nils-herrmann/clean_scirepeval huggingface dataset:
Medicinal Plants Disease Identification Using Canny Edge Detection Algorithm , Histogram Analysis and CBIR

Ayurveda certainly brings substantial revenue to India by foreign exchange through export of ayurvedic medicines. Plants diseases cause major production and economic losses in agricultural and medicinal industry worldwide. Monitoring of health and detection of diseases in plants and trees is critical issue. This paper presents a method for identification of medicinal plants based on some important features extracted from its leaf images. The most significant part of research on plant disease to identify the disease based on CBIR (content based image retrieval) that is mainly concerned with the accurate detection of diseased medicinal plant. This paper presents an approach where the plant is identified based on its leaf features such as color histogram and edge histogram. Canny edge detection is also very useful to find the strong edges of leaf of plants and that is used to draw the edge histogram which is one of the parameter for testing. Edge Histogram is plotted on strong edges after applying canny edge Detection Algorithm. Color Histogram separate the layers to plot the red, green and blue layer histogram to check the intensity of each color pixels in the sample image which is another parameter for testing that image is healthy or
An investigation of the intellectual structure of opinion mining research

Opinion mining has been receiving increasing attention from a broad range of scientific communities since early 2000s. The present study aims to systematically investigate the intellectual structure of opinion mining research.  Using topic search, citation expansion, and patent search, we collected 5,596 bibliographic records of opinion mining research. Then, intellectual landscapes, emerging trends, and recent developments were identified. We also captured domain-level citation trends, subject category assignment, keyword co-occurrence, document co-citation network, and landmark articles.  Our study was guided by scientometric approaches implemented in CiteSpace, a visual analytic system based on networks of co-cited documents. We also employed a dual-map overlay technique to investigate epistemological characteristics of the domain.  We found that the investigation of algorithmic and linguistic aspects of opinion mining has been of the community’s greatest interest to understand, quantify, and apply the sentiment orientation of texts. Recent thematic trends reveal that practical applications of opinion mining such as the prediction of market value and investigation of social aspects of product feedback have received increasing attention from the community.  Opinion mining is fast-growing and still developing, exploring the refinements of related techniques and applications in a variety of domains. We plan to apply the proposed analytics to more diverse domains and comprehensive publication materials to gain more generalized understanding of the true structure of a science.
Incorporating Semantic and Geometric Priors in Deep Pose Regression

Deep learning has enabled recent breakthroughs across a wide spectrum of scene understanding tasks, however, its applicability to camera pose regression has been unfruitful due to the direct formulation that renders it incapable of encoding scene-specific constrains. In this work, we propose the VLocNet++ architecture that overcomes this limitation by simultaneously embedding geometric and semantic knowledge of the world into the pose regression network. We employ a multitask learning approach to exploit the inter-task relationship between learning semantics, regressing 6-DoF global pose and odometry for the mutual benefit of each of these tasks. Furthermore, in order to enforce global consistency during camera pose regression, we propose the novel Geometric Consistency Loss function that leverages the predicted relative motion estimated from odometry to constrict the search space while training. Extensive experiments on the challenging Microsoft 7-Scenes benchmark and our DeepLoc dataset demonstrate that our approach exceeds the state-of-the-art outperforming local feature-based methods while simultaneously performing multiple tasks and exhibiting substantial robustness in challenging scenarios.
The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation

Traditional methods of motion segmentation use powerful geometric constraints to understand motion, but fail to leverage the semantics of high-level image understanding. Modern CNN methods of motion analysis, on the other hand, excel at identifying well-known structures, but may not precisely characterize well-known geometric constraints. In this work, we build a new statistical model of rigid motion flow based on classical perspective projection constraints. We then combine piecewise rigid motions into complex deformable and articulated objects, guided by semantic segmentation from CNNs and a second "object-level" statistical model. This combination of classical geometric knowledge combined with the pattern recognition abilities of CNNs yields excellent performance on a wide range of motion segmentation benchmarks, from complex geometric scenes to camouflaged animals.
Preliminary Design of a Tandem-Wing Tail-Sitter UAV Using Multi-Disciplinary Design Optimisation

