nils-herrmann/clean_scirepeval huggingface dataset:
Joint Dictionary Learning for Example-based Image Super-resolution

In this paper, we propose a new joint dictionary learning method for example-based image super-resolution (SR), using sparse representation. The low-resolution (LR) dictionary is trained from a set of LR sample image patches. Using the sparse representation coefficients of these LR patches over the LR dictionary, the high-resolution (HR) dictionary is trained by minimizing the reconstruction error of HR sample patches. The error criterion used here is the mean square error. In this way we guarantee that the HR patches have the same sparse representation over HR dictionary as the LR patches over the LR dictionary, and at the same time, these sparse representations can well reconstruct the HR patches. Simulation results show the effectiveness of our method compared to the state-of-art SR algorithms.
Context Encoding for Semantic Segmentation

Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. Our approach has achieved new state-of-the-art results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single model achieves a final score of 0.5567 on ADE20K test set, which surpasses the winning entry of COCO-Place Challenge 2017. In addition, we also explore how the Context Encoding Module can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset. Our 14 layer network has achieved an error rate of 3.45%, which is comparable with state-of-the-art approaches with over 10Ã— more layers. The source code for the complete system are publicly available1.
KillingFusion: Non-rigid 3D Reconstruction without Correspondences

We introduce a geometry-driven approach for real-time 3D reconstruction of deforming surfaces from a single RGB-D stream without any templates or shape priors. To this end, we tackle the problem of non-rigid registration by level set evolution without explicit correspondence search. Given a pair of signed distance fields (SDFs) representing the shapes of interest, we estimate a dense deformation field that aligns them. It is defined as a displacement vector field of the same resolution as the SDFs and is determined iteratively via variational minimization. To ensure it generates plausible shapes, we propose a novel regularizer that imposes local rigidity by requiring the deformation to be a smooth and approximately Killing vector field, i.e. generating nearly isometric motions. Moreover, we enforce that the level set property of unity gradient magnitude is preserved over iterations. As a result, KillingFusion reliably reconstructs objects that are undergoing topological changes and fast inter-frame motion. In addition to incrementally building a model from scratch, our system can also deform complete surfaces. We demonstrate these capabilities on several public datasets and introduce our own sequences that permit both qualitative and quantitative comparison to related approaches.
The role of the scapula in athletic shoulder function.

The exact role and the function of the scapula are misunderstood in many clinical situations. This lack of awareness often translates into incomplete evaluation and diagnosis of shoulder problems. In addition, scapular rehabilitation is often ignored. Recent research, however, has demonstrated a pivotal role for the scapula in shoulder function, shoulder injury, and shoulder rehabilitation. This knowledge will help the physician to provide more comprehensive care for the athlete. This "Current Concepts" review will address the anatomy of the scapula, the roles that the scapula plays in overhead throwing and serving activities, the normal biomechanics of the scapula, abnormal biomechanics and physiology of the scapula, how the scapula may function in injuries that occur around the shoulder, and treatment and rehabilitation of scapular problems.
EMPLOYEE PERFORMANCE APPRAISAL SYSTEM USING FUZZY LOGIC

Evaluation of employee performance is an important element in enhancing the quality of the work and improves employees’ motivation to perform well. It also presents a basis for upgrading and enhancing of an organization. Periodical employees’ performance evaluation in an organization assists management to recognize its strengths and weaknesses. This paper presents a design and implementation of a performance appraisal system using the fuzzy logic. In addition to the normal process of performance evaluation modules, the system contains step by step inference engine processes. These processes demonstrate several calculation details in relations composition and aggregation methods such as min operator, algebraic product, sup-min and sup-product. The system has foundation to add-on analysis module to analyze and report the final result using various similarity measures. MS Access database was used to maintain the data, build the inference logic and develop all setting user interfaces.
Deep Feature Learning for Graphs

