nils-herrmann/clean_scirepeval huggingface dataset:
Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization

Many graph-based semi-supervised learning methods for large datasets have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). This model builds a regularization framework by exploring the underlying structure of the whole dataset with both datapoints and anchors. Nevertheless, AGR still has limitations in its two components: (1) in anchor graph construction, the estimation of the local weights between each datapoint and its neighboring anchors could be biased and relatively slow; and (2) in anchor graph regularization, the adjacency matrix that estimates the relationship between datapoints, is not sufficiently effective. In this paper, we develop an Efficient Anchor Graph Regularization (EAGR) by tackling these issues. First, we propose a fast local anchor embedding method, which reformulates the optimization of local weights and obtains an analytical solution. We show that this method better reconstructs datapoints with anchors and speeds up the optimizing process. Second, we propose a new adjacency matrix among anchors by considering the commonly linked datapoints, which leads to a more effective normalized graph Laplacian over anchors. We show that, with the novel local weight estimation and normalized graph Laplacian, EAGR is able to achieve better classification accuracy with much less computational costs. Experimental results on several publicly available datasets demonstrate the effectiveness of our approach.
An Actor-Critic reinforcement learning algorithm based on adaptive RBF network

We introduce an algorithm of Actor-Critic reinforcement learning methods in continuous state space. In order to cope with large-scale or continuous state spaces, the algorithm utilizes applied radial basis function (RBF) neural network to approximate the state value function. By training self-adapted non-linear processing unit, realizing online adaptive reconstructing of state space, the approximation is improved. In order to improve the efficient of exploration, a hybrid exploration strategy is proposed. Experimental studies concerning a Mountain-Car control task illustrate the performance and applicability of the proposed algorithm.
Disease Inference from Health-Related Questions via Sparse Deep Learning

Automatic disease inference is of importance to bridge the gap between what online health seekers with unusual symptoms need and what busy human doctors with biased expertise can offer. However, accurately and efficiently inferring diseases is non-trivial, especially for community-based health services due to the vocabulary gap, incomplete information, correlated medical concepts, and limited high quality training samples. In this paper, we first report a user study on the information needs of health seekers in terms of questions and then select those that ask for possible diseases of their manifested symptoms for further analytic. We next propose a novel deep learning scheme to infer the possible diseases given the questions of health seekers. The proposed scheme is comprised of two key components. The first globally mines the discriminant medical signatures from raw features. The second deems the raw features and their signatures as input nodes in one layer and hidden nodes in the subsequent layer, respectively. Meanwhile, it learns the inter-relations between these two layers via pre-training with pseudo-labeled data. Following that, the hidden nodes serve as raw features for the more abstract signature mining. With incremental and alternative repeating of these two components, our scheme builds a sparsely connected deep architecture with three hidden layers. Overall, it well fits specific tasks with fine-tuning. Extensive experiments on a real-world dataset labeled by online doctors show the significant performance gains of our scheme.
Exploiting salient word dependency for Chinese NP identification: A study on classifier noun phrase

NP identification is a challenging subtask of NLP. The reported literatures mainly focus on base noun phrase and maximal-length noun phrase, and deal with them as a sequence labeling problem. In this paper, unlike existing perspective, we concentrate on a special subcategory of Chinese NP, classifier noun phrase (CNP), and present a new approach which uses salient word dependency, such as classifier-noun collocation, for CNP identification. The experiment result is encouraging. Our study shows that salient relations between words should be fully utilized in NP identification as well as other NLP applications.
A State-Based Predictive Approach for Leakage Reduction of Functional Units

As MOSFETs (metal-oxide-semiconductor field effect transistor) dimensions enter the sub-micrometer region, reducing leakage power becomes a significant issue of VLSI industry. In this paper, we propose a novel prediction approach to predict idleness of functional units for leakage energy management. Using a state-based predictor, historical utilization information of functional units is exploited to adjust the state of the predictor so as to enhance the accuracy of prediction. We implement our approach based on SimpleScalar and conduct experiments with a suite of fourteen benchmarks from Trimaran. The experimental results show that our approach achieves the better results compared with the previous work.
Joint Learning of Multiple Regressors for Single Image Super-Resolution