The optimisation of a tail-sitter UAV (Unmanned Aerial Vehicle) that uses a stall-tumble manoeuvre to transition from vertical to horizontal flight and a pull-up manoeuvre to regain the vertical is investigated. The tandem wing vehicle is controlled in the hover and vertical flight phases by prop-wash over wing mounted control surfaces. It represents an innovative and potentially simple solution to the dual requirements of VTOL (Vertical Take-off and Landing) and high speed forward flight by obviating the need for complex mechanical systems such as rotor heads or tilt-rotor systems.
Toddlers' persistence in the emerging occupations of functional play and self-feeding.

This descriptive study explores motivation of toddlers who are typically developing to persist with challenging occupations.



The persistence of 33 children, 12 to 19 months of age (M = 15.7 months), in functional play and self-feeding with a utensil was examined through videotape analysis of on-task behaviors.



A modest correlation was demonstrated between the percentages of on-task time in the two conditions (r = .44, p < .01). Although chronological age was not associated with persistence, participants' age-equivalent fine motor scores were correlated with persistence with challenging toys (r = .39, p < .03) but not with self-feeding with a utensil. Having an older sibling was associated with longer periods of functional play, t(32) = 3.02, p < .005, but the amount the parent urged the child to eat with a utensil was not associated with persistence in self-feeding.



The modest association between on-task time for functional play and self-feeding with a utensil reveals that factors other than urge to meet perceptual motor challenges lead to children's persistence. The results reinforce the importance of considering not only challenging activities, but also the experienced meaning that elicits optimal effort and, thus, learning.
Maternal care, gene expression, and the transmission of individual differences in stress reactivity across generations.

Naturally occurring variations in maternal care alter the expression of genes that regulate behavioral and endocrine responses to stress, as well as hippocampal synaptic development. These effects form the basis for the development of stable, individual differences in stress reactivity and certain forms of cognition. Maternal care also influences the maternal behavior of female offspring, an effect that appears to be related to oxytocin receptor gene expression, and which forms the basis for the intergenerational transmission of individual differences in stress reactivity. Patterns of maternal care that increase stress reactivity in offspring are enhanced by stressors imposed on the mother. These findings provide evidence for the importance of parental care as a mediator of the effects of environmental adversity on neural development.
Dynamics of Flexible Multibody Systems Using Virtual Work and Linear Graph Theory

By combining linear graph theory with the principle of virtual work, a dynamic formulation is obtained that extends graph-theoretic modelling methods to the analysis of exible multibody systems. The system is represented by a linear graph, in which nodes represent reference frames on rigid and exible bodies, and edges represent components that connect these frames. By selecting a spanning tree for the graph, the analyst can choose the set of coordinates appearing in the nal system of equations. This set can include absolute, joint, or elastic coordinates, or some combination thereof. If desired, all non-working constraint forces and torques can be automatically eliminated from the dynamic equations by exploiting the properties of virtual work. The formulation has been implemented in a computer program, DynaFlex, that generates the equations of motion in symbolic form. Three examples are presented to demonstrate the application of the formulation, and to validate the symbolic computer implementation.
What is parallelism?

Although parallel and convergent evolution are discussed extensively in technical articles and textbooks, their meaning can be overlapping, imprecise, and contradictory. The meaning of parallel evolution in much of the evolutionary literature grapples with two separate hypotheses in relation to phenotype and genotype, but often these two hypotheses have been inferred from only one hypothesis, and a number of subsidiary but problematic criteria, in relation to the phenotype. However, examples of parallel evolution of genetic traits that underpin or are at least associated with convergent phenotypes are now emerging. Four criteria for distinguishing parallelism from convergence are reviewed. All are found to be incompatible with any single proposition of homoplasy. Therefore, all homoplasy is equivalent to a broad view of convergence. Based on this concept, all phenotypic homoplasy can be described as convergence and all genotypic homoplasy as parallelism, which can be viewed as the equivalent concept of convergence for molecular data. Parallel changes of molecular traits may or may not be associated with convergent phenotypes but if so describe homoplasy at two biological levels-genotype and phenotype. Parallelism is not an alternative to convergence, but rather it entails homoplastic genetics that can be associated with and potentially explain, at the molecular level, how convergent phenotypes evolve.
AUTOSAR for Connected and Autonomous Vehicles: The AUTOSAR Adaptive Platform

