Miracle-dz/UniFAR-FTUs huggingface dataset:
Agridat

R (R Core Team 2013) is an open-source statistics software environment that can be downloaded from the R project website at http://www.r-project.org/ to provide a free resource for modern statistics computing. The basic R download includes a range of core tools but the real strength of R is in the contributed packages that extend and generalize the core language. A recent contributed package that should be of interest to agricultural statisticians and research workers is the ‘agridat’ package (Wright 2013), which provides access to real datasets from a large number of published agricultural research papers. Currently, the package contains more than 140 datasets covering field and horticultural data, uniformity data, animal data, tree data, time series data and a few sets of disease, soil and economics data. The data sets include, for example, the classical Mercer & Hall uniformity trial on wheat (Mercer & Hall 1911) and the classical Yates (1935) split-plot experiment on oats as well as many other data sets both old and new. The ‘agridat’ datasets are formatted as data frames and each dataset has example code which can be input directly into R to provide an example analysis of the formatted dataset. For example, the Mercer & Hall dataset has code that provides graphical displays of the uniformity data, whereas the Yates (1935) oats dataset has code that provides a mixed model analysis of the split-plot oats data. Other datasets include a wide range of statistical methods and provide a wealth of interesting examples. Access to real research data is important for the development and testing of new statistical methods and the ‘agridat’ datasets should be a boon to methodological research in agriculture. Installation of the ‘agridat’ package is fairly straightforward but does require some basic knowledge and experience of the R environment. As R is now such awidely used andwellestablished statistics language, time spent learning about packages such as ‘agridat’ should be time well spent.
Effects of some humidity and IBA hormone dose applications on rooting of M9 apple clonal rootstock Softwood top cuttings

In this study, softwood cuttings were taken from M9 dwarf apple rootstocks in early June. Different indole-3-butyric acid (IBA) concentrations [0 (control), 500, 1500, 2500 and 3500 ppm], different air humidity levels (85 - 90%, 95-100%) and rooting media of perlite effects on rooting capability and root formation of M9 apple rootstock cuttings were examined. All the cuttings remained alive. The highest ratio of cutting callus formation was found in control group (58%) in 95 - 100% humidity level and 2500 ppm IBA hormone dose application in 85 - 90% humidity level. The highest rooting ratio was obtained from control group (46%) in 95 - 100% humidity level; the lowest one was 3500 ppm IBA dose application (17%) in 85 - 90% relative humidity level. The highest rooting surface lenght was found in 1500 ppm hormone dose (0.53 cm) in 95 - 100% and 2500 ppm IBA dose application (0.42 cm) in 85 90% humidity level. With respect to root numbers, the highest value was from 1500 ppm IBA application (1.29 number/cutting) in 95 - 100% relative humidity, and 500 and 2500 ppm IBA doses (1.04 number/cutting) in 85 - 90% relative humidity level. The longest root was obtained from control group (2.03 cm) in 95 - 100% humidity level and 500 ppm IBA hormone dose (1.80 cm) in 85 - 90% humidity level. The shortest root was obtained from 2500 ppm IBA hormone dose application (0.09 cm) in 85-90% humidity level. The highest root branching value was obtained from 1500 ppm hormone dose application (0.88 number/cutting) in 95 - 100% humidity level.
Hygroscopic properties of submicrometer atmospheric aerosol particles measured with H-TDMA instruments in various environments—a review

The hygroscopic properties play a vital role for the direct and indirect effects of aerosols on climate, as well as the health effects of particulate matter (PM) by modifying the deposition pattern of inhaled particles in the humid human respiratory tract. Hygroscopic Tandem Differential Mobility Analyzer (H-TDMA) instruments have been used in field campaigns in various environments globally over the last 25 yr to determine the water uptake on submicrometre particles at subsaturated conditions. These investigations have yielded valuable and comprehensive information regarding the particle hygroscopic properties of the atmospheric aerosol, including state of mixing. These properties determine the equilibrium particle size at ambient relative humidities and have successfully been used to calculate the activation of particles at water vapour supersaturation. This paper summarizes the existing published H-TDMA results on the sizeresolved submicrometre aerosol particle hygroscopic properties obtained from ground-based measurements at multiple marine, rural, urban and free tropospheric measurement sites. The data is classified into groups of hygroscopic growth indicating the external mixture, and providing clues to the sources and processes controlling the aerosol. An evaluation is given on how different chemical and physical properties affect the hygroscopic growth.
Revisiting Supervised Word Embeddings

author Word embeddings are playing a crucial role in a variety of applications. However, most previous works focus on word embeddings which are either non-discriminative or hardly interpretable. In this work, we investigate a novel approach, referred to as SWET , which learns supervised word embeddings using topic models from labeled corpora. SWET inher-its the interpretability of topic models, the discriminativeness of supervised inference from labels. More importantly, SWET enables us to directly exploit a large class of existing un-supervised and supervised topic models to learn supervised word embeddings. Extensive experiments show that SWET outperforms unsupervised approaches by a large margin, and are highly competitive with supervised baselines.
Synthesis and Bioactivity Evaluation of Cinnamic Acid Esters from Oxalis pes-caprace

