nils-herrmann/clean_scirepeval huggingface dataset:
Deep Aligned Convolutional Neural Network for Skeleton Based Action Recognition and Segmentation
Venue: 2019 IEEE International Conference on Data Mining (ICDM)
Year: 2019

Convolutional neural networks (CNNs) are deep learning frameworks which are well known for their notable performance in classification tasks. Hence, many skeleton based action recognition and segmentation (SBARS) algorithms benefit from them in their designs. However, a shortcoming of such applications is the general lack of spatial relationships between the input features in such data types. Besides, non uniform temporal scalings is a common issue in skeleton based data streams which leads to having different input sizes even within one specific action category. In this work, we propose a novel deep aligned convolutional neural network (DACNN) to tackle the above challenges for the particular problem of SBARS. Our network is designed by introducing a new type of filters in the context of CNNs which are trained based on their alignments to the local subsequences in the inputs. These filters result in efficient predictions as well as learning interpretable patterns in the data. We empirically evaluate our framework on real world benchmarks showing that the proposed DACNN algorithm obtains a competitive performance compared to the state of the art while benefiting from a less complicated yet more interpretable model.
The Relationship between Physical, Motor, and Intellectual Development of Preschool Children
Year: 2016

The study examined the relationship of physical, motor, and intellectual development of preschool children. The sample included 72 children aged 5.60 0.32) years. Two anthropometric measures (body height, body weight) two motor skills tests (obstacle course, broad jump) and an intelligence test (Raven's CPM) were applied. Taxonomic analysis identified two distinctive developmental profiles in terms of physical growth and motor competence: 1) Shorter children of a lower body weight, less competent; 2) Taller, heavier and more competent children. Physical growth and motor development are positively correlated in children aged 5 6, with no such relation between these domains and intelligence.
Numerical Solution for Nonlinear Time Fractional Partial Differential Equation With Variable Coefficient Using Reproducing Kernel Hibert Space Method
Venue: 2020 2nd International Conference on Mathematics and Information Technology (ICMIT)
Year: 2020

The iterative reproducing kernel Hilbert space method is used to approximate the solution of the nonlinear time fractional partial differential equation with variable coefficient. The experiment results show the rapidity of convergence of the approximate solution and demonstrate that this method is effective and accurate method.
New Differential Fault Analysis on AES Key Schedule: Two Faults Are Enough
Venue: CARDIS
Year: 2008

In this paper we show a new differential fault analysis (DFA) on the AES 128 key scheduling process. We can obtain 96 bits of the key with 2 pairs of correct and faulty ciphertexts enabling an easy exhaustive key search of 232keys. Furthermore we can retrieve the entire 128 bits with 4 pairs. To the authors' best knowledge, it is the smallest number of pairs to find the entire AES 128 key with a fault attack on the key scheduling process. Up to now 7 pairs by Takahashi et al. were the best. By corrupting state, not the key schedule, Piret and Quisquater showed 2 pairs are enough to break AES 128 in 2003. The advantage of DFA on the key schedule is that it can defeat some fault protected AES implementations where the round keys are not rescheduled prior to the check. We implemented our algorithm on a 3.2 GHz Pentium 4 PC. With 4 pairs of correct and faulty ciphertexts, we could find 128 bits less than 2.3 seconds.
Knowledge related challenges and solutions in GSD
Venue: Expert Syst. J. Knowl. Eng.
Year: 2014

A number knowledge related challenges may complicate the work in global software development GSD projects. In practice, even a small amount of missing knowledge may cause an activity to fail to create and transfer information which is critical to later functions, causing these later functions to fail. Thus, knowledge engineering holds a central role in order to succeed with globally distributed product development. Furthermore, examining the challenges faced in GSD from a cognitive perspective will help to find solutions that take into account the knowledge needs of different stakeholders in GSD and thus help to establish conditions for successful GSD projects. In this paper, we will discuss these challenges and solutions based on an extensive literature study and practical experience gained in several international projects over the last decade. Altogether, over 50 case studies were analysed. We analysed the challenges identified in the cases from a cognitive perspective for bridging and avoiding the knowledge gaps and, based on this analysis, we will present example solutions to address the challenges during the GSD projects. We will conclude that through understanding both the nature of GSD and the KE challenges in depth, it will be possible for organizations to make their distributed operations successful.
Parametric Optimization of Laser Additive Manufacturing of Inconel 625 Using Taguchi Method and Grey Relational Analysis
Venue: Scanning
Year: 2020

