nils-herrmann/clean_scirepeval huggingface dataset:
Changes of shear moduli in carbonate rocks : Implications for Gassmann applicability

Understanding the effects of saturation on the acoustic properties of porous media is paramount for using amplitude versus offset (AVO) technique and 4-D seismic. Most laboratory research on saturation effects has been carried out in sandstones, despite the fact that about half of the world’s oil and gas reserves are in carbonates. We conducted saturation experiments in carbonates with the intention to fill this gap. These experimental data are used to test theoretical assumptions in AVO and seismic analysis in general. Earlier studies have shown that the complex pore structures of carbonates produce poorly defined porosity-velocity trends. Although porosity is the most important factor to control sonic velocity, our data document that pore type, pore fluid compressibility and variations in shear modulus due to saturation are also important factors for velocities in carbonate rocks. Complete saturation of the pore space separated our samples into two groups: one group showed decreases in shear bulk modulus of the rock by up to 2 GPa, the other group showed increase by up to 3 GPa. This change in shear modulus questions Gassmann's assumption of constant shear modulus in dry and saturated rocks. It also explains our observation that velocities predicted with by the Gassmann equation underand overestimates the measured velocities of saturated carbonate samples. In addition, the Vp/Vs ratio shows an overall increase with saturation. In particular, rocks displaying shear weakening have distinct higher Vp/Vs ratios.
Investigating pipeline and state of the art blood glucose biosensors to formulate next steps.

Ten years on from a review in the twentieth issue of this journal, this contribution assess the direction research in the field of glucose sensing for diabetes is headed and various technologies to be seen in the future. The emphasis of this review was placed on the home blood glucose testing market. After an introduction to diabetes and glucose sensing, this review analyses state of the art and pipeline devices; in particular their user friendliness and technological advancement. This review complements conventional reviews based on scholarly published papers in journals.
Genetic algorithm-neural network : feature extraction for bioinformatics data.

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data.
ContainerLeaks: Emerging Security Threats of Information Leakages in Container Clouds

Container technology provides a lightweight operating system level virtual hosting environment. Its emergence profoundly changes the development and deployment paradigms of multi-tier distributed applications. However, due to the incomplete implementation of system resource isolation mechanisms in the Linux kernel, some security concerns still exist for multiple containers sharing an operating system kernel on a multi-tenancy container cloud service. In this paper, we first present the information leakage channels we discovered that are accessible within the containers. Such channels expose a spectrum of system-wide host information to the containers without proper resource partitioning. By exploiting such leaked host information, it becomes much easier for malicious adversaries (acting as tenants in the container clouds) to launch advanced attacks that might impact the reliability of cloud services. Additionally, we discuss the root causes of the containers' information leakages and propose a two-stage defense approach. As demonstrated in the evaluation, our solution is effective and incurs trivial performance overhead.
The relationship between nursing leadership and patient outcomes: a systematic review update.

Our aim was to describe the findings of a systematic review of studies that examine the relationship between nursing leadership practices and patient outcomes.



As healthcare faces an economic downturn, stressful work environments, upcoming retirements of leaders and projected workforce shortages, implementing strategies to ensure effective leadership and optimal patient outcomes are paramount. However, a gap still exists in what is known about the association between nursing leadership and patient outcomes.



Published English-only research articles that examined leadership practices of nurses in formal leadership positions and patient outcomes were selected from eight online bibliographic databases. Quality assessments, data extraction and analysis were completed on all included studies.



A total of 20 studies satisfied our inclusion criteria and were retained. Current evidence suggests relationships between positive relational leadership styles and higher patient satisfaction and lower patient mortality, medication errors, restraint use and hospital-acquired infections.



The findings document evidence of a positive relationship between relational leadership and a variety of patient outcomes, although future testing of leadership models that examine the mechanisms of influence on outcomes is warranted.



Efforts by organisations and individuals to develop transformational and relational leadership reinforces organisational strategies to improve patient outcomes.
Making machine learning models interpretable

Data of different levels of complexity and of ever growing diversity of characteristics are the raw materials that machine learning practitioners try to model using their wide palette of methods and tools. The obtained models are meant to be a synthetic representation of the available, observed data that captures some of their intrinsic regularities or patterns. Therefore, the use of machine learning techniques for data analysis can be understood as a problem of pattern recognition or, more informally, of knowledge discovery and data mining. There exists a gap, though, between data modeling and knowledge extraction. Models, depending on the machine learning techniques employed, can be described in diverse ways but, in order to consider that some knowledge has been achieved from their description, we must take into account the human cognitive factor that any knowledge extraction process entails. These models as such can be rendered powerless unless they can be interpreted, and the process of human interpretation follows rules that go well beyond technical prowess. For this reason, interpretability is a paramount quality that machine learning methods should aim to achieve if they are to be applied in practice. This paper is a brief introduction to the special session on interpretable models in machine learning, organized as part of the 20 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. It includes a discussion on the several works accepted for the session, with an overview of the context of wider research on interpretability of machine learning models.
On-demand Injection of Lexical Knowledge for Recognising Textual Entailment

