nils-herrmann/clean_scirepeval huggingface dataset:
Aalborg Universitet Review of Active and Reactive Power Sharing Strategies in Hierarchical Controlled

Microgrids consist of multiple parallel-connected distributed generation (DG) units with coordinated control strategies, which are able to operate in both grid-connected and islanded mode. Microgrids are attracting more and more attention since they can alleviate the stress of main transmission systems, reduce feeder losses, and improve system power quality. When the islanded microgrids are concerned, it is important to maintain system stability and achieve load power sharing among the multiple parallel-connected DG units. However, the poor active and reactive power sharing problems due to the influence of impedance mismatch of the DG feeders and the different ratings of the DG units are inevitable when the conventional droop control scheme is adopted. Therefore, the adaptive/improved droop control, network-based control methods and cost-based droop schemes are compared and summarized in this paper for active power sharing. Moreover, nonlinear and unbalanced loads could further affect the reactive power sharing when regulating the active power, and it is difficult to share the reactive power accurately only by using the enhanced virtual impedance method. Therefore, the hierarchical control strategies are utilized as supplements of the conventional droop controls and virtual impedance methods. The improved hierarchical control approaches such as the algorithms based on graph theory, multi-agent system, the gain scheduling method and predictive control have been proposed to achieve proper reactive power sharing for islanded microgrids and eliminate the effect of the communication delays on hierarchical control. Finally, the future research trends on islanded microgrids are also discussed in this paper.
From POS tagging to dependency parsing for biomedical event extraction

Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance. We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT. To the best of our knowledge, there is no recent work making such comparisons in the biomedical context; specifically no detailed analysis of neural models on this data is available. Experimental results show that in general, the neural models outperform the feature-based models on two benchmark biomedical corpora GENIA and CRAFT. We also perform a task-oriented evaluation to investigate the influences of these models in a downstream application on biomedical event extraction, and show that better intrinsic parsing performance does not always imply better extrinsic event extraction performance. We have presented a detailed empirical study comparing traditional feature-based and neural network-based models for POS tagging and dependency parsing in the biomedical context, and also investigated the influence of parser selection for a biomedical event extraction downstream task. We make the retrained models available at https://github.com/datquocnguyen/BioPosDep.
Reverse-Projection Method for Measuring Camera MTF

This paper proposes several adjustments to the ISO 12233 slanted edge algorithm for estimating camera MTF. First, the Ridler-Calvard binary image segmentation method is used to find the line. Secondly, total least squares, rather than ordinary least squares, is used to compute the line parameters. Finally, the pixel values are projected in the reverse direction from the 1D array to the 2D image, rather than from the 2D image to the 1D array. Together, these changes yield an algorithm that exhibits significantly less variation than existing techniques when applied to real images. In particular, the proposed algorithm is largely invariant to the rotation angle of the edge as well as to the size of the image crop.
Improvised Architecture for Distributed Web Crawling

Web crawlers are program, designed to fetch web pages for information retrieval system. Crawlers facilitate this process by following hyperlinks in web pages to automatically download new or update existing web pages in the repository. A web crawler interacts with millions of hosts, fetches millions of page per second and updates these pages into a database, creating a need for maintaining I/O performance, network resources within OS limit, which are essential in order to achieve high performance at a reasonable cost. This paper aims to showcase efficient techniques to develop a scalable web crawling system, addressing challenges which deals with issues related to the structure of the web, distributed computing, job scheduling, spider traps, canonicalizing URLs and inconsistent data formats on the web. A brief discussion on new web crawler architecture is done in this paper.
An SVM-based approach for stock market trend prediction

In this paper, an SVM-based approach is proposed for stock market trend prediction. The proposed approach consists of two parts: feature selection and prediction model. In the feature selection part, a correlation-based SVM filter is applied to rank and select a good subset of financial indexes. And the stock indicators are evaluated based on the ranking. In the prediction model part, a so called quasi-linear SVM is applied to predict stock market movement direction in term of historical data series by using the selected subset of financial indexes as the weighted inputs. The quasi-linear SVM is an SVM with a composite quasi-linear kernel function, which approximates a nonlinear separating boundary by multi-local linear classifiers with interpolation. Experimental results on Taiwan stock market datasets demonstrate that the proposed SVM-based stock market trend prediction method produces better generalization performance over the conventional methods in terms of the hit ratio. Moreover, the experimental results also show that the proposed SVM-based stock market trend prediction system can find out a good subset and evaluate stock indicators which provide useful information for investors.
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge this gap. Rather than designing a “fast” reinforcement learning algorithm, we propose to represent it as a recurrent neural network (RNN) and learn it from data. In our proposed method, RL, the algorithm is encoded in the weights of the RNN, which are learned slowly through a general-purpose (“slow”) RL algorithm. The RNN receives all information a typical RL algorithm would receive, including observations, actions, rewards, and termination flags; and it retains its state across episodes in a given Markov Decision Process (MDP). The activations of the RNN store the state of the “fast” RL algorithm on the current (previously unseen) MDP. We evaluate RL experimentally on both small-scale and large-scale problems. On the small-scale side, we train it to solve randomly generated multi-armed bandit problems and finite MDPs. After RL is trained, its performance on new MDPs is close to human-designed algorithms with optimality guarantees. On the largescale side, we test RL on a vision-based navigation task and show that it scales up to high-dimensional problems.
Second Language Acquisition Modeling: An Ensemble Approach

