nils-herrmann/clean_scirepeval huggingface dataset:
Love and Fortune Balance in Marriage view of marriage in Pride and Prejudice
Year: 2017

Ignored the eighteenth century or viewed it as a period of unenlightenment. According to recent studies, the changing role and increasing importance of women was one of that period's vital events. Without thinking themselves as feminists in the current scene or developing a political program, indeed, while often disguising their purposes or apologizing for their behavior, English women of the eighteenth century made concern about the "woman question" An active issue, especially with respect to a woman's right to choose her husband and their right to educated that is her right to knowledge. Jane Austen was among those authors. Little is directly known of Jane Austen's acquaintance with the developing female consciousness in the eighteenth century. That she examines the "drama of women" in her novels is a judgment based on the likelihood that she was sensitive to her social and literary environment, on the similarity between her views and those of "feminist" writers of the 1790s, and, above all, on the evidence of the novels themselves. Jane Austen was born 16 December 1775, the seventh of eight children in International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)
Bin Picking based on Harmonic Shape Contexts and Graph Based Matching
Venue: 18th International Conference on Pattern Recognition (ICPR'06)
Year: 2006

In this work we address the general bin picking problem where 3D data is available. We apply harmonic shape contexts (HSC) features since these are invariant to translation, scale, and 3D rotation. Each object is divided into a number of sub models each represented by a number of HSC features. These are compared with HSC features extracted in the current data using a graph based scheme. Results show that the approach is somewhat sensitive to noise, but works in presence of occlusion
A Local and Global Discriminative Framework and Optimization for Balanced Clustering
Venue: IEEE Transactions on Neural Networks and Learning Systems
Year: 2019

For many specific applications in data mining and machine learning, we face explicit or latent size constraint for each cluster that leads to the "balanced clustering" problem. Many existing clustering algorithms perform well in partitioning but fail in producing balanced clusters and preserving the naturally balanced structure of some data. In this paper, we propose a novel balanced clustering framework that flexibly utilizes local and global information of data. First, we propose the global balanced clustering (GBC) in which a global discriminative partitioning model is combined with the minimization of the distribution entropy of data. Then, we show that the proposed GBC can be further used to globally regularize some widely used local clustering models, so as to transform them into balanced clustering that simultaneously capture local and global data. We apply our global balanced regularization to spectral clustering (SC) and local learning (LL) based clustering, respectively, and propose another two novel balanced clustering models: the local and global balanced SC (LGB SC) and LGB LL. Finding the optimal balanced partition is nondeterministic polynomial time (NP) hard in general. We adopt the method of augmented Lagrange multipliers to help optimize our model. Comprehensive experiments on several real world benchmarks demonstrate the advantage of our framework to yield balanced clusters while preserving good clustering quality. Our proposed LGB SC and LGB LL also outperform SC and LL as well as other classical clustering methods.
Applicability of regression equation using widths of mandibular permanent first molars and incisors as a predictor of widths of mandibular canines and premolars in contemporary Indian population.
Venue: Journal of the Indian Society of Pedodontics and Preventive Dentistry
Year: 2013

Predicting the size of unerupted teeth during the mixed dentition period is a critical factor in managing the developing occlusion. Different studies found that the combined width of only the four mandibular permanent incisors is not a good predictor of the sum of unerupted mandibular permanent canines and premolars (SPCP) In 2007, Melgaco et al. developed a new method for SPCP by measuring the sum of the mandibular first permanent molars and four mandibular permanent incisors (SMI) AIM It was aimed to evaluate the accuracy of this new method in comparison with Moyers' mixed dentition analysis table in contemporary Indian population.  Sixty boys and 60 girls from Gandhinagar district (age ranged from 12 to 14 years) were included.  The mesiodistal crown widths of all fully erupted teeth were measured with digital vernier callipers and the odontometric values obtained were then subjected to statistical and linear regression analysis.  Student's unpaired t test gave statistically significant difference between the original values of teeth and the values obtained by Melgaco's prediction equation as well as Moyers' mixed dentition analysis table (P 0.001) High values of correlation (r 0.77) and determination coefficients (r2 0.59) were found while considering Melgaco's method. Also, no statistically significant difference was found between the tooth sizes of males and females.  From this study, it can be evaluated that Melgaco's method gives better prediction and a simplified equation Y 0.925X can be suggested for the present population.
PENGARUH MODEL PEMBELAJARAN MIND MAPPING TERHADAP KEMAMPUAN BERBICARA MAHASISWA PADA MATA KULIAH PUBLIC SPEAKING
Year: 2018