This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, DeepGL begins by deriving a set of base features (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher-order. Contrary to previous work, DeepGL learns relational functions (each representing a feature) that generalize across-networks and therefore useful for graph-based transfer learning tasks. Moreover, DeepGL naturally supports attributed graphs, learns interpretable graph representations, and is space-efficient (by learning sparse feature vectors). In addition, DeepGL is expressive, flexible with many interchangeable components, efficient with a time complexity of O(|E|), and scalable for large networks via an efficient parallel implementation. Compared with the state-of-the-art method, DeepGL is (1) effective for across-network transfer learning tasks and attributed graph representation learning, (2) space-efficient requiring up to 6× less memory, (3) fast with up to 182× speedup in runtime performance, and (4) accurate with an average improvement of 20% or more on many learning tasks.
Generative Adversarial Network based on Resnet for Conditional Image Restoration

The GANs promote an adversarive game to approximate complex and jointed example probability. The networks driven by noise generate fake examples to approximate realistic data distributions. Later the conditional GAN merges prior-conditions as input in order to transfer attribute vectors to the corresponding data. However, the CGAN is not designed to deal with the high dimension conditions since indirect guide of the learning is inefficiency. In this paper, we proposed a network ResGAN to generate fine images in terms of extremely degenerated images. The coarse images aligned to attributes are embedded as the generator inputs and classifier labels. In generative network, a straight path similar to the Resnet is cohered to directly transfer the coarse images to the higher layers. And adversarial training is circularly implemented to prevent degeneration of the generated images. Experimental results of applying the ResGAN to datasets MNIST, CIFAR10/100 and CELEBA show its higher accuracy to the state-of-art GANs.
Effect of exercise on depression severity in older people: systematic review and meta-analysis of randomised controlled trials.

The prevalence of depression in older people is high, treatment is inadequate, it creates a substantial burden and is a public health priority for which exercise has been proposed as a therapeutic strategy.



To estimate the effect of exercise on depressive symptoms among older people, and assess whether treatment effect varies depending on the depression criteria used to determine participant eligibility.



Systematic review and meta-analysis of randomised controlled trials of exercise for depression in older people.



Nine trials met the inclusion criteria and seven were meta-analysed. Exercise was associated with significantly lower depression severity (standardised mean difference (SMD) = -0.34, 95% CI -0.52 to -0.17), irrespective of whether participant eligibility was determined by clinical diagnosis (SMD = -0.38, 95% CI -0.67 to -0.10) or symptom checklist (SMD = -0.34, 95% CI -0.62 to -0.06). Results remained significant in sensitivity analyses.



Our findings suggest that, for older people who present with clinically meaningful symptoms of depression, prescribing structured exercise tailored to individual ability will reduce depression severity.
WikiRank: Improving Keyphrase Extraction Based on Background Knowledge

Keyphrase is an efficient representation of the main idea of documents. While background knowledge can provide valuable information about documents, they are rarely incorporated in keyphrase extraction methods. In this paper, we propose WikiRank, an unsupervised method for keyphrase extraction based on the background knowledge from Wikipedia. Firstly, we construct a semantic graph for the document. Then we transform the keyphrase extraction problem into an optimization problem on the graph. Finally, we get the optimal keyphrase set to be the output. Our method obtains improvements over other state-of-art models by more than 2% in F1-score.
Enhancing Q-Learning for Optimal Asset Allocation

This paper enhances the Q-Iearning algorithm for optimal asset allocation proposed in (Neuneier, 1996 ). The new formulation simplifies the approach by using only one value-function for many assets and allows model-free policy-iteration. After testing the new algorithm on real data, the possibility of risk management within the framework of Markov decision problems is analyzed. The proposed methods allows the construction of a multi-period portfolio management system which takes into account transaction costs, the risk preferences of the investor, and several constraints on the allocation.
Unit identifier : Used for identifying a Modbus server connected to a Modbus gateway 3 , say over a serial line Attacks against Modbus TCP

In a model-based intrusion detection approach for protecting SCADA networks, we construct models that characterize the expected/acceptable behavior of the system, and detect attacks that cause violations of these models. Process control networks tend to have static topologies, regular traffic patterns, and a limited number of applications and protocols running on them. Thus, we believe that model-based monitoring, which has the potential for detecting unknown attacks, is more feasible for control networks than for general enterprise networks. To this end, we describe three model-based techniques that we have developed and a prototype implementation of them for monitoring Modbus TCP networks.
Antecedents and Impact of Knowledge Management Capability in Public Organizations