Synthesis of cinnamic acid esters 4a-c to 7 was achieved starting from appropriately substitute d benzaldehydes. While compound 4a-c to 7 was exhibited potent antioxidative activity in both the NBT and DPPH-radical scavenging models among the sy nthesized cinnamic acid ester derivatives. No ester derivative showed significant 5-Lox, Tyrosine inhibitory and Cytotoxic activities in the present study.
Clinical Toxicology

The aim of this study is to perform molecular docking studies to identify potential binding affinities of the phyto-compounds from compounds claulamines E, clausemarin B, Clausenaline C, Clausenaline E, Murrayanine, vanillic acid, Xanthotoxol for searching of lead molecule for anti-diabetic activity. Computer assisted drug design approach has contributed to successfully disclosure of against diabetic agent. Molecular docking keeps on being a great promise in the field of computer based drug design. The procedure of molecular docking includes investigation of various holding methods of one ligand with targeted receptors protein. We have found that numerous values of Molecular docking score by Schrodinger. Compounds claulamines E, clausemarin B, Clausenaline C, Clausenaline E, Murrayanine, vanillic acid, Xanthotoxol respectively. Among all the compounds Clausenaline E showed the best docking score. So from this investigation we can say that this compound Clausenaline E can be suggested for further experiment for the grater pharmacological effect in diabetic patient.
Puzzle Out the Semantic Web Search

The increase in web popularity has created the demand for systems that help the users ﬁnd relevant information easily. Question Answering systems made it possible to ask questions and retrieve answers using natural language queries, rather than the keyword-based retrieval mechanisms used by current search engines. In this paper we propose a Cooperative Question Answering System that integrates natural language processing, ontologies and information retrieval technologies in a uniﬁed framework. It accepts natural language queries and is able to return a cooperative answer based on semantic web resources. Our system resorts to ontologies not only for reasoning but also to ﬁnd answers and is independent of prior knowledge of the semantic resources by the user. The natural language question is translated into its semantic representation and then answered by consulting the semantics sources of information. The system is able to clarify the problems of ambiguity and helps ﬁnding the path to the correct answer. If there are multiple answers to the question posed, they will be grouped according to their semantic meaning, providing a more cooperative answer to the user.
Developing Valid Measurement Procedures for Speech Intelligibility of Pathological Speech

Speech disorders in general, and dysarthria especially, lead to decreased speech intelligibility. This can have a severe impact on the patients' quality of life because they can lose social contact and eventually become isolated from society. Most of the time, degraded speech intelligibility can be improved through speech therapy. However, monitoring the effectiveness of speech therapy requires clear definition and operationalization of speech intelligibility. Until now, little is known about which deviations in pathological speech most affect intelligibility, and how intelligibility can best be improved. Furthermore, measuring intelligibility is complex, and it is time-consuming as it requires a lot of manual work. Therefore, I will study the following novel aspects in this project: which deviations in pathological speech have the most impact on intelligibility, what are good procedures for measuring intelligibility, and how can the workload in measuring intelligibility be reduced by making use of software tools.
A framework to incorporate aspects of social perception in synthetic voices

In my dissertation, I investigate the social speaker characteristics: warmth and competence. I refer to them as the global aspects of social perception and analyse them in synthetic speech. Speciﬁcally, I hypothesize two things: (a) there are certain vocal cues responsible for evoking these characteristics and (b) it is feasible to incorporate these characteristics in the present day speech generation mechanisms. To validate my hypotheses, I propose a two stage framework: In the ﬁrst stage, I perform extensive subjective evaluations in the form of listening tests to identify which synthetic voices demonstrate characteristics of warmth and competence. I also perform analysis on the voices to identify the vocal cues corresponding to these characteristics. In the second stage of my dissertation, I propose to incorporate the identiﬁed vocal cues into the speech generation mechanism. Employing two target application scenarios - Customer Service and Health care , I propose to show that incorporating social speaker characteristics can not only improve user satisfaction but also user trust.
Discovering Entity Columns of Web Tables Effectively and Efficiently

Compared with traditional relational tables, web tables have no designated key attributes or entity columns, which make them difficult for machines to understand. The effectiveness of existing methods for entity column detection usually depends on the coverage of knowledge base, and efficiency of traversing knowledge base is low. In this paper, we propose a novel framework for discovering entity columns in web tables based on approximate primary functional dependency. We build the table schema dependency graph to reflect semantic dependency relationships between columns of a web table. By calculating the importance of each attribute node in the table schema dependency graph iteratively based on LeaderRank, our method can detect entity columns accurately and efficiently for both single-entity tables and multi-entity tables. The experimental results on real web datasets show that our method significantly outperforms previous work in both effectiveness and efficiency, especially for large tables.
Mining Influential Who-to-Post and When-to-Post Curators on Social Networks

