nils-herrmann/clean_scirepeval huggingface dataset:
A Tale of Two Studies on Auditory Training in Children: A Response to the Claim that 'Discrimination Training of Phonemic Contrasts Enhances Phonological Processing in Mainstream School Children' by Moore, Rosenberg and Coleman (2005)
Venue: Dyslexia
Year: 2014

In a previous article, Moore, Rosenberg and Coleman (Brain and Language, 2005, 94, 72 85) reported evidence for significant improvements in phonological awareness in mainstream children following 6 h of exposure to a commercially available phoneme discrimination training programme, but not in a control group. In a follow up study, we failed to replicate this finding, despite using an almost identical training programme (Halliday, Taylor, Millward, Moore,  2012, 55, 168 181) This paper directly compares the methods and the results of the two studies, in an effort to explain the discrepant findings. It reports that the trained group in Moore et al. (2005) showed significantly greater improvements in phonological awareness following training than the trained group in Halliday et al. (2012) However, the control group in Halliday et al. (2012) showed significantly greater improvements in phonological awareness than the control group in Moore et al. (2005) The paper concludes that differences in the randomization, blinding, experimenter familiarity and treatment of trained and control groups contributed to the different outcomes of the two studies. The results indicate that a plethora of factors can contribute to training effects and highlight the importance of well designed randomized controlled trials in assessing the efficacy of a given intervention.
Boosting Black Box Variational Inference
Venue: NeurIPS
Year: 2018

Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by choosing a relatively simple variational approximation. Borrowing ideas from the classic boosting framework, recent approaches attempt to \emph{boost} VI by replacing the selection of a single density with an iteratively constructed mixture of densities. In order to guarantee convergence, previous works impose stringent assumptions that require significant effort for practitioners. Specifically, they require a custom implementation of the greedy step (called the LMO) for every probabilistic model with respect to an unnatural variational family of truncated distributions. Our work fixes these issues with novel theoretical and algorithmic insights. On the theoretical side, we show that boosting VI satisfies a relaxed smoothness assumption which is sufficient for the convergence of the functional Frank Wolfe (FW) algorithm. Furthermore, we rephrase the LMO problem and propose to maximize the Residual ELBO (RELBO) which replaces the standard ELBO optimization in VI. These theoretical enhancements allow for black box implementation of the boosting subroutine. Finally, we present a stopping criterion drawn from the duality gap in the classic FW analyses and exhaustive experiments to illustrate the usefulness of our theoretical and algorithmic contributions.
The Gini index of travel demand imbalances
Year: 2017

Along a public transport line operators serve multiple spatio temporally differentiated markets with the same second best capacity. Horcher and Graham (2016) showed in the simplest back haul setting that the asymmetry in demand between jointly served markets may have crucial impact on (1) the optimal capacity, (2) the equilibrium occupancy rate of vehicles and thus the crowding experience of passengers, and (3) the optimal pricing decisions. In this research the authors extend the analysis of the back haul problem to a more realistic urban public transport setting: a transit line along which capacity is still indivisible due to operational constraints, but more than two origin destination pairs have to be served. We investigate what may be a suitable measure of demand imbalances in this setting that could replace the share of main haul demand in total ridership in the back haul problem (Horcher and Graham, 2016) In a network not only the spread of demand between line section matters, but also the spread of operational costs between them, as it may be increasingly expensive to handle excess demand on relatively long line section. To tackle these challenges we propose the Gini coefficient of demand imbalances, a statistical index frequently used in macroeconomics as a measure of income inequality. The Gini coefficient measures statistical dispersion between two frequency distributions. In a public transport context we can adopt this concept by plotting the cumulative share of operational costs on line sections against the cumulative share of demand, after sorting both variables in increasing order. The resulting Lorenz curve is the diagonal of the graph in case of perfect equality, while if all demand is concentrated on a negligably short line section, then the Lorenz curve moves along the two sides of the graph  The Gini index is the share of the area between the actual Lorenz curve and the one belonging to perfect equality
Room temperature high Giant Magnetoresistance graphene based spin valve and its application for realization of logic gates
Year: 2020