In order to improve the forming quality of the Inconel 625 cladding layer and make it to be more widely used. This paper addresses an experimental investigation on the influence of major process parameters like laser power, scanning speed, powder feed rate, and overlapping rate along with their interactions on surface roughness and width error of laser additive manufacturing process for forming Inconel 625 samples. Taguchi method and grey relational analysis were used to optimize the selected parameters, and the verification tests were carried out. The change of microhardness and microstructure in the overlapping zone and nonoverlapping zone of the cladding layer were studied by microhardness tester and scanning electron microscopy (SEM) The results show that the most significant effect in processing parameters on surface roughness and width error are both overlapping rate, and the optimal levels of laser power, scanning speed, powder feeder rate, and overlapping rate are 1800 W, 8 mm/s, 10 g/min, and 30% respectively. Analysis of microstructure and composition showed that the content of Cr was high both in the Laves phase and matrix, the content of Nb in the Laves phase increased significantly and reached up to 24.48 wt% and the Laves in the nonoverlapping zone was more compact than the overlapping zone.
Playing Atari with few neurons
Venue: Auton. Agents Multi Agent Syst.
Year: 2021

We propose a new method for learning compact state representations and policies separately but simultaneously for policy approximation in vision based applications such as Atari games. Approaches based on deep reinforcement learning typically map pixels directly to actions to enable end to end training. Internally, however, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it, two objectives which can be addressed independently. Separating the image processing from the action selection allows for a better understanding of either task individually, as well as potentially finding smaller policy representations which is inherently interesting. Our approach learns state representations using a compact encoder based on two novel algorithms: (i) Increasing Dictionary Vector Quantization builds a dictionary of state representations which grows in size over time, allowing our method to address new observations as they appear in an open ended online learning context; and (ii) Direct Residuals Sparse Coding encodes observations in function of the dictionary, aiming for highest information inclusion by disregarding reconstruction error and maximizing code sparsity. As the dictionary size increases, however, the encoder produces increasingly larger inputs for the neural network; this issue is addressed with a new variant of the Exponential Natural Evolution Strategies algorithm which adapts the dimensionality of its probability distribution along the run. We test our system on a selection of Atari games using tiny neural networks of only 6 to 18 neurons (depending on each game's controls) These are still capable of achieving results that are not much worse, and occasionally superior, to the state of the art in direct policy search which uses two orders of magnitude more neurons.
Multiple image Encryption Based on Wavelet Transform and Improved Double Random Phase Encoding
Year: 2014

A novel multiple image encryption algorithm is proposed based on wavelet transform and double random phase encoding. Using the multi level decomposition properity of wavelet transform,the method combines the low frequency components of plain images into a single image. Then,the combined image is encrypted by double random phase encoding. The lateral superposition way with low frequency portions avoids the crosstalk,increases the system capacity and improves the speed of encryption. The idea of generating random phase masks with chaotic maps increases the sensitivity of keys while the key volume decreases. Finally,numerical experiments show that the proposed method is effective on multiple image encryption,extremely sensitive to keys and robust.
Esculetin, a natural coumarin compound, evokes Ca2+ movement and activation of Ca2+ associated mitochondrial apoptotic pathways that involved cell cycle arrest in ZR 75 1 human breast cancer cells
Venue: Tumor Biology
Year: 2015

