , Processing and Knowledge Integration Applied to Quasi-Planar Objects

Digital investigations of the real world through point clouds and derivatives are changing how curators, cultural heritage researchers and archaeologists work and collaborate. To progressively aggregate expertise and enhance the working proficiency of all professionals, virtual reconstructions demand adapted tools to facilitate knowledge dissemination. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. In this paper, we review the state of the art of point cloud integration within archaeological applications, giving an overview of 3D technologies for heritage, digital exploitation and case studies showing the assimilation status within 3D GIS. Identified issues and new perspectives are addressed through a knowledge-based point cloud processing framework for multi-sensory data, and illustrated on mosaics and quasi-planar objects. A new acquisition, pre-processing, segmentation and ontology-based classification method on hybrid point clouds from both terrestrial laser scanning and dense image matching is proposed to enable reasoning for information extraction. Experiments in detection and semantic enrichment show promising results of 94% correct semantization. Then, we integrate the metadata in an archaeological smart point cloud data structure allowing spatio-semantic queries related to CIDOC-CRM. Finally, a WebGL prototype is presented that leads to efficient communication between actors by proposing optimal 3D data visualizations as a basis on which interaction can grow.
Compact and Computationally Efficient Representation of Deep Neural Networks

At the core of any inference procedure in deep neural networks are dot product operations, which are the component that require the highest computational resources. For instance, deep neural networks such as VGG-16 require up to 15 gigaoperations in order to perform the dot products present in a single forward pass, which results in significant energy consumption and therefore limit their use in resource-limited environments, e.g., on embedded devices or smartphones. A common approach to reduce the cost of inference is to reduce its memory complexity by lowering the entropy of the weight matrices of the neural network, e.g., by pruning and quantizing their elements. However, the quantized weight matrices are then usually represented either by a dense or sparse matrix storage format, whose associated dot product complexity is not bounded by the entropy of the matrix. This means that the associated inference complexity ultimately depends on the implicit statistical assumptions that these matrix representations make about the weight distribution, which can be in many cases suboptimal. In this paper we address this issue and present new efficient representations for matrices with low entropy statistics. These new matrix formats have the novel property that their memory and algorithmic complexity are implicitly bounded by the entropy of the matrix, consequently implying that they are guaranteed to become more efficient as the entropy of the matrix is being reduced. In our experiments we show that performing the dot product under these new matrix formats can indeed be more energy and time efficient under practically relevant assumptions. For instance, we are able to attain up to x42 compression ratios, x5 speed ups and x90 energy savings when we convert in a lossless manner the weight matrices of state-of-the-art networks such as AlexNet, VGG-16, ResNet152 and DenseNet into the new matrix formats and benchmark their respective dot product operation. Neural network compression, computationally efficient deep learning, data structures, sparse matrices, lossless coding.
The effects of animal-assisted therapy on loneliness in an elderly population in long-term care facilities.

Animal-assisted therapy (AAT) is claimed to have a variety of benefits, but almost all published results are anecdotal. We characterized the resident population in long-term care facilities desiring AAT and determined whether AAT can objectively improve loneliness.



Of 62 residents, 45 met inclusion criteria for the study. These 45 residents were administered the Demographic and Pet History Questionnaire (DPHQ) and Version 3 of the UCLA Loneliness Scale (UCLA-LS). They were then randomized into three groups (no AAT; AAT once/week; AAT three times/week; n = 15/group) and retested with the UCLA-LS near the end of the 6-week study.



Use of the DPHQ showed residents volunteering for the study had a strong life-history of emotional intimacy with pets and wished that they currently had a pet. AAT was shown by analysis of covariance followed by pairwise comparison to have significantly reduced loneliness scores in comparison with the no AAT group.



The desire for AAT strongly correlates with previous pet ownership. AAT reduces loneliness in residents of long-term care facilities.
SPEECH SIGNAL ENHANCEMENT USING FIREFLY OPTIMIZATION ALGORITHM

The speech signal enhancement is essential to obtain clean speech signal from noisy signal. For multimodal optimization, the natural-inspired algorithms such as Firefly Algorithm (FA) are better. The proposed algorithm contains preprocessing module, optimization module and spectral filtering module. Here, Loizou’s and Aurora databases are considered for signals. In this paper the Perceptional Evolution of Speech Quality (PESQ) and Signal-to-Noise ratio (SNR) of the enhanced signal are calculated to evaluate the performance of Firefly Algorithm.
Hereditary spherocytosis, elliptocytosis, and other red cell membrane disorders.