In this study we carried out first principle calculations for spin dependent electronic transport in our proposed spintronic device consisting of graphene nanoribbon as scattering region with cobalt electrodes and magnesium oxide as tunneling barrier. The I V Characteristics are calculated by using Non Equilibrium Green's Function formalism in combination with Semi Empirical (SE) Extended Huckel Theory (EHT) using Atomistic Tool Kit software at room temperature. The high value of GMR (Giant Magnetoresistance) is recommended for spintronic devices. The results reveal that the device shows a very high GMR ratio of 5.16 x 10 4 The device also shows very low off state current, therefore negligible off state power consumption. The investigation of spin dependent non equilibrium transport is done by examining bias dependent transmission coefficients. Furthermore, some basic logic gates, like NOT, AND and NOR have been realized from the modeled device. The device may also find applications in data storage, magnetic field sensors etc.
Predicting sepsis with a recurrent neural network using the MIMIC III database
Venue: Comput. Biol. Medicine
Year: 2019

Predicting sepsis onset with a recurrent neural network and performance comparison with InSight a previously proposed algorithm for the prediction of sepsis onset.  A retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC III database) who did not fall under the definition of sepsis at the time of admission. The area under the receiver operating characteristic (AUROC) measures the performance of the prediction task. We examine the sequence length given to the machine learning algorithms for different points in time before sepsis onset concerning the prediction performance. Additionally, the impact of sepsis onset's definition is investigated. We evaluate the model with a relatively large and thus more representative patient population compared to related works in the field.  For a prediction 3 h prior to sepsis onset, our network achieves an AUROC of 0.81 (95% CI: 0.78 0.84) The InSight algorithm achieves an AUROC of 0.72 (95% CI: 0.69 0.75) For a fixed sensitivity of 90% our network reaches a specificity of 47.0% (95% CI: 43.1% 50.8% compared to 31.1% (95% CI: 24.8% 37.5% for InSight. In addition, we compare the performance for 6 and 12 h prediction time for both approaches.  Our findings demonstrate that a recurrent neural network is superior to InSight considering the prediction performance. Most probably, the improvement results from the network's ability of revealing time dependencies. We show that the length of the look back has a significant impact on the performance of the classifier. We also demonstrate that for the correct detection of sepsis onset for a retrospective analysis, further research is necessary.
Relationship Between Knowledge, Education and Family Support with Exclusive Breastfeeding
Year: 2019

Breastfeeding is an ideal source of nutrition with a balanced composition and adapted to the baby's growth needs. Based on data from the 2012 IDHS, babies who exclusively breastfed up to 6 months were 42 percent. The main causes are low maternal knowledge and lack of family support in breastfeeding. Peucangpari Village is one of the villages in the working area of Cigemblong Health Center, Lebak, Banten, where the coverage of exclusive breastfeeding is still low, which is only 11.9% The purpose of this study was to determine the relationship between knowledge, education and family support with exclusive breastfeeding. This study is a correlation with a cross sectional approach. The population in this study were mothers who had babies aged 6 24 months in Peucangpari Village, Cigemblong Lebak as many as 42 people, the study sample was taken using a total population technique. The results showed that there was a relationship between knowledge, education, and family support with exclusive breastfeeding in Peucangpari Village, Cigemblong Lebak in 2018. Researchers suggested the need to provide health education to all communities, especially mothers who have babies about the benefits of exclusive breastfeeding, so that the community or mother those who have not given exclusive breastfeeding to their babies can increase their knowledge and will give exclusive breastfeeding to their babies.
Impact of sodium bicarbonate treatment of waste hemp fibers on the properties of dicyanate ester of bisphenol A/bisphenol A based benzoxazine resin composites
Venue: Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications
Year: 2019

This present study details an experimental investigation on understanding the effects of surface modified waste hemp fibers on the overall properties of thermosetting resin blends composed of dicyanate ester of bisphenol A and bisphenol A based benzoxazine resins. At first, the hemp fibers were subjected to sodium bicarbonate treatment, and then various experimental tests in terms of the Fourier transform infrared spectroscopy, scanning electron microscopy, and thermogravimetric analysis tests were, respectively, carried out to understand the influence of the as used treatment on the structural, morphological, and thermal properties of the hemp fibers. The obtained results from the surface characterization evidently demonstrated the positive sodium bicarbonate impacts on the properties of the hemp fibers. After that, various amounts of the treated fibers were incorporated within the dicyanate ester of bisphenol A/bisphenol A based benzoxazine resin blends and the obtained composite materials were characterized for their structural, mechanical behavior and thermal properties. The obtained results clearly revealed that the prepared blend composites were identified to possess good bending and impact strength properties; however, a slight decline was observed in the thermal properties. The scanning electron microscopy and Fourier transform infrared spectroscopy investigations demonstrated a good dispersion of the fibers within the blend matrix, and also an enhanced fiber blend matrix interfacial adhesion, which were plausibly behind the improved mechanical properties of the prepared blend composites.
Towards a Generic Education Pattern of System Analysis and Design Courses: An Empirical Study
Year: 2017