Accurate prediction of students knowledge is a fundamental building block of personalized learning systems. Here, we propose a novel ensemble model to predict student knowledge gaps. Applying our approach to student trace data from the online educational platform Duolingo we achieved highest score on both evaluation metrics for all three datasets in the 2018 Shared Task on Second Language Acquisition Modeling. We describe our model and discuss relevance of the task compared to how it would be setup in a production environment for personalized education.
Molecular Hypergraph Grammar with its Application to Molecular Optimization

This paper is concerned with a molecular optimization framework using variational autoencoders (VAEs). In this paradigm, VAE allows us to convert a molecular graph into/from its latent continuous vector, and therefore, the molecular optimization problem can be solved by continuous optimization techniques. One of the longstanding issues in this area is that it is difficult to always generate valid molecules. The very recent work called the junction tree variational autoencoder (JT-VAE) successfully solved this issue by generating a molecule fragment-by-fragment. While it achieves the state-of-the-art performance, it requires several neural networks to be trained, which predict which atoms are used to connect fragments and stereochemistry of each bond. In this paper, we present a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to address the issue. Our idea is to develop a novel graph grammar for molecular graphs called molecular hypergraph grammar (MHG), which can specify the connections between fragments and the stereochemistry on behalf of neural networks. This capability allows us to address the issue using only a single VAE. We empirically demonstrate the effectiveness of MHGVAE over existing methods.
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.
Phylogenomic insights into the cambrian explosion, the colonization of land and the evolution of flight in arthropoda.

The timing of the origin of arthropods in relation to the Cambrian explosion is still controversial, as are the timing of other arthropod macroevolutionary events such as the colonization of land and the evolution of flight. Here we assess the power of a phylogenomic approach to shed light on these major events in the evolutionary history of life on earth. Analyzing a large phylogenomic dataset (122 taxa, 62 genes) with a Bayesian-relaxed molecular clock, we simultaneously reconstructed the phylogenetic relationships and the absolute times of divergences among the arthropods. Simulations were used to test whether our analysis could distinguish between alternative Cambrian explosion scenarios with increasing levels of autocorrelated rate variation. Our analyses support previous phylogenomic hypotheses and simulations indicate a Precambrian origin of the arthropods. Our results provide insights into the 3 independent colonizations of land by arthropods and suggest that evolution of insect wings happened much earlier than the fossil record indicates, with flight evolving during a period of increasing oxygen levels and impressively large forests. These and other findings provide a foundation for macroevolutionary and comparative genomic study of Arthropoda.
Simplified modeling and identification approach for model-based control of parallel mechanism robot leg

Recently, new configurations of robot legs using a parallel mechanism have been studied for improving the locomotion ability in four-legged robots. However, it is difficult to obtain full dynamics of the parallel-mechanism robot legs because this mechanism has many links and complex constraint conditions, which make it difficult to design a modelbased controller. Here, we propose the simplified modeling of a parallel-mechanism robot leg with two degrees-of-freedom (2DOF), which can be used instead of complex full dynamics for model-based control. The new modeling approach considers the robot leg as a 2DOF Revolute and Prismatic(RP) manipulator, inspired by the actuation mechanism of robot legs, for easily designing a nominal model of the controller. To verify the effectiveness of the new modeling approach experimentally, we conducted dynamic simulations using a commercial multi-dynamics simulator. The simulation results confirmed that the proposed modeling approach could be an alternative modeling method for parallel-mechanism robot legs.
The role of budgets in organizations facing strategic change : an exploratory study

Recent attention in the general management literature has focused on mechanisms and processes used by organizations to respond and adapt to changes in their operating environment. There is, however, very little broad-based empirical research examining the role that management accounting control systems can play in shaping organizational change. Much of the empirical research to date has focused on the role of accounting as a diagnostic tool for assessing and rewarding managerial performance despite the recognition that accounting can serve as a dialogue, learning and idea creation machine (Burchell et al., 1980. . The purpose of this study is to explore how accounting can serve this alternative role. We use (Simons, R. 1990.) Accounting Organisations and Society 15, 127±143). interactive/diagnostic classi®cation of management control systems to capture how accounting can be used as a learning machine in the formulation and implementation of strategic change. A theoretical model is developed to examine the relationship between strategic change, style of budget use and performance. It is argued that an interactive style of budget use can mitigate the disruptive performance eects of the strategic change process. The data, collected from Chief Executive Ocers in 63 public hospitals, provide results that are consistent with our expectations.
Review. Fungal endophytes and their interaction with plant pathogens