This study aims to describe the effect of Mind Mapping Learning Model on students' ability in writing in Public Speaking courses. The subjects of the study were all three semester students of English education which amounted to four classes of 60 students. This research uses questionnaires data which is distributed to 60 English Semester Three Students at UMSU. The data analysis used is descriptive analysis which include mean, median, standard error and standard deviation, and t test to know the influence of Mind Mapping Learning Model to students ability in writing in Public Speaking course.  Mind Mapping, Speech Capability, public speaking course.
Off line handwritten signature verification using compositional synthetic generation of signatures and Siamese Neural Networks
Venue: Neurocomputing
Year: 2020

In this work, we propose the use of Siamese Neural Networks to help solve the off line handwritten signature verification problem with random forgeries in a writer independent context. Our proposed solution can be used on new signers without the need for any additional training. Also, we have analyzed three types of synthetic data to increase the amount of samples and the variability needed for training deep neural networks: augmented data samples from GAVAB dataset, a proposal of compositional synthetic signature generation from shape primitives and the GPDSSynthetic dataset. The first two approaches are "on demand" generators and they can be used during the training stage to produce a potentially infinite number of synthetic signatures. In our approach, we initially trained Siamese Neural Networks using signatures from GAVAB dataset and different combinations of synthetic data. The best verification results were obtained when combining original and synthetic signatures for training. Additionally, we tested our approach on the GPSSynthetic, MCYT, SigComp11 and CEDAR datasets demonstrating the generalization capabilities of our proposal.
Parameter Hub: High Performance Parameter Servers for Efficient Distributed Deep Neural Network Training
Venue: ArXiv
Year: 2018

Most work in the deep learning systems community has focused on faster inference, but arriving at a trained model requires lengthy experiments. Accelerating training lets developers iterate faster and come up with better models. DNN training is often seen as a compute bound problem, best done in a single large compute node with many GPUs. As DNNs get bigger, training requires going distributed. Distributed deep neural network (DDNN) training constitutes an important workload on the cloud. Larger DNN models and faster compute engines shift training performance bottleneck from computation to communication. Our experiments show existing DNN training frameworks do not scale in a typical cloud environment due to insufficient bandwidth and inefficient parameter server software stacks. We propose PHub, a high performance parameter server (PS) software design that provides an optimized network stack and a streamlined gradient processing pipeline to benefit common PS setups, and PBox, a balanced, scalable central PS hardware that fully utilizes PHub capabilities. We show that in a typical cloud environment, PHub can achieve up to 3.8x speedup over state of theart designs when training ImageNet. We discuss future directions of integrating PHub with programmable switches for in network aggregation during training, leveraging the datacenter network topology to reduce bandwidth usage and localize data movement.
A comparative evaluation of outlier detection algorithms: Experiments and analyses
Venue: Pattern Recognit.
Year: 2018

We survey unsupervised machine learning algorithms in the context of outlier detection. This task challenges state of the art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning. The selected methods are benchmarked on publicly available datasets and novel industrial datasets. Each method is then submitted to extensive scalability, memory consumption and robustness tests in order to build a full overview of the algorithms' characteristics.
Big data and big techs: understanding the value of information in platform capitalism
Year: 2020

One of the major challenges that result from the digital transformation occurring in our societies bears on its impact on the organization and regulation of the economy. This leads to a dramatic change to the economic institutions of capitalism into what could be defined as platform capitalism that rests on a fundamental dilemma between 'decentralization' on the one side and 'concentration' on the other. This is the main puzzle that the emergence of a big data driven economy is actually offering to law and economics scholars and to policy makers. This paper introduces to some of the major aspects of this dilemma.
Internet research in psychology.
Venue: Annual review of psychology
Year: 2015

Today the Internet plays a role in the lives of nearly 40% of the world's population, and it is becoming increasingly entwined in daily life. This growing presence is transforming psychological science in terms of the topics studied and the methods used. We provide an overview of the literature, considering three broad domains of research: translational (implementing traditional methods online; e.g. surveys) phenomenological (topics spawned or mediated by the Internet; e.g. cyberbullying) and novel (new ways to study existing topics; e.g. rumors) We discuss issues (e.g. sampling, ethics) that arise when doing research online and point to emerging opportunities (e.g. smartphone sensing) Psychological research on the Internet comes with new challenges, but the opportunities far outweigh the costs. By integrating the Internet, psychological research has the ability to reach large, diverse samples and collect data on actual behaviors, which will ultimately increase the impact of psychological research on society.
The effector AvrRxo1 phosphorylates NAD in planta
Venue: PLoS pathogens
Year: 2017