System analysis and design plays an integral part in the development process, which makes it a mandatory and important topic in software engineering education. However, the diverse design concerns and activities involved in deign process make it a great difficult to deliver system design topic effectively. To enhance the learning process, we utilized the mission guides style teaching in our system design course. Students are required to analyze and design a package delivery application system by using suggested open materials and tools. Positive feedbacks are received on stimulating students' interest. To explore a more generic education pattern for system analysis and design education, we conducted an empirical study based on the information and reflection collected during course delivery process. Students' presentation performance, assignment quality, final reflection and professor's evaluation were summarized and analyzed. In this study, we found that (1) teamwork can stimulate students' learning interest and passion, making class atmosphere active; (2) scenario based modeling practice can help students improve the understanding of knowledge more quickly and easily; (3) most students prefer (Sino English) Bilingual teaching method to gain a more professional education. These results indicate the key factors towards a generic education pattern of system design course. It is also expected to be utilized on better courses preparing.
Sostavy faz vzaimodeistviia redkozemel'nykh metallov s selenom
Year: 2013

Metodami differentsial'nogo termicheskogo i rentgenofazovogo analiza izucheno vzaimodeistvie redkozemel'nykh metallov (Pr, Nd, Sm, Y, Er, Yb) s selenom pri sootnosheniiakh Ln Se 1 2, 2 3, 3 4, 1 1 v politermicheskikh (2931270 K) i izotermicheskikh (770, 1170 K) rezhimakh termoobrabotki. Pri nagrevanii shikhty ekzoeffekt vzaimodeistviia elementov proiavliaetsia v intervalakh temperatur 620750 K (Pr) 580870 K (Sm) 620920 K (Er) Pri 770 K, po dannym RFA, v gomogennom sostoianii polucheny fazy LnSe2 i Yb2Se3, pri 1170 K Ln3Se4, Ln2Se3 i YbSe. Po dannym metoda rastrovoi elektronnoi mikroskopii, chastitsy selenidnykh faz obrazovany fragmentami 1 25 mkm, kotorye sozdaiut gubchatuiu strukturu aglomeratov s razmerami ot desiati do soten mikron.
Fast Semantic Segmentation for Scene Perception
Venue: IEEE Transactions on Industrial Informatics
Year: 2019

Semantic segmentation is a challenging problem in computer vision. Many applications, such as autonomous driving and robot navigation with urban road scene, need accurate and efficient segmentation. Most state of the art methods focus on accuracy, rather than efficiency. In this paper, we propose a more efficient neural network architecture, which has fewer parameters, for semantic segmentation in the urban road scene. An asymmetric encoder decoder structure based on ResNet is used in our model. In the first stage of encoder, we use continuous factorized block to extract low level features. Continuous dilated block is applied in the second stage, which ensures that the model has a larger view field, while keeping the model small scale and shallow. The down sampled features from encoder are up sampled with decoder to the same size output as the input image and the details refined. Our model can achieve end to end and pixel to pixel training without pretraining from scratch. The parameters of our model are only <inline formula><tex math notation="LaTeX"$0.2M$/tex math>/inline formula> <inline formula><tex math notation="LaTeX"$100 \times$/tex math>/inline formula> less than those of others such as SegNet, etc. Experiments are conducted on five public road scene datasets (CamVid, CityScapes, Gatech, KITTI Road Detection, and KITTI Semantic Segmentation) and the results demonstrate that our model can achieve better performance.
Organic consumers' price knowledge, willingness to pay and purchase decision
Year: 2016

The purpose of this paper is to gain insights into organic consumers' price sensitivity by investigating price knowledge, willingness to pay and real purchase decision.  Organic food consumers' price knowledge, willingness to pay and real purchase decision were examined in a comprehensive field study with 642 respondents. An innovative method was used to collect data for products that were truly relevant to the respondents: before entering the shop, respondents were asked about the items on their shopping list, the prices they expected to find and the maximum prices they were willing to pay. If respondents stated a willingness to pay value below the actual store price, they were approached again after shopping to verify their purchase decision.  The great majority of respondents failed to estimate the correct store price. The deviation between the estimated price and the actual store price was on average 19.9 per cent. The respondents were willing to pay on average 52.7 per cent above store prices. It was revealed that in 67.0 per cent of the cases, respondents bought a product even though the store price was higher than the willingness to pay they stated upon entering the store.  Category specific insights into price knowledge and willingness to pay of organic consumers might be used for price differentiation strategies.  To the authors' knowledge, this is the first study to investigate organic consumers' item and store specific price knowledge, willingness to pay and real purchase decision in a single source approach.
Differential Fault Analysis on AES and DES
Year: 2013