Curators on the social networking sites become prominent and indispensable nowadays. Gradually, they come to be the voice in the business’s online marketing ﬁeld. The problem that how to ﬁnd the most future-inﬂuential curators and plan the best posting time for them, notwithstanding, has been hidden and under-explored as yet. In this study, we initiate to analyze this problem with those two primary concerns from four distinct dimensions. To ﬁnd the most future-inﬂuential curators, we consider this problem from the following two dimensions, Future Inﬂuence Ranking Prediction and Future Inﬂuential Leader Prediction. To plan the best posting time for the curators, similarly, we consider this part with two dimensions below, Accumulated Inﬂuence Post-time Scheduling and Limited Inﬂuence Post-time Scheduling. We aim at predicting the future inﬂuential curator with a series of basic and advanced self-deﬁned features. Based on network embedding, we add learned features to capture the connection between users. To deal with the problem, we implement Learning-to-Rank algorithm and two newly devised ones, self-training algorithm and mutual-training algorithm, which are served to become the solution to the imbalanced data. With the experiments on large-scale Facebook data, we ﬁnd the proposed methods signiﬁ-cantly outperform the conventional prediction settings. The F1 score in predicting the most future inﬂuential curators can be up to 0.875. Also, in the part of planning the best posting time, the result shows in comparison with the overall performance of curators, the limited inﬂuence of the curators in our planned time can be boosted up to three times
A Domain-Specific Non-Factoid Question Answering System based on Terminology Mining and Siamese Neural Network

The non-factoid question answering system (QAS) responds to an input question by fetching an answer from a question answering (QA) database. The existing non-factoid QASs still cannot well adapt to specific professional domains due to the lack of domain knowledge. Aiming at this problem, this paper proposes a domain-specific non-factoid QAS by combining information retrieval technique and deep neural network. First, it extracts professional terms from the domain-specific documents. The professional terms can be used as an important source of domain knowledge. Second, it trains a deep Sia-mese neural network for semantically matching the questions. Finally, it queries and ranks the candidate answers based on the professional terms and the deep Siamese neural network. We conducted experiments based on two real domain-specific QA databases, and the experiment results have demonstrated the effectiveness of the proposed QAS.
EMOTION DETECTION AND ANALYSIS USING CONVOLUTION NEURAL NETWORK

---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Electroencephalogram (EEG) signal processing finds diverse applications including disease identification and monitoring, sleep studies, brain computer interface, biofeedback machines. Emotion detection from EEG signal is a leap frog in the field of medical science and engineering technology as it could help detect the emotion of facially paralyzed patients, infants, human emotional stress management and early detection of diseases. The classification of emotions using novel classification methods such as Convolution Neural Network (CNN) would enable the development of more accurate emotion detection systems. This work implements an efficient emotion detection and analysis software that will work on DEAP dataset, which is a three dimensional recording of EEG and peripheral physiological signals from 32 participants. In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of Electroencephalogram (EEG), an EEG emotional feature learning and classification method using Convolution Neural Network (CNN) was proposed based on temporal features, frequential features, and their combinations of EEG signals in DEAP dataset. The extracted features will be given to the CNN classifier and the accuracy of the result is compared with existing classifiers.
DOCUMENT CLUSTERING

In a world flooded with information, document clustering is an important tool that can help categorize and extract insight from text collections. It works by grouping similar documents, while simultaneously discriminating between groups. In this article, we provide a brief overview of the principal techniques used to cluster documents, and introduce a series of novel deeplearning based methods recently designed for the document clustering task. In our overview, we point the reader to salient works that can provide a deeper understanding of the topics discussed.
Title: An integrative review exploring psycho-social impacts

The purpose of this integrative review was to explore psycho-social impacts and 12 therapeutic interventions for parent caregivers of young people living with Duchenne’s Muscular 13 Dystrophy (DMD). This review utilised Whittemore and Knafl's [1] methodology. Electronic 14 databases were searched for research publications between 2010 and 2020. This included Medline, 15 CINAHL, PsycINFO, ERIC, ERC, AMED. Four central themes emerged: Living with DMD; 16 Knowing and telling; Transitioning; Building resilience. The impact on parents caring for a child 17 with DMD affected all aspects of their lives, changed over time and had identifiable peak stress 18 points. Unmet parental information and support needs left parents struggling in their role. 19 Transition required changes to parenting behaviours and required adaptation and resilience. It is 20 proposed that future investment should focus on anticipating family need, targeting intervention 21 cognisant of predictable stress points and building resilience through social community. Parents 22 may then be better positioned to support their child in looking forward. 23
Johann Wolfgang Goethe-Universität