Gram negative bacterial pathogens of plants and animals employ type III secreted effectors to suppress innate immunity. Most characterized effectors work through modification of host proteins or transcriptional regulators, although a few are known to modify small molecule targets. The Xanthomonas type III secreted avirulence factor AvrRxo1 is a structural homolog of the zeta toxin family of sugar nucleotide kinases that suppresses bacterial growth. AvrRxo1 was recently reported to phosphorylate the central metabolite and signaling molecule NAD in vitro, suggesting that the effector might enhance bacterial virulence on plants through manipulation of primary metabolic pathways. In this study, we determine that AvrRxo1 phosphorylates NAD in planta, and that its kinase catalytic sites are necessary for its toxic and resistance triggering phenotypes. A global metabolomics approach was used to independently identify 3' NADP as the sole detectable product of AvrRxo1 expression in yeast and bacteria, and NAD kinase activity was confirmed in vitro. 3' NADP accumulated upon transient expression of AvrRxo1 in Nicotiana benthamiana and in rice leaves infected with avrRxo1 expressing strains of X. oryzae. Mutation of the catalytic aspartic acid residue D193 abolished AvrRxo1 kinase activity and several phenotypes of AvrRxo1, including toxicity in yeast, bacteria, and plants, suppression of the flg22 triggered ROS burst, and ability to trigger an R gene mediated hypersensitive response. A mutation in the Walker A ATP binding motif abolished the toxicity of AvrRxo1, but did not abolish the 3' NADP production, virulence enhancement, ROS suppression, or HR triggering phenotypes of AvrRxo1. These results demonstrate that a type III effector targets the central metabolite and redox carrier NAD in planta, and that this catalytic activity is required for toxicity and suppression of the ROS burst.
Pinyin Invented Spelling in Mandarin Chinese Speaking Children With and Without Reading Difficulties
Venue: Journal of learning disabilities
Year: 2015

This study examined analytical pinyin (a phonological coding system for teaching pronunciation and lexical tones of Chinese characters) skills in 54 Mandarin speaking fourth graders by using an invented spelling instrument that tapped into syllable awareness, phoneme awareness, lexical tones, and tone sandhi in Chinese. Pinyin invented spelling was significantly correlated with Chinese character recognition and Chinese phonological awareness (i.e. syllable deletion and phoneme deletion) In comparison to good and average readers, poor readers performed significantly worse on the invented spelling task, and a difference was also found between average and good readers. To differentiate readers at different levels, the pinyin invented spelling task, which examined both segmental and suprasegmental elements, was superior to the typical phonological awareness task, which examined segments only. Within this new task, items involving tone sandhi (Chinese language changes in which the tones of words alter according to predetermined rules) were more difficult to manipulate than were those without tone sandhi. The findings suggest that this newly developed task may be optimal for tapping unique phonological and linguistic features in reading of Chinese and examining particular tonal difficulties in struggling Chinese readers. In addition, the results suggest that phonics manipulations within tasks of phonological and tonal awareness can alter their difficulty levels.
What is epistemic value in free energy models of learning and acting? A bounded rationality perspective
Venue: Cognitive neuroscience
Year: 2015

Free energy models of learning and acting do not only care about utility or extrinsic value, but also about intrinsic value, that is, the information value stemming from probability distributions that represent beliefs or strategies. While these intrinsic values can be interpreted as epistemic values or exploration bonuses under certain conditions, the framework of bounded rationality offers a complementary interpretation in terms of information processing costs that we discuss here.
Isolasi, Karakterisasi, dan Uji Aktivitas Antioksidan Senyawa Turunan Kuersetin dari Kulit Batang Melicope quercifolia
Year: 2020

Two quercetin derivatives, pachypodol (1) and ayanin (2) were isolated from the stem bark of Melicope quercifolia (B.S. William) Ridl. Their structures were determined based on spectroscopic data such as UV, IR, MS and NMR. Compounds 1 2 were evaluated for their antioxidant against DPPH radical showing their IC50 were 254 10 and 38.55 0.02 ug/mL, respectively.
Correlation of Blood Biomarkers and Biomarker Panels with Traumatic Findings on Computed Tomography after Traumatic Brain Injury.
Venue: Journal of neurotrauma
Year: 2019

The aim of the study was to examine the ability of eight protein biomarkers and their combinations in discriminating computed tomography (CT) negative and CT positive patients with traumatic brain injury (TBI) utilizing highly sensitive immunoassays in a well characterized cohort. Blood samples were obtained from 160 patients with acute TBI within 24 h of admission. Levels of b amyloid isoforms 1 40 (Ab40) and 1 42 (Ab42) glial fibrillary acidic protein (GFAP) heart fatty acid binding protein (H FABP) interleukin 10 (IL 10) neurofilament light (NF L) S100 calcium binding protein B (S100B) and tau were measured. Patients were divided into CT negative (n 65) and CT positive (n 95) and analyses were conducted separately for TBIs of all severities (Glasgow Coma Scale [GCS] score 3 15) and mild TBIs (mTBIs; GCS 13 15) NF L, GFAP, and tau were the best in discriminating CT negative and CT positive patients, both in patients with mTBI and with all severities. In patients with all severities, area under the curve of the receiver operating characteristic (AUC) was 0.822, 0.817, and 0.781 for GFAP, NF L, and tau, respectively. In patients with mTBI, AUC was 0.720, 0.689, and 0.676, for GFAP, tau, and NF L, respectively. The best panel of three biomarkers for discriminating CT negative and CT positive patients in the group of all severities was a combination of GFAP+H FABP+IL 10, with a sensitivity of 100% and specificity of 38.5% In patients with mTBI, the best panel of three biomarkers was H FABP+S100B+tau, with a sensitivity of 100% and specificity of 46.4% Panels of biomarkers outperform individual biomarkers in separating CT negative and CT positive patients. Panels consisted mainly of different biomarkers than those that performed best as an individual biomarker.
Implementation of an Automated Irrigation System for Agriculture Monitoring using IoT Communication
Venue: 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC)
Year: 2019