Hereditary spherocytosis and elliptocytosis are the two most common inherited red cell membrane disorders resulting from mutations in genes encoding various red cell membrane and skeletal proteins. Red cell membrane, a composite structure composed of lipid bilayer linked to spectrin-based membrane skeleton is responsible for the unique features of flexibility and mechanical stability of the cell. Defects in various proteins involved in linking the lipid bilayer to membrane skeleton result in loss in membrane cohesion leading to surface area loss and hereditary spherocytosis while defects in proteins involved in lateral interactions of the spectrin-based skeleton lead to decreased mechanical stability, membrane fragmentation and hereditary elliptocytosis. The disease severity is primarily dependent on the extent of membrane surface area loss. Both these diseases can be readily diagnosed by various laboratory approaches that include red blood cell cytology, flow cytometry, ektacytometry, electrophoresis of the red cell membrane proteins, and mutational analysis of gene encoding red cell membrane proteins.
Self-Control : Skill , Knowledge , or Perishable Resource ?

The effect of self-control on individual behavior has long been a subject of debate. Previous investigations have led to three main models of self-control, with contradictory predictions. By perceiving self-control as a knowledge, skill, and perishable resource, those models make assertions of a positive, neutral, and negative impact, respectively, of an initial act of self-control on subsequent self-control ability. Using biometric data enabled us to settle this controversy by developing a unified model of self-control that reconciles the diverging results in the literature. By monitoring actual compliance with an initial self-control task, our model overcomes the main limitation of assuming full compliance with the self-control treatment and restricting the analysis to linear relationships. The results suggest a dual impact of an initial act of self-control on subsequent self-control ability. Specifically, while an initial moderate self-control act enhances subsequent self-control ability, exerting self-control beyond a certain threshold causes fatigue and reduces subsequent self-control ability.
Keyword focused web crawler

Users and uses of internet is growing tremendously these days which causing an extreme trouble and efforts at user side to get web pages searched which are as per concern and relevant to user's requirement Generally users approach to search web pages from a large available hierarchy of concepts or use a query to browse web pages from available search engine and receive results based on search pattern where few of the results are relevant to search and most of them are not. Web crawler plays an important role in search engine and act as a key element when performance is considered. This paper includes domain engineering concept and keyword driven crawling with relevancy decision mechanism and uses Ontology concepts which ensures the best path for improving crawler's performance. This paper introduces extraction of URLs based on keyword or search criteria. It extracts URLs for web pages which contains searched keyword in their content and considers such pages only as important and doesn't download web pages irrelevant to search. It offers high optimality comparing with traditional web crawler and can enhance search efficiency with more accuracy.
Unusual variants of malignant melanoma

A potential diagnostic pitfall in the histologic assessment of melanoma is the inability to recognize unusual melanoma variants. Of these, the more treacherous examples include the desmoplastic melanoma, the nevoid melanoma, the so-called ‘minimal-deviation melanoma,’ melanoma with prominent pigment synthesis or ‘animal-type melanoma,’ and the malignant blue nevus. Also problematic are the unusual phenotypic profiles seen in vertical growth phase melanomas; these include those tumors whose morphological peculiarities mimic cancers of nonmelanocytic lineage and those melanomas that express aberrant antigenic profiles not commonly associated with a melanocytic histogenesis. Metaplastic change in melanoma, balloon cell melanoma, signet-ring cell melanoma, myxoid melanoma, small cell melanoma and rhabdoid melanoma all have the potential to mimic metastatic and primary neoplasms of different lineage derivations. Abnormal immunohistochemical expression of CD 34, cytokeratins, epithelial membrane antigen, and smooth muscle markers as well as the deficient expression of S100 protein and melanocyte lineage-specific markers such as GP100 protein (ie HMB-45 antibody) and A103 (ie Melan-A) also present confusing diagnostic challenges. In this review, we will discuss in some detail certain of these novel clinicopathologic types of melanoma, as well as the abnormal phenotypic expressions seen in vertical growth phase melanoma.