AbstractEsculetin (6,7 dihydroxycoumarin) a derivative of coumarin compound, is found in traditional medicinal herbs. It has been shown that esculetin triggers diverse cellular signal transduction pathways leading to regulation of physiology in different models. However, whether esculetin affects Ca2+ homeostasis in breast cancer cells has not been explored. This study examined the underlying mechanism of cytotoxicity induced by esculetin and established the relationship between Ca2+ signaling and cytotoxicity in human breast cancer cells. The results showed that esculetin induced concentration dependent rises in the intracellular Ca2+ concentration [Ca2+]i) in ZR 75 1 (but not in MCF 7 and MDA MB 231) human breast cancer cells. In ZR 75 1 cells, this Ca2+ signal response was reduced by removing extracellular Ca2+ and was inhibited by the store operated Ca2+ channel blocker 2 aminoethoxydiphenyl borate (2 APB) In Ca2+ free medium, pre treatment with the endoplasmic reticulum Ca2+ pump inhibitor thapsigargin (TG) abolished esculetin induced [Ca2+]i rises. Conversely, incubation with esculetin abolished TG induced [Ca2+]i rises. Esculetin induced cytotoxicity that involved apoptosis, as supported by the reduction of mitochondrial membrane potential and the release of cytochrome c and the proteolytic activation of caspase 9/caspase 3, which were partially reversed by pre chelating cytosolic Ca2+ with 1,2 bis(2 aminophenoxy)ethane N,N,N',N' tetraacetic acid acetoxymethyl ester (BAPTA AM) Moreover, esculetin increased the percentage of cells in G2/M phase and regulated the expressions of p53, p21, CDK1, and cyclin B1. Together, in ZR 75 1 cells, esculetin induced [Ca2+]i rises by releasing Ca2+ from the ER and causing Ca2+ influx through 2 APB sensitive store operated Ca2+ entry. Furthermore, esculetin activated Ca2+ associated mitochondrial apoptotic pathways that involved G2/M cell cycle arrest. Graphical abstractThe summary of esculetin evoked [Ca2+]i rises and activated Ca2+ associated mitochondrial apoptotic pathways that involved cell cycle arrest. The natural coumarin derivative esculetin caused Ca2+ influx via 2 APB sensitive store operated Ca2+ entry and induced Ca2+ release from the endoplasmic reticulum. Moreover, esculetin activated the mitochondrial pathway of apoptosis in a Ca2+ associated manner that involved G2/M arrest.
Heterogeneous Catalytic Hydrogenation of Levulinic Acid to g Valerolactone with Formic Acid as Internal Hydrogen Source: Key Issues and Their Effects.
Venue: ChemSusChem
Year: 2020

As one of the most promising biomass based platform molecules, g valerolactone (GVL) can be synthesized from a variety of lignocellulosic feed stocks via different hydrogen supply pathways. Among these transformation routes, hydrogenation of levulinic acid (LA) to GVL using Formic acid (FA) as the internal hydrogen source is regarded as a critical path for the sustainable development of renewable energy systems. Although a large number of studies on the synthesis of GVL have been reported, the FA/LA catalytic system has not been thoroughly interpreted as it should be. Here in, core concerns are focused on key issues and their effects in this FA/LA catalytic system. The catalytic mechanism, together with competitive ad sorption behaviour between FA and LA on heterogeneous catalysts, was presented. Effects of active metal species and catalyst supports on the overall catalytic performance were summarized. Influences of key condition parameters, including the time and temperature, FA/LA molar ratios as well as the aqueous solvent, were discussed. In particular, impacts and improvements of coke deposition and metal leaching, which could greatly affect the catalyst stability, were analysed in detail. Additionally, several feasible suggestions for the enhancement of the catalytic efficiency and stability were also proposed.
Effects of resistance training, detraining, and retraining on strength and functional capacity in elderly
Venue: Aging Clinical and Experimental Research
Year: 2018