Public organizations are increasingly embracing knowledge management (KM) tools and practices. However, there is a lack of understanding and empirical research in the public context to offer relevant insights. This study examines how KM influences public organizations’ performance through the concept of KM capability. KM capability represents an organization’s ability to capture, share, apply, and create knowledge. The salient resources influencing KM capability are identified and their direct and interaction effects are examined. The effect of KM capability on organizational performance is also assessed. Findings from a survey of 101 public organizations indicate that the physical KM resources of KM technology support and non-IT KM investments significantly influence the level of KM capability. Physical KM resources also have stronger effect on KM capability when the organizational KM resource of senior management championship exists. In contrast, the effects of physical KM resources are weakened by centralized and formalized organizational structure. Implications of these findings for KM research and practice in public organizations are discussed.
Health monitoring using Internet of Things (IoT)

Health is the level of useful and metabolic potency of a living organism. In humans, it is the power of people or communities to adapt and self-manage once facing physical, mental, psychological and social changes. Keeping track of the health standing of the patient reception may be a troublesome task. Specially maturity patients ought to be sporadically monitored and their adored ones got to learn concerning their health standing from time to time whereas at work. During this paper, our objective is to trace patient's health with the assistance of sensors and internet. Internet is employed to tell their beloved if there is a drag. The health observation system can keep track of patient's pulse rate, eco rate of heart, pressure level rate, temperature etc. If system detects any abrupt changes in patient heartbeat or temperature, the system mechanically alerts the user concerning the patients standing over IOT and additionally shows details of heartbeat and temperature of patient live over the internet.
Generalized Hill-Stability Criteria for Hierarchical Three-Body Systems at Arbitrary Inclinations

A fundamental aspect of the three-body problem is its stability. Most stability studies have focused on the co-planar three-body problem, deriving analytic criteria for the dynamical stability of such pro/retrograde systems. Numerical studies of inclined systems phenomenologically mapped their stability regions, but neither complement it by theoretical framework, nor provided satisfactory fit for their dependence on mutual inclinations. Here we present a novel approach to study the stability of hierarchical three-body systems at arbitrary inclinations, which accounts not only for the instantaneous stability of such systems, but also for the secular stability and evolution through Lidov-Kozai cycles and evection. We generalize the Hill-stability criteria to arbitrarily inclined triple systems, explain the existence of quasistable regimes and characterize the inclination dependence of their stability. We complement the analytic treatment with an extensive numerical study, to test our analytic results. We find excellent correspondence up to high inclinations (∼ 120◦), beyond which the agreement is marginal. At such high inclinations the stability radius is larger, the ratio between the outer and inner periods becomes comparable, and our secular averaging approach is no longer strictly valid. We therefore combine our analytic results with polynomial fits to the numerical results to obtain a generalized stability formula for triple systems at arbitrary inclinations. Besides providing a generalized secular-based physical explanation for the stability of non co-planar systems, our results have direct implications for any triple systems, and in particular binary planets and moon/satellite systems; we briefly discuss the latter as a test case for our models.
Learning long-term dependencies with gradient descent is difficult

Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases. These results expose a trade-off between efficient learning by gradient descent and latching on information for long periods. Based on an understanding of this problem, alternatives to standard gradient descent are considered.
Video Synthesis from a Single Image and Motion Stroke

