nils-herrmann/clean_scirepeval huggingface dataset:
Generalized anxiety disorder, depressive symptoms and sleep quality during COVID 19 outbreak in China: a web based cross sectional survey
Venue: Psychiatry Research
Year: 2020

China has been severely affected by Coronavirus Disease 2019(COVID 19) since December, 2019. We aimed to assess the mental health burden of Chinese public during the outbreak, and to explore the potential influence factors. Using a web based cross sectional survey, we collected data from 7,236 self selected volunteers assessed with demographic information, COVID 19 related knowledge, generalized anxiety disorder (GAD) depressive symptoms, and sleep quality. The overall prevalence of GAD, depressive symptoms, and sleep quality of the public were 35.1% 20.1% and 18.2% respectively. Young people reported a significantly higher prevalence of GAD and depressive symptoms than older people. Compared with other occupational group, healthcare workers were more likely to have poor sleep quality. Multivariate logistic regression showed that age 35 years) and time spent focusing on the COVID 19 3 hours per day) were associated with GAD, and healthcare workers were at high risk for poor sleep quality. Our study identified a major mental health burden of the public during the COVID 19 outbreak. Young people, people spending too much time thinking about the outbreak, and healthcare workers were at high risk of mental illness. Continuous surveillance of the psychological consequences for outbreaks should become routine as part of preparedness efforts worldwide.
Malware classification using fusion of neural networks
Venue: Defense Commercial Sensing
Year: 2019

The modern day Cyber field continues to be plagued with innumerable forms of malware that are created on a massive scale. The ever changing nature of malware threats combined with the obfuscation techniques used by attackers creates the need for effective methods of malware classification. As of 2018, an average of one million new forms of malware are created world wide each day, which raises the question of how to combat these attacks. While most antiviruses scan the integrity and composition of files in the system, we propose a new approach to Cyber Defense. As a replacement for standard file scans, we advocate the conversion of the malware binary into a grayscale image for classification and visualization. As discovered by previous research, different types of malware families tend to display similar characteristics and binary patterns between the various malware files in each family. Since there are similarities between the various files of malware in each family, the idea arose to augment these groups with synthetic data generated from a Generative Adversarial Network (GAN) The idea of a constant stream of generated malware leads to the hypothesis that by adding synthetic data based on each family to each family the images are generated from will create a higher learning rate from the Deep Convolutional Neural Network (DCNN) Various architectures of the DCNN will be used as assessments that benchmark each architectures' learning rate before and after the augmentation.
Continuous time path planning for multi agents with fuzzy reinforcement learning
Venue: J. Intell. Fuzzy Syst.
Year: 2017

There are a lot of applications of multi agent systems, such as robot navigation, distributed control, data mining, etc. Reinforcement learning (RL) is a popular method used in multi agent path planning. RL algorithm needs an accurate representation of a small and discrete space. In order to plan multi agents in continuous time, this paper approximate the Q values with the fuzzy logic, such that the modified RL can work in continuous state space. The fuzzy reinforcement learning proposed in this paper uses fuzzy Q iteration algorithm and a modified Wolf PH algorithm. The convergence and existence of the algorithm are proven. The continuous time planning algorithm is applied to a cooperative task of two mobile Khepera robots. The experimental results show the effectiveness of the new path planning method for the multi agents in continuous time.
Erarbeitung des Akzeptanzmodells
Year: 2020

Im Folgenden wird das Akzeptanzmodell fur Chatbots als innovative Medientechnologie im Marketing erarbeitet. Zunachst werden dazu theoretische Grundlagen und einschlagige Akzeptanzmodelle diskutiert, bevor bestehende relevante Akzeptanzmodelle diskutiert werden und die zugehorigen Akzeptanz Determinanten zu einem uberprufbaren Modell zusammengefasst werden.
Efficient Implementation of Recurrent Neural Network Transducer in Tensorflow
Venue: 2018 IEEE Spoken Language Technology Workshop (SLT)
Year: 2018

Recurrent neural network transducer (RNN T) has been successfully applied to automatic speech recognition to jointly learn the acoustic and language model components. The RNN T loss and its gradient with respect to the softmax outputs can be computed efficiently using a forward backward algorithm. In this paper, we present an efficient implementation of the RNN T forward backward and Viterbi algorithms using standard matrix operations. This allows us to easily implement the algorithm in TensorFlow by making use of the existing hardware accelerated implementations of these operations. This work is based on a similar technique used in our previous work for computing the connectionist temporal classification and lattice free maximum mutual information losses, where the forward and backward recursions are viewed as a bi directional RNN whose states represent the forward and backward probabilities. Our benchmark results on graphic processing unit (GPU) and tensor processing unit (TPU) show that our implementation can achieve better throughput performance by increasing the batch size to maximize parallel computation. Furthermore, our implementation is about twice as fast on TPU compared to GPU for batch
Relationship between Result of Sentiment Analysis and User Satisfaction The case of Korean Meteorological Administration
Year: 2016