We approach the recognition of textual entailment using logical semantic representations and a theorem prover. In this setup, lexical divergences that preserve semantic entailment between the source and target texts need to be explicitly stated. However, recognising subsentential semantic relations is not trivial. We address this problem by monitoring the proof of the theorem and detecting unprovable sub-goals that share predicate arguments with logical premises. If a linguistic relation exists, then an appropriate axiom is constructed on-demand and the theorem proving continues. Experiments show that this approach is effective and precise, producing a system that outperforms other logicbased systems and is competitive with state-of-the-art statistical methods.
Spatiotemporal Multi-Task Network for Human Activity Understanding

Recently, remarkable progress has been achieved in human action recognition and detection by using deep learning techniques. However, for action detection in real-world untrimmed videos, the accuracies of most existing approaches are still far from satisfactory, due to the difficulties in temporal action localization. On the other hand, the spatiotempoal features are not well utilized in recent work for video analysis. To tackle these problems, we propose a spatiotemporal, multi-task, 3D deep convolutional neural network to detect (including temporally localize and recognition) actions in untrimmed videos. First, we introduce a fusion framework which aims to extract video-level spatiotemporal features in the training phase. And we demonstrate the effectiveness of video-level features by evaluating our model on human action recognition task. Then, under the fusion framework, we propose a spatiotemporal multi-task network, which has two sibling output layers for action classification and temporal localization, respectively. To obtain precise temporal locations, we present a novel temporal regression method to revise the proposal window which contains an action. Meanwhile, in order to better utilize the rich motion information in videos, we introduce a novel video representation, interlaced images, as an additional network input stream. As a result, our model outperforms state-of-the-art methods for both action recognition and detection on standard benchmarks.
Sentiment Classification of Movie Reviews Using Contextual Valence Shifters

We present two methods for determining the sentiment expressed by a movie review. The semantic orientation of a review can be positive, negative, or neutral. We examine the effect of valence shifters on classifying the reviews. We examine three types of valence shifters: negations, intensifiers, and diminishers. Negations are used to reverse the semantic polarity of a particular term, while intensifiers and diminishers are used to increase and decrease, respectively, the degree to which a term is positive or negative. The first method classifies reviews based on the number of positive and negative terms they contain. We use the General Inquirer to identify positive and negative terms, as well as negation terms, intensifiers, and diminishers. We also use positive and negative terms from other sources, including a dictionary of synonym differences and a very large Web corpus. To compute corpus-based semantic orientation values of terms, we use their association scores with a small group of positive and negative terms. We show that extending the term-counting method with contextual valence shifters improves the accuracy of the classification. The second method uses a Machine Learning algorithm, Support Vector Machines. We start with unigram features and then add bigrams that consist of a valence shifter and another word. The accuracy of classification is very high, and the valence shifter bigrams slightly improve it. The features that contribute to the high accuracy are the words in the lists of positive and negative terms. Previous work focused on either the term-counting method or the Machine Learning method. We show that combining the two methods achieves better results than either method alone.
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power. ESPNet is 22 times faster (on a standard GPU) and 180 times smaller than the state-of-the-art semantic segmentation network PSPNet , while its categorywise accuracy is only 8% less. We evaluated ESPNet on a variety of semantic segmentation datasets including Cityscapes, PASCAL VOC, and a breast biopsy whole slide image dataset. Under the same constraints on memory and computation, ESPNet outperforms all the current efficient CNN networks such as MobileNet [16], ShuffleNet , and ENet  on both standard metrics and our newly introduced performance metrics that measure efficiency on edge devices. Our network can process high resolution images at a rate of 112 and 9 frames per second on a standard GPU and edge device, respectively.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

We present a novel framework that enables efficient probabilistic inference in large-scale scientific models by allowing the execution of existing domain-specific simulators as probabilistic programs, resulting in highly interpretable posterior inference. Our framework is general purpose and scalable, and is based on a crossplatform probabilistic execution protocol through which an inference engine can control simulators in a language-agnostic way. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the τ (tau) lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Highenergy physics has a rich set of simulators based on quantum field theory and the interaction of particles in matter. We show how to use probabilistic programming to perform Bayesian inference in these existing simulator codebases directly, in particular conditioning on observable outputs from a simulated particle detector to directly produce an interpretable posterior distribution over decay pathways. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of Markov chain Monte Carlo sampling.
Mitigation of Lower Order Harmonics in a Grid-Connected Single-Phase PV Inverter

In this paper, a simple single-phase grid-connected photovoltaic (PV) inverter topology consisting of a boost section, a low-voltage single-phase inverter with an inductive filter, and a step-up transformer interfacing the grid is considered. Ideally, this topology will not inject any lower order harmonics into the grid due to high-frequency pulse width modulation operation. However, the nonideal factors in the system such as core saturation-induced distorted magnetizing current of the transformer and the dead time of the inverter, etc., contribute to a significant amount of lower order harmonics in the grid current. A novel design of inverter current control that mitigates lower order harmonics is presented in this paper. An adaptive harmonic compensation technique and its design are proposed for the lower order harmonic compensation. In addition, a proportional-resonant-integral (PRI) controller and its design are also proposed. This controller eliminates the dc component in the control system, which introduces even harmonics in the grid current in the topology considered. The dynamics of the system due to the interaction between the PRI controller and the adaptive compensation scheme is also analyzed. The complete design has been validated with experimental results and good agreement with theoretical analysis of the overall system is observed.
Adversarial Examples that Fool both Human and Computer Vision

Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we create the first adversarial examples designed to fool humans, by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by modifying models to more closely match the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.
Leadership in the digital business transformation

The role of leadership in digital business transformation is a topical issue in need of more in-depth research. Based on an empirical investigation of eight Finnish organizations operating in the service sector, we gain understanding of the role and focus of leadership in the context of digital business transformation. Through a qualitative content analysis of data from 46 interviews, the four main leadership foci of digital business transformation are found: strategic vision and action, leading cultural change, enabling, and leading networks. The findings are discussed in the context of extant research on leadership and digital business development.
A Comparison of Features for the Automatic Labeling of Student answers to Open-ended Questions

The automatic evaluation of text-based assessment items, such as short answers or essays, is an open and important research challenge. In this paper, we compare several features for the classification of short open-ended responses to questions related to a large first-year health sciences course. These features include a) traditional n-gram models; b) entity URIs (Uniform Resource Identifier) and c) entity mentions extracted using a semantic annotation API; d) entity mention embeddings based on GloVe, and e) entity URI embeddings extracted from Wikipedia. These features are used in combination with classification algorithms to discriminate correct answers from incorrect ones. Our results show that, on average, n-gram features performed the best in terms of precision and entity mentions in terms of f1-score. Similarly, in terms of accuracy, entity mentions and n-gram features performed the best. Finally, features based on dense vector representations such as entity embeddings and mention embeddings obtained the best f1-score for predicting correct answers.
A Project-based Digital Storytelling Approach for Improving Students' Learning Motivation, Problem-Solving Competence and Learning Achievement

Although project-based learning is a well-known and widely used instructional strategy, it remains a challenging issue to effectively apply this approach to practical settings for improving the learning performance of students. In this study, a project-based digital storytelling approach is proposed to cope with this problem. With a quasiexperiment, the proposed approach has been applied to a learning activity of a science course in an elementary school. A total of 117 Grade 5 students in an elementary school in southern Taiwan were assigned to an experimental group (N = 60) and a control group (N = 57) to compare the performance of the approach with that of conventional project-based learning. A web-based information-searching system, Meta-Analyzer, was used to enable the students to collect data on the Internet based on the questions raised by the teachers, and Microsoft’s Photo Story was used to help the experimental group develop movies for storytelling based on the collected data. Moreover, several measuring tools, including the science learning motivation scale, the problem-solving competence scale and the science achievement test, were used to collect feedback as well as evaluate the learning performance of the students. The experimental results show that the project-based learning with digital storytelling could effectively enhance the students’ science learning motivation, problem-solving competence, and learning achievement.
BlockDrop: Dynamic Inference Paths in Residual Networks

Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications. We introduce BlockDrop, an approach that learns to dynamically choose which layers of a deep network to execute during inference so as to best reduce total computation without degrading prediction accuracy. Exploiting the robustness of Residual Networks (ResNets) to layer dropping, our framework selects on-the-fly which residual blocks to evaluate for a given novel image. In particular, given a pretrained ResNet, we train a policy network in an associative reinforcement learning setting for the dual reward of utilizing a minimal number of blocks while preserving recognition accuracy. We conduct extensive experiments on CIFAR and ImageNet. The results provide strong quantitative and qualitative evidence that these learned policies not only accelerate inference but also encode meaningful visual information. Built upon a ResNet-101 model, our method achieves a speedup of 20% on average, going as high as 36% for some images, while maintaining the same 76.4% top-1 accuracy on ImageNet.
A Fully-Integrated Low-Dropout Regulator With Full-Spectrum Power Supply Rejection

A fully-integrated low-dropout regulator (LDO) with fast transient response and full spectrum power supply rejection (PSR) is proposed to provide a clean supply for noise-sensitive building blocks in wideband communication systems. With the proposed point-of-load LDO, chip-level high-frequency glitches are well attenuated, consequently the system performance is improved. A tri-loop LDO architecture is proposed and verified in a 65 nm CMOS process. In comparison to other fully-integrated designs, the output pole is set to be the dominant pole, and the internal poles are pushed to higher frequencies with only 50 μA of total quiescent current. For a 1.2 V input voltage and 1 V output voltage, the measured undershoot and overshoot is only 43 mV and 82 mV, respectively, for load transient of 0 μA to 10 mA within edge times of 200 ps. It achieves a transient response time of 1.15 ns and the figure-of-merit (FOM) of 5.74 ps. PSR is measured to be better than -12 dB over the whole spectrum (DC to 20 GHz tested). The prototype chip measures 260×90 μm2, including 140 pF of stacked on-chip capacitors.