In this paper, we propose a new method to automatically generate a video sequence from a single image and a user provided motion stroke. Generating a video sequence based on a single input image has many applications in visual content creation, but it is tedious and time-consuming to produce even for experienced artists. Automatic methods have been proposed to address this issue, but most existing video prediction approaches require multiple input frames. In addition, generated sequences have limited variety since the output is mostly determined by the input frames, without allowing the user to provide additional constraints on the result. In our technique, users can control the generated animation using a sketch stroke on a single input image. We train our system such that the trajectory of the animated object follows the stroke, which makes it both more flexible and more controllable. From a single image, users can generate a variety of video sequences corresponding to different sketch inputs. Our method is the first system that, given a single frame and a motion stroke, can generate animations by recurrently generating videos frame by frame. An important benefit of the recurrent nature of our architecture is that it facilitates the synthesis of an arbitrary number of generated frames. Our architecture uses an autoencoder and a generative adversarial network (GAN) to generate sharp texture images, and we use another GAN to guarantee that transitions between frames are realistic and smooth. We demonstrate the effectiveness of our approach on the MNIST, KTH, and Human 3.6M datasets.
Biomimetic flexible robot arm design and kinematic analysis of a novel flexible robot arm

Conventional robot manipulators have singularities in their workspaces and constrained spatial movements. Flexible and soft robots provide a unique solution to overcome this limitation. Flexible robot arms have biologically inspired characteristics as flexible limbs and redundant degrees of freedom. From these special characteristics, flexible manipulators are able to develop abilities such as bend, stretch and adjusting stiffness to traverse a complex maze. Many researchers are working to improve capabilities of flexible arms by improving the number of degrees of freedoms and their methodologies. The proposed flexible robot arm is composed of multiple sections and each section contains three similar segments and a base segment. These segments act as the backbone of the basic structure and each section can be controlled by changing the length of three control wires. These control wires pass through each segment and are held in place by springs. This design provides each segment with 2 DOF. The proposed system single section can be bent 90° with respective to its centre axis. Kinematics of the flexible robot is derived with respect to the base segment.
SELF – FORGIVENESS : THE STEPCHILD OF FORGIVENESS RESEARCH

Although research on interpersonal forgiveness is burgeoning, there is little conceptual or empirical scholarship on self–forgiveness. To stimulate research on this topic, a conceptual analysis of self–forgiveness is offered in which self–forgiveness is defined and distinguished from interpersonal forgiveness and pseudo self–forgiveness. The conditions under which self–forgiveness is appropriate also are identified. A theoretical model describing the processes involved in self–forgiveness following the perpetration of an interpersonal transgression is outlined and the proposed emotional, social–cognitive, and offense–related determinants of self–forgiveness are described. The limitations of the model and its implications for future research are explored.
The automation of science.

The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge.
Early Pest Identification in Agricultural Crops using Image Processing Techniques

Early disease detection is a major challenge in agriculture field. Hence proper measures has to be taken to fight bioagressors of crops while minimizing the use of pesticides. The techniques ofmachine vision are extensively applied to agricultural science,and it has great perspective especially in the plant protection field,which ultimately leads to crops management. Our goal is early detection of bioagressors. The paper describes a software prototype system for pest detection on the infected images of different leaves. Images of the infected leaf are captured bydigital camera and processed using image growing, image segmentation techniques to detect infected parts of the particular plants.Then the detected part is been processed for futher feature extraction which gives general idea about pests. This proposes automatic detection and calculating area of infection on leaves of a whitefly (Trialeurodes vaporariorum Westwood) at a mature stage.
Indian Herbs and Herbal Drugs Used for the Treatment of Diabetes

Traditional Medicines derived from medicinal plants are used by about 60% of the world's population. This review focuses on Indian Herbal drugs and plants used in the treatment of diabetes, especially in India. Diabetes is an important human ailment afflicting many from various walks of life in different countries. In India it is proving to be a major health problem, especially in the urban areas. Though there are various approaches to reduce the ill effects of diabetes and its secondary complications, herbal formulations are preferred due to lesser side effects and low cost. A list of medicinal plants with proven antidiabetic and related beneficial effects and of herbal drugs used in treatment of diabetes is compiled. These include, Allium sativum, Eugenia jambolana, Momordica charantia Ocimum sanctum, Phyllanthus amarus, Pterocarpus marsupium, Tinospora cordifolia, Trigonella foenum graecum and Withania somnifera. One of the etiologic factors implicated in the development of diabetes and its complications is the damage induced by free radicals and hence an antidiabetic compound with antioxidant properties would be more beneficial. Therefore information on antioxidant effects of these medicinal plants is also included.