Recent empirical work shows that a better legal environment leads to lower expected rates of return in an international cross-section of countries. This paper investigates whether differences in firm-specific corporate governance also help to explain expected returns in a cross-section of firms within a single jurisdiction. Constructing a corporate governance rating (CGR) for German firms, we document a positive relationship between the CGR and firm value. In addition, there is strong evidence that expected returns are negatively correlated with the CGR, if dividend yields and price-earnings ratios are used as proxies for the cost of capital. Most results are robust for endogeneity, with causation running from corporate governance practices to firm fundamentals. Finally, an investment strategy that bought high-CGR firms and shorted low-CGR firms would have earned abnormal returns of around 12 percent on an annual basis during the sample period. We rationalize the empirical evidence with lower agency costs and/or the removal of certain governance malfunctions for the high-CGR firms.
A Differential Privacy Topology Scheme for Average Path Length Query

Privacy protection of sensitive information has become an urgent problem to be solved in social networks. Differential privacy is used in many privacy protection methods because it can provide strong protection. Most of existing differential privacy scheme mainly implements the privacy protection on nodes or edges in the network by perturbing the data query results. The privacy protection requirements of multiple types of information cannot be satisfied in these schemes. In order to solve these issues, a differential privacy security mechanism with average path length (APL) query is proposed in this paper, which realize the privacy protection of both edge weights and network vertices. The reasons for choosing this attribute as the query function are analyzed. The global sensitivity of APL query under the need of node privacy protection and edge-weighted privacy protection is proved. Based on previous studies, the concept of edge-weighted neighborhood graph in differential privacy is proposed. The relationship between data availability and privacy control parameters in differential privacy is analyzed through experiments.
Mining and Linking Funding Information from Unstructured and Structured Sources

The scientific research and development market is a $136bn industry in the US alone, with a 5-year growth of 2.3%, as recorded in 2017 [1]. Within this economy, organizations which fund research need to ensure that they are awarding funds to the right research teams and topics so that they can maximize the impact of the associated available funds. As a result, institutions and researchers are required to report on funded research outcomes and acknowledge the funding source and grants. In parallel, funding bodies should be in a position to trace back these acknowledgements and justify the impact and results of their research allocated funds to their stakeholders and the taxpayers alike. Researchers should also be able to have access to such information, which can help them make better educated decisions for the discovery of appropriate funding opportunities for their scientific interests, experience and profile. Institution research leaders use will use this information to benchmark against peer institutions and gain insights for increasing application success rates, This situation creates unique opportunities for the affiliated industry, to coordinate and develop solutions that can serve funding agencies, researchers and research leaders. A fundamental problem that needs to be addressed is, however, the ability to automatically extract the funding information from scientific articles, which can in turn become searchable in bibliographic databases. Furthermore, this information should be also linked to structured knowledge sources of awarded grants made available by the funding agencies. In this paper we define analytically the problem, discuss the challenges and the existing state-of-the-art approaches for the formulation of an efficient solution, and we present a cutting-edge technology that has been developed by Elsevier to address this problem.
Hydroxyapatite synthesised from nanosized calcium carbonate via hydrothermal method

Abstract Hydroxyapatite (HAp) was synthesised using a hydrothermal method. Nanosized CaCO3 was used as the calcium source, while (NH4)2HPO4 was used as the phosphorous source. Well crystallised HAp and a small amount of β-tricalcium phosphate (β-TCP) were obtained after the hydrothermal reaction. Fourier transform infrared spectra show that under conditions of pH 6 and 250°C, the functional groups of OH− and PO43− were stronger than those under pH 10. This suggests that the powder synthesised at pH 6 has more HAp phase than that at pH 10. Scanning and transmission electron microscope images show that HAp exhibits a rod-like shape at pH 6 and 250°C. After hydrothermal reaction at pH 6 and 250°C, most products are HAp with a small amount of β-TCP synthesised as byproduct and some residual CaCO3.
Anomaly Chicken Cell Identification Using Deep Learning Techniques

Chicken cell abnormal identification by manual method that clearly lacks speed and accuracy. However, the success of deep learning techniques from the convolutional neural network (CNN), it may be providing solutions to cell biology laboratory tasks. This paper collected the novel chicken cell microscopic image datasets for training the different kinds of CNN models and optimizers to find promising applications that might be developed. The top model indicates that ResNet34 with Adam optimizer achieved training accuracy of 100%, testing accuracy of 98.14%, and the lower time on the outstanding confusion matrix. In addition, the validation result represented correct identification, guaranteeing by experts. This study shows the potential method to be improved to an application of identification systems in the actual animal and biology laboratories.