BackgroundThe interruption of training (detraining) results in loss of the gains acquired. Partial retention could occur after detraining, and variation in training stimuli may optimize retraining adaptations.AimTo evaluate the effect of a resistance retraining program on strength and functional capacity performance after a detraining period.MethodsTen elderly men and women (63 68 years) completed 12 weeks of training, 16 weeks of detraining, and 8 weeks of retraining. One repetition maximum (1 RM) at 45deg leg press, maximum isometric knee extension torque, rate of torque development (RTD) 30 s sit to stand, timed up and go, and stair ascent and descent tests were assessed.ResultsThe 1 RM increased after training (p 0.01) and remained higher after a detraining period when compared to pre training (p 0.01) Post retraining values were not different from post training period (p 0.05) For RTD and 30 s sit to stand, there was an increase after retraining when compared to pre training values (p 0.05) For timed up and go and stair ascent and descent, reductions were observed between pre training and post training periods (p 0.05) only timed up and go increased after the detraining period (p 0.01).DiscussionAfter 16 weeks of detraining, the maximum strength did not return to baseline levels, and a retraining with explosive strength exercise sessions can recover maximum strength gains, RTD, and functional capacity at the same level obtained after a detraining period.ConclusionsThe inclusion of an explosive strength session in retraining period improves RTD and 30 s sit to stand performance and can accelerate the recovery of strength after a detraining period.
How well do Elo based ratings predict professional tennis matches?
Year: 2019

This paper examines the performance of five different measures for forecasting men's and women's professional tennis matches. We use data derived from every match played at the 2018 and 2019 Wimbledon tennis championships, the 2019 French Open, the 2019 US Open, and the 2020 Australian Open. We look at the betting odds, the official tennis rankings, the standard Elo ratings, surface specific Elo ratings, and weighted composites of these ratings, including and excluding the betting odds. The performance indicators used are prediction accuracy, calibration, model discrimination, Brier score, and expected return. We find that the betting odds perform relatively well across these tournaments, while standard Elo (especially for women's tennis) and surface adjusted Elo (especially for men's tennis) also perform well on a range of indicators. For all but the hard court surfaces, a forecasting model which incorporates the betting odds tends also to perform well on some indicators. We find that the official ranking system proved to be a relatively poor measure of likely performance compared to betting odds and Elo related methods. Our results add weight to the case for a wider use of Elo based approaches within sports forecasting, as well as arguably within the player rankings methodologies.
Survey on eHMI concepts: The effect of text, color, and perspective
Venue: Transportation Research Part F: Traffic Psychology and Behaviour
Year: 2019

The automotive industry has presented a variety of external human machine interfaces (eHMIs) for automated vehicles (AVs) However, there appears to be no consensus on which types of eHMIs are clear to vulnerable road users. Here, we present the results of two large crowdsourcing surveys on this topic. In the first survey, we asked respondents about the clarity of 28 images, videos, and patent drawings of eHMI concepts presented by the automotive industry. Results showed that textual eHMIs were generally regarded as the clearest. Among the non textual eHMIs, a projected zebra crossing was regarded as clear, whereas light based eHMIs were seen as relatively unclear. A considerable proportion of the respondents mistook non textual eHMIs for a sensor. In the second survey, we examined the effect of perspective of the textual message (egocentric from the pedestrian's point of view: 'Walk' 'Don't walk' vs. allocentric: 'Will stop' 'Won't stop' and color (green, red, white) on whether respondents felt safe to cross in front of the AV. The results showed that textual eHMIs were more persuasive than color only eHMIs, which is in line with the results from the first survey. The eHMI that received the highest percentage of 'Yes' responses was the message 'Walk' in green font, which points towards an egocentric perspective taken by the pedestrian. We conclude that textual egocentric eHMIs are regarded as clearest, which poses a dilemma because textual instructions are associated with practical issues of liability, legibility, and technical feasibility.
MSARN: A Deep Neural Network Based on an Adaptive Recalibration Mechanism for Multiscale and Arbitrary Oriented SAR Ship Detection
Venue: IEEE Access
Year: 2019