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The Level of People's Obedience to MUI Fatwas (COVID 19, Bank Interest, and Interfaith Marriage)
Year: 2020

Indonesian Ulama Council (MUI) is an organization for Muslim scholars who provide guidance for the Muslim community in the form of fatwas on a particular situation on which the community can rely. In the ongoing situation relating to the COVID 19 pandemic since March 2020, MUI released a fatwa as to how to face the pandemic. This research is aimed to measure the level of people's obedience to the fatwa. To obtain an accurate measurement, this research involves two others MUI's fatwas a fatwa related to bank interest and a fatwa related to interfaith marriage by way of making a comparison. This research uses mixed methods which combined the quantitative method using SPSS and the qualitative method. This research finds that the young unmarried Muslim, mostly under 25 year old university students, have the highest level of obedience to the fatwa related to COVID 19. The fatwa related to interfaith marriage is the most obeyed fatwas, and fatwa related to COVID 19 is the least one among three fatwas in this research. Abstrak Majelis Ulama Indonesia (MUI) merupakan wadah bagi para 'ulama' yang memberikan pembinaan bagi umat Islam dalam bentuk fatwa tentang situasi tertentu yang dapat diandalkan oleh masyarakat. Terkait pandemi COVID 19 sejak Maret 2020, MUI mengeluarkan fatwa tentang cara menghadapi pandemi tersebut. Penelitian ini bertujuan untuk mengukur tingkat kepatuhan masyarakat terhadap fatwa. Untuk mendapatkan pengukuran yang akurat, penelitian ini juga melibatkan dua fatwa MUI lainnya fatwa terkait bunga bank dan fatwa terkait perkawinan beda agama dengan cara membuat perbandingan. Penelitian ini menggunakan metode campuran yang memadukan metode kuantitatif dengan SPSS dan metode kualitatif. Penelitian tersebut menemukan bahwa pemuda Muslim yang belum menikah, kebanyakan berusia di bawah 25 tahun, memiliki tingkat kepatuhan tertinggi terhadap fatwa terkait COVID 19. Fatwa terkait perkawinan beda agama merupakan fatwa yang paling banyak ditaati, dan fatwa terkait COVID 19 merupakan yang paling sedikit di antara tiga fatwa dalam penelitian ini.
Pembentukan Personaliti Remaja
Year: 2008

Mengikut Allport personaliti ialah satu organisasi dinamik berkaitan dengan system psikofizikal dalam diri individu yang akan menentukan penyesuaian tertentu ke atas persekitaran. Organisasi dinamik Dynamik Organisation bermaksud integrasi serta hubungan di antara pelbagai aspek personaliti. Personaliti ini merupakan sesuatu yang berubah dan berkembang dan bukan sesuatu yang statistik.aEUR~Sistem psikofizikalaEUR(tm) ialah tabiat kebiasaan, sikap, nilai kepercayaan, keadaan emosi, sentimen dan motif yang dalam bentuk psikologi tetapi mempunyai asas fizikal di dalam urat saraf, kelenjar dan keadaan tubuh badan am. Sistem ini bukan terhasil daripada baka. Walaupun sistem ini bukan terhasil daripada baka, system ini telah terbentuk dengan cara pembelajaran akibat pengalaman seseorang kanak kanak. Sistem psikofizikal adalah kuasa penggerak yang menentukan penyesuaian jenis apakah yang dibuat oleh seseorang kanak kanak.
Regularizing Meta Learning via Gradient Dropout
Venue: ACCV
Year: 2020

With the growing attention on learning to learn new tasks using only a few examples, meta learning has been widely used in numerous problems such as few shot classification, reinforcement learning, and domain generalization. However, meta learning models are prone to overfitting when there are no sufficient training tasks for the meta learners to generalize. Although existing approaches such as Dropout are widely used to address the overfitting problem, these methods are typically designed for regularizing models of a single task in supervised training. In this paper, we introduce a simple yet effective method to alleviate the risk of overfitting for gradient based meta learning. Specifically, during the gradient based adaptation stage, we randomly drop the gradient in the inner loop optimization of each parameter in deep neural networks, such that the augmented gradients improve generalization to new tasks. We present a general form of the proposed gradient dropout regularization and show that this term can be sampled from either the Bernoulli or Gaussian distribution. To validate the proposed method, we conduct extensive experiments and analysis on numerous computer vision tasks, demonstrating that the gradient dropout regularization mitigates the overfitting problem and improves the performance upon various gradient based meta learning frameworks.
Energy Efficient VM Placement Algorithms for Cloud Data Center
Venue: CloudCom Asia
Year: 2015