New applications like highly automated driving, Car-2-X, software updates over the air, or vehicles as part of the internet of things raise completely new requirements to a software platform for the next generation of ECUs. AUTOSAR as the worldwide leading standardization organization for in-vehicle software bears this challenge and paves the way making vehicles intelligent and adaptive. Based on a set of selected use-cases, identified by the AUTOSAR partners, the challenges and the approach to master requirements for next generation cars are described. The new platform aims to support dynamic deployment of customer applications, to provide an environment for applications that require high-end computing power and to connect deeply embedded and non-AUTOSAR systems in a smooth way while preserving typical features originated in deeply embedded systems like safety, determinism and real-time capabilities. Built around existing standards such as POSIX, the AUTOSAR Adaptive Platform will complement automotive specific functionalities enabling the platform to run in an automotive network.
Document Clustering and Text Summarization

This paper describes a text mining tool that performs two tasks, namely document clustering and text summarization. These tasks have, of course, their corresponding counterpart in “conventional” data mining. However, the textual, unstructured nature of documents makes these two text mining tasks considerably more difficult than their data mining counterparts. In our system document clustering is performed by using the Autoclass data mining algorithm. Our text summarization algorithm is based on computing the value of a TF-ISF (term frequency – inverse sentence frequency) measure for each word, which is an adaptation of the conventional TF-IDF (term frequency – inverse document frequency) measure of information retrieval. Sentences with high values of TF-ISF are selected to produce a summary of the source text. The system has been evaluated on real-world documents, and the results are satisfactory.
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)(2), a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET.
Refined Slanted-Edge Measurement from Practical Camera and Scanner Testing

It has been almost five years since the ISO adopted a standard for measurement of image resolution of digital still cameras using slanted-edge gradient analysis. The method has also been applied to the spatial frequency response and MTF of film and print scanners, and CRT displays. Each of these applications presents challenges to the use of the method. Previously, we have described causes of both bias and variation error in terms of the various signal processing steps involved. This analysis, when combined with observations from practical systems testing, has suggested improvements and interpretation of results. Specifically, refinements in data screening for signal encoding problems, edge feature location and slope estimation, and noise resilience will be addressed.
Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning

Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.
Deep Neural Networks motivated by Partial Differential Equations

Partial differential equations (PDEs) are indispensable for modeling many physical phenomena and also commonly used for solving image processing tasks. In the latter area, PDE-based approaches interpret image data as discretizations of multivariate functions and the output of image processing algorithms as solutions to certain PDEs. Posing image processing problems in the infinite dimensional setting provides powerful tools for their analysis and solution. Over the last few decades, the reinterpretation of classical image processing problems through the PDE lens has been creating multiple celebrated approaches that benefit a vast area of tasks including image segmentation, denoising, registration, and reconstruction. In this paper, we establish a new PDE-interpretation of a class of deep convolutional neural networks (CNN) that are commonly used to learn from speech, image, and video data. Our interpretation includes convolution residual neural networks (ResNet), which are among the most promising approaches for tasks such as image classification having improved the state-of-the-art performance in prestigious benchmark challenges. Despite their recent successes, deep ResNets still face some critical challenges associated with their design, immense computational costs and memory requirements, and lack of understanding of their reasoning. Guided by well-established PDE theory, we derive three new ResNet architectures that fall into two new classes: parabolic and hyperbolic CNNs. We demonstrate how PDE theory can provide new insights and algorithms for deep learning and demonstrate the competitiveness of three new CNN architectures using numerical experiments.
Memory Augmented Policy Optimization for Program Synthesis with Generalization