Endophytes are fungi which infect plants without causing symptoms. Fungi belonging to this group are ubiquitous, and plant species not associated to fungal endophytes are not known. In addition, there is a large biological diversity among endophytes, and it is not rare for some plant species to be hosts of more than one hundred different endophytic species. Different mechanisms of transmission, as well as symbiotic lifestyles occur among endophytic species. Latent pathogens seem to represent a relatively small proportion of endophytic assemblages, also composed by latent saprophytes and mutualistic species. Some endophytes are generalists, being able to infect a wide range of hosts, while others are specialists, limited to one or a few hosts. Endophytes are gaining attention as a subject for research and applications in Plant Pathology. This is because in some cases plants associated to endophytes have shown increased resistance to plant pathogens, particularly fungi and nematodes. Several possible mechanisms by which endophytes may interact with pathogens are discussed in this review.  biocontrol, biodiversity, symbiosis.
Vertical Versus Shared Leadership as Predictors of the Effectiveness of Change Management Teams : An Examination of Aversive , Directive , Transactional , Transformational , and Empowering Leader Behaviors

This study investigated vertical versus shared leadership as predictors of the effectiveness of 71 change management teams. Vertical leadership stems from an appointed or formal leader of a team, whereas shared leadership (C. L. Pearce, 1997; C. L. Pearce & J. A. Conger, in press; C. L. Pearce & H. P. Sims, 2000) is a group process in which leadership is distributed among, and stems from, team members. Team effectiveness was measured approximately 6 months after the assessment of leadership and was also measured from the viewpoints of managers, internal customers, and team members. Using multiple regression, the authors found both vertical and shared leadership to be significantly related to team effectiveness ( p .05), although shared leadership appears to be a more useful predictor of team effectiveness than vertical leadership.
Fast and scalable polynomial kernels via explicit feature maps

Approximation of non-linear kernels using random feature mapping has been successfully employed in large-scale data analysis applications, accelerating the training of kernel machines. While previous random feature mappings run in O(ndD) time for $n$ training samples in d-dimensional space and D random feature maps, we propose a novel randomized tensor product technique, called Tensor Sketching, for approximating any polynomial kernel in O(n(d+D \log{D})) time. Also, we introduce both absolute and relative error bounds for our approximation to guarantee the reliability of our estimation algorithm. Empirically, Tensor Sketching achieves higher accuracy and often runs orders of magnitude faster than the state-of-the-art approach for large-scale real-world datasets.
DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks

The past few years have witnessed the fast development of different regularization methods for deep learning models such as fully-connected deep neural networks (DNNs) and Convolutional Neural Networks (CNNs). Most of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, Cutout and DropBlocks. DropConnect select to drop connections between fully-connected layers. By randomly discard some features or connections, the above mentioned methods control the overfitting problem and improve the performance of neural networks. In this paper, we proposed two novel regularization methods, namely DropFilter and DropFilter-PLUS, for the learning of CNNs. Different from the previous methods, DropFilter and DropFilter-PLUS selects to modify the convolution filters. For DropFilter-PLUS, we find a suitable way to accelerate the learning process based on theoretical analysis. Experimental results on MNISTshow that using DropFilter and DropFilter-PLUS may improve performance on image classification tasks.
Virtual Reality Interpolation Synthesis of Articulated Figure Motion

ost conventional media depend on engaging and appealing characters. Empty spaces and buildings would not fare well as television or movie programming, yet virtual reality usually offers up such spaces. The problem lies in the difficulty of creating computer-generated characters that display real-time, engaging interaction and realistic motion. Articulated figure motion for real-time computer graphics offers one solution to this problem. A common approach stores a set of motions and lets you choose one particular motion at a time. This article describes a process that greatly expands the range of possible motions. Mixing motions selected from a database lets you create a new motion to exact specifications. The synthesized motion retains the original motions' subtle qualities, such as the realism of motion capture or the expressive, exaggerated qualities of artistic animation. Our method provides a new way to achieve inverse kine-matics capability—for example, placing the hands or feet of an articulated figure in specific positions. It proves useful for both real-time graphics and prerendered animation production. Video production in computer-based and traditional animation relies largely on the labor-intensive process of keyframing. 1 This does not permit creating real-time characters with intriguingly rich varieties of motion. Motion capture allows more rapid production of character motion, but still suffers from the inherent lack of control of prerecorded motion data. Physical simulation approaches offer promise, but are hampered by lack of control, difficulty of use, instabilities, and computational cost that usually precludes real-time operation. 2 The predominant approach to real-time motion synthesis , selection of one stored motion at runtime, restricts computation to the stored motion's display. Unfortunately, a limited number of stored motions can result in repetitive or incorrect motions. The repertoire of possible motions expands greatly— at the cost of additional computation—with interpolation synthesis. This technique combines a set of motions similar to that desired to form an exactly specified motion. A small set of example motions then yields a continuous multidimensional space of possible motions. The example motions can come from keyframing, physical simulations, or motion capture. Because the interpolation process generally preserves the motion's subtle qualities, we can create reusable libraries of motion data from a single step of laborious keyframing, iterative estimation of initial conditions, or correction of errors and dropouts, respectively. This article focuses on real-time interpolation synthesis of motion based on motion capture data. This approach works well for real-time character motion in interactive entertainment or …