Cloud is the computing paradigm which provides computing resource as a service through network. The client can use computing resource in a convenient and on demand way, just like the water and the electricity we use daily. The mapping between virtual machine and physical machine is the key of the VM scheduling problem. Nowadays we advocate low carbon life. It calls for the green cloud computing solutions whether protecting the environment or saving the cost of cloud suppliers. The proposed VM placement algorithm is energy efficient, and considers the multi dimentional resource constrains, such as CPU, memory, network bandwidth, and so on. The experimental results show that the proposed algorithms not only contribute a lot to energy saving, but also try best to meet the quality of service QoS. Therefore, we make significant savings in operating cost and make full use of various resources in the cloud data center. The algorithm has promising prospect in application.
Implementing Family Integrated Care in the NICU: A Parent Education and Support Program
Venue: Advances in neonatal care official journal of the National Association of Neonatal Nurses
Year: 2013

The purpose of this study was to develop, implement, and evaluate a parent education and support program that enhances family integrated care in a Canadian neonatal intensive care unit (NICU) A total of 39 mothers of infants born at 35 or fewer weeks' gestation were enrolled in the pilot program. We examined the development, implementation, and qualitative assessment of the education component of a family integrated care program. We enrolled in groups of 4 or 5, the study mothers agreed to attend daily educational sessions, provide care for their infants for at least 8 hours daily, and participate in medical rounds. The educational sessions were provided by staff and veteran parents to assist parents' development of confidence in providing caregiving skills and assuming the role of a primary caregiver for their infants as they moved closer to discharge. Effectiveness of the program was evaluated through anecdotal feedback and a formal evaluation process at discharge. The results indicated that the mothers were provided with the tools to parent their infants in the NICU, recognize their own strengths, increase their problem solving strategies, and emotionally prepare them to take their infant home. Feedback from the participants provided direction to adapt the program to provide optimal parent support and education. Parental education is a valued and vital component of family integrated care in the NICU.
Probing the Reproducibility of Leaf Growth and Molecular Phenotypes: A Comparison of Three Arabidopsis Accessions Cultivated in Ten Laboratories1[W]
Venue: Plant Physiology
Year: 2010

A major goal of the life sciences is to understand how molecular processes control phenotypes. Because understanding biological systems relies on the work of multiple laboratories, biologists implicitly assume that organisms with the same genotype will display similar phenotypes when grown in comparable conditions. We investigated to what extent this holds true for leaf growth variables and metabolite and transcriptome profiles of three Arabidopsis (Arabidopsis thaliana) genotypes grown in 10 laboratories using a standardized and detailed protocol. A core group of four laboratories generated similar leaf growth phenotypes, demonstrating that standardization is possible. But some laboratories presented significant differences in some leaf growth variables, sometimes changing the genotype ranking. Metabolite profiles derived from the same leaf displayed a strong genotype x environment (laboratory) component. Genotypes could be separated on the basis of their metabolic signature, but only when the analysis was limited to samples derived from one laboratory. Transcriptome data revealed considerable plant to plant variation, but the standardization ensured that interlaboratory variation was not considerably larger than intralaboratory variation. The different impacts of the standardization on phenotypes and molecular profiles could result from differences of temporal scale between processes involved at these organizational levels. Our findings underscore the challenge of describing, monitoring, and precisely controlling environmental conditions but also demonstrate that dedicated efforts can result in reproducible data across multiple laboratories. Finally, our comparative analysis revealed that small variations in growing conditions (light quality principally) and handling of plants can account for significant differences in phenotypes and molecular profiles obtained in independent laboratories.
Robust and ultrafast design of a control system based on optimal sensitivity and optimal complementary sensitivity
Venue: 14th International Conference on Sciences and Techniques of Automatic Control Computer Engineering STA'2013
Year: 2013

A new patented control algorithm achieves an optimized performance of the time response while preserving all the advantages that high gain feedback offers in terms of stability margins and robustness without having to worry about a risk of oscillations or instability. The algorithm is applied to stable invertible systems and is based on the quasi linear principle i.e. the poles of the controller depend on the gain. It is applied to control a hard disk drive and simulation results show its superior performance. The design of optimal sensitivity is fine tuned by the plot of the inverse of the plant on Nichols chart while the design of optimal complementary sensitivity is fine tuned on the Nichols chart. This paper shows that the addition of phase networks in the intermediate frequency range further improves the time response of the system as predicted in
Laser welded stainless steel I sections: residual stress measurements and column buckling tests
Year: 2016