This paper presents Memory Augmented Policy Optimization (MAPO): a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministic environments with discrete actions. The formulation expresses the expected return objective as a weighted sum of two terms: an expectation over a memory of trajectories with high rewards, and a separate expectation over the trajectories outside the memory. We propose 3 techniques to make an efficient training algorithm for MAPO: (1) distributed sampling from inside and outside memory with an actor-learner architecture; (2) a marginal likelihood constraint over the memory to accelerate training; (3) systematic exploration to discover high reward trajectories. MAPO improves the sample efficiency and robustness of policy gradient, especially on tasks with a sparse reward. We evaluate MAPO on weakly supervised program synthesis from natural language with an emphasis on generalization. On the WIKITABLEQUESTIONS benchmark we improve the state-of-the-art by 2.5%, achieving an accuracy of 46.2%, and on the WIKISQL benchmark, MAPO achieves an accuracy of 74.9% with only weak supervision, outperforming several strong baselines with full supervision. Our code is open sourced at https://github.com/crazydonkey200/neural-symbolic-machines.
High-Efficiency , High Step-Up DC – DC Converters

Many applications call for high step-up dc–dc converters that do not require isolation. Some dc–dc converters can provide high step-up voltage gain, but with the penalty of either an extreme duty ratio or a large amount of circulating energy. DC–DC converters with coupled inductors can provide high voltage gain, but their efficiency is degraded by the losses associated with leakage inductors. Converters with active clamps recycle the leakage energy at the price of increasing topology complexity. A family of high-efficiency, high step-up dc–dc converters with simple topologies is proposed in this paper. The proposed converters, which use diodes and coupled windings instead of active switches to realize functions similar to those of active clamps, perform better than their active-clamp counterparts. High efficiency is achieved because the leakage energy is recycled and the output rectifier reverse-recovery problem is alleviated.
CoT: Cooperative Training for Generative Modeling of Discrete Data

We propose Cooperative Training (CoT) for training generative models that measure a tractable density for discrete data. CoT coordinately trains a generator G and an auxiliary predictive mediator M . The training target of M is to estimate a mixture density of the learned distribution G and the target distribution P , and that of G is to minimize the Jensen-Shannon divergence estimated through M . CoT achieves independent success without the necessity of pre-training via Maximum Likelihood Estimation or involving high-variance algorithms like REINFORCE. This low-variance algorithm is theoretically proved to be unbiased for both generative and predictive tasks. We also theoretically and empirically show the superiority of CoT over most previous algorithms in terms of generative quality and diversity, predictive generalization ability and computational cost.
How AUDI AG Established Big Data Analytics in Its Digital Transformation

Digital transformation often includes establishing big data analytics capabilities and poses considerable challenges for traditional manufacturing organizations, such as car companies. Successfully introducing big data analytics requires substantial organizational transformation and new organizational structures and business processes. Based on the three-stage evolution of big data analytics capabilities at AUDI, the full article provides recommendations for how traditional manufacturing organizations can successfully introduce big data analytics and master the related organizational transformations. Stage I: Advancing. In Stage I, AUDI’s sales and marketing department initiated data analytics projects. Commitment within the organization for data analytics grew slowly, and the strategic importance of the area was increasingly recognized. During this first stage, the IT department played a passive role, responding to the initiators of data analytics projects. The company’s digital innovation hub, however, laid the technology foundation for big data analytics during the Advancing stage. Stage II: Enabling. In Stage II, analytics competencies were built up not only in the digital innovation hub but also in the IT department. The IT department enabled big data analytics through isolated technology activities, sometimes taking on or insourcing tasks previously carried out by external consultancies or the digital innovation hub. Analytics services were developed through a more advanced technology infrastructure as well as analytics methods. Stage III: Leveraging. In the current Stage III, AUDI is leveraging the analytics competencies of the digital innovation hub and the IT department to centrally provide analytics-as-a-service. The IT department is now fully responsible for all technology tasks and is evolving to become a consulting partner for the other big data analytics stakeholders (sales and marketing department and digital innovation hub). In particular, digital services are enabled by leveraging the most valuable data source (i.e., operational car data).