The ability of symmetric encryption algorithm AES and DES against the differential fault analysis is examined.Two kinds of differential fault analysis on AES are described,and the initial key is recovered by software emulation.The result shows that the attack on AES can be realized by 20 times of fault inductions.Then a new differential fault analysis on DES is described.The principle of this attack is analogous to differential fault analysis on AES.Result shows that attack on DES needs more fault inductions.Therefore symmetric encryption algorithm AES and DES without any protection are vulnerable to differential fault analysis.It is presented that such attacks can be resisted by fault detection.
Cognitive Analysis in Social Networks for Viral Marketing
Venue: IEEE Transactions on Industrial Informatics
Year: 2021

Viral marketing is the modern version of the old "word of mouth" advertising, where companies choose a restricted number of persons, considered "influential," recommending them products or services that will be in turn iteratively suggested. In this article, we propose cognitive models and algorithms for marketing applications through online social networks, considered as a graph database, and define the concept of influence graph leveraging particular user behavioral patterns, by querying the initial heterogeneous graph network. We also model the diffusion across the network, without any preliminary information, as a combinatorial multiarmed bandit problem, for the selection of most influential users. We have used the YELP social network as a case study for our approach, showing how it is possible to generate an influence graph considering several kinds of relevant paths (mainly considering reviews to the same firms) by which a user can influence other ones. Several experiments have been carried out and discussed, putting into evidence the effectiveness and efficacy of the proposed methods for influence maximization with respect to other approaches of state of the art.
Deep Learning for Image Super Resolution: A Survey
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2021

Image Super Resolution (SR) is an important class of image processing techniqueso enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.
Applying a systems thinking framework to assess knowledge assets dynamics for business performance improvement
Venue: Expert Syst. Appl.
Year: 2012

Knowledge assets represent strategic resources and sources of organizational value creation. Their effective development and deployment is at the basis of organizational value creation capacity. However there is still a lack of applied approaches and tools explaining how knowledge assets dynamics take place in organizational value creation mechanisms. In particular, there is a managerial need to define decision support frameworks that can enable managers to understand how knowledge assets interact each other and with organizational performance in order to support the achievement of company's strategic objectives. A better understanding of why and how knowledge assets management initiatives can be turned into value creation mechanisms with positive impacts on business performance is fundamental to avoid misallocation of resources and to support management decisions. This paper proposes a systems thinking based framework, the Knowledge Assets Dynamics Value Map (KAVDM) to explicate the working mechanisms by means knowledge assets can evolve on the basis of knowledge management initiatives and affect business performance improvements. The KAVDM offers a holistic view of the mechanisms at the basis of how knowledge assets are translated into organizational value. It supports the explanation and monitoring of how knowledge assets are interpedently and dependently linked, and how the management of one knowledge asset activates flow dynamics, that influence both other knowledge assets and business performance. Using the KAVDM managers can reflect upon the knowledge components grounding a company's value creation and assess their mental models and views of the reality. Finally, an application of the KAVDM within a construction company is presented and its main managerial benefits addressed.
Vision and Language Navigation using Multi head Attention Mechanism
Venue: 2020 6th International Conference on Big Data and Information Analytics (BigDIA)
Year: 2020

In the wake of the developments of deep learning, more and more research focus on the intersection fields of natural language processing and machine vision, vision and language navigation (VLN) is one of them. The VLN task needs an embodied agent to carry out the natural language instruction and navigate inside a real 3D environment with the help of visual information, planning a trajectory from start point to goal location. In this paper, inspired by the previous works, we introduce a multi head attention module with a parallel attention computing method which apply multi head attention mechanism on visual and textual input to enhance the performance of the model. Specifically, first we design a multihead attention module with trainable parameters, which can extract associated attention from the textual and visual information, extracted attention variables are used to promote the agent aware of which part of the sentence or image is more important. Second, in order to help the agent perceives more useful information, we perform a parallel computing method on extracted attention and input, i.e. visual and textual input feature, then use a layer normalization to combine them. The results of experiments indicate our proposed module enables the model to obtain better performance and surpasses the baseline model. The success rate of our model is 51% and oracle success rate is 62% with low navigation error.