Laser welding is a high precision fabrication process suitable for joining a wide range of steels and stainless steels. Laser welded structural stainless steel members, for which there are currently little experimental data owing to their recent introduction to the construction industry, are the focus of the present study. To address the lack of test data and to investigate their structural response, a total of 9 stub column tests and 22 flexural buckling tests (14 buckling about the minor axis and 8 about the major axis) have been performed on laser welded austenitic stainless steel I section members. Complementary tensile coupon tests, initial geometric imperfection measurements, and residual stress measurements have also been carried out and are reported herein. Based on the results obtained, a representative residual stress pattern is proposed, the design provisions of Eurocode 3 Part 1.4 and the continuous strength method are assessed, and column buckling curves for laser welded stainless steel I section members are recommended.
To Group or Not to Group? Evidence from Mutual Fund Databases
Year: 2015

Despite the overwhelming trend in mutual funds towards team management, empirical studies find no performance benefits for this phenomenon. We show it is caused by large discrepancies in reported managerial structures in CRSP and Morningstar Principia datasets versus SEC records, resulting in up to 50 bps underestimation of the team impact on fund returns. Using more accurate Morningstar Direct data, we find that team managed funds outperform single managed funds across various performance metrics. The relation between team size and fund performance is nonlinear. Also, team managed funds take no more risk than single managed funds. Overall, team management benefits the fund industry performance.
1 A Survey on Compiler Autotuning using Machine Learning
Year: 2018

Since the mid 1990s, researchers have been trying to use machine learning based approaches to solve a number of di erent compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase ordering (choosing the order of applying optimizations) The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classi es the recent advances in using machine learning for the compiler optimization eld, particularly on the two major problems of (1) selecting the best optimizations, and (2) the phase ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the ne grain classi cation among di erent approaches and nally, the in uential papers of the eld.
Xie Qing PCT He NT porBNPLian He Jian Ce Dui Hu Xi Kun Nan Yuan Yin De Jian Bie Zhen Duan Jie Zhi Tan Tao
Year: 2014

Mu De :Tan Tao Xie Qing Jiang Gai Su Yuan (PCT)He NDuan BXing Nao Na Tai (NT proBNP)De Lian He Jian Ce Dui Fei Yuan Xing He Xin Yuan Xing Hu Xi Kun Nan De Jian Bie Zhen Duan Jie Zhi Fang Fa Xuan Ze Lin Chuang Shang Ming Que Zhen Duan Wei Fei Yuan Xing Hu Xi Kun Nan De Huan Zhe 45Li Zuo Wei AZu ,Xuan Ze Lin Chuang Shang Ming Que Zhen Duan Wei Xin Yuan Xing Hu Xi Kun Nan De Huan Zhe 45Li Zuo Wei BZu Tong Shi Jian Ce Liang Zu Xie Qing Zhong PCTHe NT proBNPDe Shui Ping Bing ,Jin Xing Tong Ji Xue Fen Xi Jie Guo AZu Xie Qing Zhong PCTShui Ping Gao Yu BZu ,Chai Yi You Tong Ji Xue Yi Yi (P<0.01);BZu Xie Qing Zhong NT proBNPShui Ping Gao Yu AZu ,Chai Yi You Tong Ji Xue Yi Yi (P<0.01) Jie Lun Jian Ce PCTHe NT proBNPShui Ping ,Dui Jian Bie Xin Yuan Xing He Fei Yuan Xing Hu Xi Kun Nan You Yi Ding De Lin Chuang Ying Yong Jie Zhi
Characterisations of low phosphorus electroless Ni and composite electroless Ni P SiC coatings on A356 aluminium alloy
Year: 2018

A low phosphorus electroless nickel coating of Ni 2.5 wt %P alloy and a composite coating of Ni 5 wt %P SiC were prepared on A356 aluminium alloy substrates using two types of electroless bath solutions, an alkaline bath for low phosphorus and acidic for composite plating. The coatings morphologies have been characterised using optical and scanning electron microscopy. In addition, X ray diffraction, microhardness, reciprocating wear testing and adhesion tests have been conducted to characterise structure and mechanical properties of the resulting coatings. The results obtained revealed that a crack free and homogeneous coating could be produced using an optimum bath formulation. The maximum thickness of the composite coatings was 50 um, the thickness of coatings tested. The composite coating was more resistant to wear in comparison to the low phosphorus nickel one, but had lower adhesion.