Ship detection plays an important role in synthetic aperture radar (SAR) image interpretation. However, there are still some difficulties in SAR ship detection. First, ships often have a large aspect ratio and arbitrary directionality in SAR images. Traditional detection algorithms can cause the detection area to be redundant, which makes it difficult to accurately locate the target in complex scenes. Second, ships in ports are often densely arranged, and the effective identification of densely arranged ships is complicated. Finally, ships in SAR images exist at a variety of scales due to the multiresolution imaging modes used and ship shape variations, which pose a considerable challenge for ship detection. To solve the above problems, we propose a multiscale adaptive recalibration network (MSARN) to detect multiscale and arbitrarily oriented ships in complex scenarios. The recalibration of the extracted multiscale features through global information increases the sensitivity of the network to the target angle, thereby increasing the accuracy of positioning. In particular, we designed a pyramid anchor and a loss function to match the rotated target. In addition, we modified the rotation non maximum suppression (RNMS) method to solve the problem of the large overlap ratio of the detection box. The proposed model combines the positioning advantage of rotation detection with the speed advantage of a single stage framework. Experiments show that based on the SAR rotation ship detection (SRSD) data set, the proposed algorithm has a faster detection speed and higher accuracy than some state of the art methods.
An english language learning study with rural chinese children using an augmented reality app
Venue: IDC
Year: 2020

Augmented reality (AR) apps have the potential to support early English learning for children. However, few studies have investigated how children from rural low socio economic status (SES) schools, who learn English as a foreign language (EFL) used and perceived an AR app in language learning. In this paper, we present an exploratory case study of 11 EFL children and four school teachers from a Chinese rural county who used an AR app (called AR PhonoBlocks) for one week. The goal of the app is to support children to learn the alphabetic principle of English. The key features are overlaid dynamic colour cues on 3D physical letters. We present the results including themes related to children's interactional behaviours and motivations, and rural teachers' feedback on the opportunities and concerns around using an AR app in a rural school context. We suggest design implications and future research directions for designing AR apps to support EFL children from low SES schools in early English learning.
Fuzzy Multi Criteria Decision Making Model for Supplier Evaluation and Selection in a Wind Power Plant Project
Venue: Mathematics
Year: 2019

In order to meet ambitious growth targets in the medium term, Vietnam must continue exploiting traditional energy sources. In the longer term, Vietnam has to develop a strategy and roadmap for the development of new energy sources. In these new energy sources, wind energy has emerged as a viable option. Given the geographic conditions of a locality with a long coastline and high winds that are fairly distributed all year, many wind power plants are being built in Vietnam. One of the most important pieces of equipment in a wind power plant is the wind turbine. The wind turbine suppliers' selection is a complex and multicriteria decision making (MCDM) process that can reduce the costs of procuring equipment and aid in receiving products on time. Many studies have applied the MCDM model to various fields of science and engineering. One of the fields that the MCDM approaches have been applied to is the supplier selection problem. Supplier selection is an important issue of the MCDM model. Especially in a renewable energy project, decision makers have to evaluate both natural and society factors. Although some researchers have reviewed the applications of the MCDM model in wind turbine supplier selection, limited work has focused on this problem in a fuzzy environment. Therefore, in this work, the authors propose a fuzzy MCDM model for the wind turbine supplier selection process under fuzzy environment conditions. In the first step, all factors for wind turbine supplier selection are identified by supply chain operations reference (SCOR) metrics and the results from a review of the literature. A fuzzy analytic network process (FANP) model is applied for determining the weight of all the criteria in the second stage, and the technique for order preference by similarity to an ideal solution (TOPSIS) model is used to rank all the potential suppliers in the final stage. As a result, Decision Making Unit 010 (DMU010) becomes an optimal option for the wind turbine supplier selection processes. The contribution of this research is to develop new hybrid fuzzy MCDM approaches for wind turbine supplier selections. Furthermore, this work presents useful guidelines for wind turbines as well as provides a guideline for supplier selection in other industries.