In the recent existence, one of the best and familiar technologies scaling innovative heights and making a standard scale is the Internet of Things (IoT) It is definitely the opportunity of digital communication that has altered things of authentic humanity into smarter electronic devices. In India, regrettably, farmers still use conventional techniques of agricultural supervision leading to inputs wastages and near to the ground production due imprecise types and amount of water functional to the field which depends upon soil investigation and plant capitulate. Irrigation processes are as old as humans because agriculture is the most advanced occupation of civilian humanity and need to change irrigation technology. The main target of this paper is to overcome the previously mentioned tribulations by planning a automated water system structure grounded on the idea of IoT and to provide a smart and sustainable solution to a farmer for maintaining their crop health besides yield. We have considered the grain crop that is rice because water is essential for the growth and development of rice plants. Development of the concept of IoT with sensor technology and the solar system is an innovative and future trend in the agriculture filed. Watering large areas of plants is a difficult task and needs an automatic system to reduce human effort. In order to overcome this problem, many irrigation planning methods have been developed based on soil, crop and weather monitoring. Irrigation planning depends on how much water is used during irrigation. The water system control is the way toward altering the capability of Hydrogen (pH) temperature and moisture of the dirt soil. Smart and automated irrigation is a current farming procedure that has been broadly cultivated in created nations to assemble the difficulties of expanding demand of food supply. In this work, we use the concept of a dashboard, it operates via http protocol and by using this concept we can turn on/off water pump of farmland.
The effects of supplementing an organic acid blend and/or microbial phytase to a corn soybean based diet fed to broiler chickens
Year: 2011

This experiment was designed to investigate the effects of supplementation with phytase, either alone or in combination with an organic acid blend on growth performance, some internal organs measurements and tibia development, intestinal and cecal microbiology. One day old male Ross 308 strain broiler chickens (n=140) were allocated to 5 dietary treatments in a randomized complete block design. The treatments were as follows: (1) Positive control (PC) (2) Negative control (NC) that was the same as diet PC except that available phosphorus was reduced by 0.25 percentage units; (3) NC plus microbial phytase (MP) (NC MP; 500 FTU/kg) (4) NC plus organic acid blend (OA) (NC OA) and (5) NC MP OA. Body weight gain (BWG) was not improved by supplementation of OA to the NC diet. In contrast, BWG was increased by MP, and MP+OA interaction (P<0.001) compared to NC group. Tibia ash content was lowest (P<0.05) for birds fed the all NC diets compared to the PC and NC+P diet. The addition of phytase slightly increased tibia phosphorus according to NC+OA and NC+P+OA diets (P<0.05) NC diet resulted in increased weight of gizzard, heart, liver and bursa of Fabricious and length of duodenum, jejunum and ileum. In ileal digesta, lactic acid bacteria (LAB) counts were significantly increased for birds fed OA, whereas Escherichia coli were significantly decreased compared to PC group (P<0.001)
Enterprise Ontology Driven Development
Venue: EOMAS@CAiSE
Year: 2018

Most of the current techniques and approaches for user requirements specification have problems with capturing the appropriate context for development of enterprise information systems. Primarily, they are designed to capture the functional aspects of software rather than its relevancy to an enterprise. Transactions defined in the DEMO (Design Engineering Methodology for Organizations) represent business activities in their existential essence without implementation details. Therefore, they are great candidates to be utilized for the initial development phase of enterprise information systems. The paper exemplifies how to specify software specification the using the DEMO transaction pattern and BDD (Behaviour Driven Development) technique. This proposal resulted from a significant lack of direct utilization of ontology for enterprise information systems development. The major part of the paper gives a step by step explanation of how to integrate DEMO transaction patterns into initial BDD scenarios for the development of enterprise information systems. Such created scenarios provide a perfect guideline in the initial phase of information system development for enterprises. The created scenarios were verified using the domain specific language Gherkin and BDD framework Behat.