Using a global regression model for single image super-resolution (SISR) generally fails to produce visually pleasant output. The recently developed local learning methods provide a remedy by partitioning the feature space into a number of clusters and learning a simple local model for each cluster. However, in these methods the space partition is conducted separately from local model learning, which results in an abundant number of local models to achieve satisfying performance. To address this problem, we propose a mixture of experts (MoE) method to jointly learn the feature space partition and local regression models. Our MoE consists of two components: gating network learning and local regressors learning. An expectation-maximization (EM) algorithm is adopted to train MoE on a large set of LR/HR patch pairs. Experimental results demonstrate that the proposed method can use much less local models and time to achieve comparable or superior results to state-of-the-art SISR methods, providing a highly practical solution to real applications.
Multi-Channel missing data recovery by exploiting the low-rank hankel structures

This paper studies the low-rank matrix completion problem by exploiting the temporal correlations in the data. Connecting low-rank matrices with dynamical systems such as power systems, we propose a new model, termed multi-channel low-rank Hankel matrices, to characterize the intrinsic low-dimensional structures in a collection of time series. An accelerated multi-channel fast iterative hard thresholding (AM-FIHT) with a linear convergence rate is proposed to recover the missing points. The required number of observed entries for successful recovery is significantly reduced from conventional low-rank completion methods. Numerical experiments are carried out on recorded PMU data to verify the proposed method.
Detecting Group Activities With Multi-Camera Context

Human group activities detection in multi-camera CCTV surveillance videos is a pressing demand on smart surveillance. Previous works on this topic are mainly based on camera topology inference that is hard to apply to real-world unconstrained surveillance videos. In this paper, we propose a new approach for multi-camera group activities detection. Our approach simultaneously exploits intra-camera and inter-camera contexts without topology inference. Specifically, a discriminative graphical model with hidden variables is developed. The intra-camera and inter-camera contexts are characterized by the structure of hidden variables. By automatically optimizing the structure, the contexts are effectively explored. Furthermore, we propose a new spatiotemporal feature, named vigilant area (VA), to characterize the quantity and appearance of the motion in an area. This feature is effective for group activity representation and is easy to extract from a dynamic and crowded scene. We evaluate the proposed VA feature and discriminative graphical model extensively on two real-world multi-camera surveillance video data sets, including a public corpus consisting of 2.5 h of videos and a 468-h video collection, which, to the best of our knowledge, is the largest video collection ever used in human activity detection. The experimental results demonstrate the effectiveness of our approach.
The Optimizing Design of Wheeled Robot Tracking System by PID Control Algorithm Based on BP Neural Network

The tracking system of robot was optimized by using the fuzzy BP neural network PID control algorithm in this paper, which was based on the traditional PID control algorithm. The control algorithm combines the functions of fuzzy control, the neural network control and the traditional PID control algorithm, which resulted The PID control has the ability of logical reasoning and computing. The system collects the scheduled route information through the sensor module and altogether with the actual output of the system constitute the excitation signal of the neural network. The MATLAB software is used to simulate the system in this paper, compared with the result of the simulation of traditional PID control algorithm. The result shows that optimize design of wheeled robot tracking system with PIDcontrol algorithm based on BP neural network has better adaptive ability and self-learning ability and the improvement of the stability of wheeled robot tracking system.
Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence

Dense 3D shape correspondence is an important problem in computer vision and computer graphics. Recently, the local shape descriptor based 3D shape correspondence approaches have been widely studied, where the local shape descriptor is a real-valued vector to characterize the geometrical structure of the shape. Different from these realvalued local shape descriptors, in this paper, we propose to learn a novel binary spectral shape descriptor with the deep neural network for 3D shape correspondence. The binary spectral shape descriptor can require less storage space and enable fast matching. First, based on the eigenvectors of the Laplace-Beltrami operator, we construct a neural network to form a nonlinear spectral representation to characterize the shape. Then, for the defined positive and negative points on the shapes, we train the constructed neural network by minimizing the errors between the outputs and their corresponding binary descriptors, minimizing the variations of the outputs of the positive points and maximizing the variations of the outputs of the negative points, simultaneously. Finally, we binarize the output of the neural network to form the binary spectral shape descriptor for shape correspondence. The proposed binary spectral shape descriptor is evaluated on the SCAPE and TOSCA 3D shape datasets for shape correspondence. The experimental results demonstrate the effectiveness of the proposed binary shape descriptor for the shape correspondence task.
Extracting Multi-document Summarization Based on Local Topics

In this paper, we propose a new method for text summarization. The system finds topic word and event word firstly, and then recalculates word weight. Using recalculated word weight to compute similarly of paragraphs to search local topics units. The most representative sentences in each local topic unit are selected as the summary sentences. By analyzing semantic structure of the documents first, the summary sentences are not redundancy and the coverage of each local topic is balanced. Experimental results show that our approach is effective and efficient, and performance of the system is reliable.
Calculus relations in spatial summation and determination of macro connection in neuronal networks

A concept of macro connection as an analytic representation for the interactions between any two neurons or neuronal groups is proposed. Based on this concept, the neuronal interactions are investigated at two levels, i.e., the synaptic connections and macro connections. The efficacy of macro connection is explicitly represented and quantitatively determined by the efficacies of the constitutent synapses. This issue is addressed through a calculus approach to obtain an analytical expression for spatial summation properties of a single cell. It is shown that the spatial summation processes in a single cell are, to a large extent, governed by the rules of differential calculus, and that the total differential form is meaningful from the range of subcellular and cellular levels through the level of local networks. As an application of the authors' result, a computational method is proposed for the receptive field and projective field of single cells which, in the sense of medium-term average, can quantitatively give these macro functionally significant units a constructive explanation in terms of the contributive micro components.<<ETX>>
The analysis of response characteristic of the Phase-adjuster for the D20VVT engine

Understanding the velocity response characteristic of the variable valve timing system under parameters of different work status has a very important influence on drawing up the system's control strategy. In this text, a simulation mold of the VVT system has been established based on AMESim. The system changed the engine's timing phase through turning the camshaft. The simulation results have registered that the speed of the engine and the engine oil pressure have a great influence on Phase-adjuster response characteristic as well as the structure parameters of Phase-adjuster. The engine oil's temperature and the rotary inertia of rotor were so small that can be ignored. We also got the flow feature of the oil control valve under control signal of different duty ratio.
Visual Classification by ℓ1-Hypergraph Modeling

Visual classification has attracted considerable research interests in the past decades. In this paper, a novel $\ell _1$ -hypergraph model for visual classification is proposed. Hypergraph learning, as a natural extension of graph model, has been widely used in many machine learning tasks. In previous work, hypergraph is usually constructed by attribute-based or neighborhood-based methods. That is, a hyperedge is generated by connecting a set of samples sharing a same feature attribute or in a neighborhood. However, these methods are unable to explore feature space globally or sensitive to noises. To address these problems, we propose a novel hypergraph construction approach that leverages sparse representation to generate hyperedges and learns the relationship among hyperedges and their vertices. First, for each sample, a hyperedge is generated by regarding it as the centroid and linking it as well as its nearest neighbors. Then, the sparse representation method is applied to represent the centroid vertex by other vertices within the same hyperedge. The vertices with zero coefficients are removed from the hyperedge. Finally, the representation coefficients are used to define the incidence relation between the hyperedge and the vertices. In our approach, we also optimize the hyperedge weights to modulate the effects of different hyperedges. We leverage the prior knowledge on the hyperedges so that the hyperedges sharing more vertices can have closer weights, where a graph Laplacian is used to regularize the optimization of the weights. Our approach is named $\ell _1$ -hypergraph since the $\ell _1$ sparse representation is employed in the hypergraph construction process. The method is evaluated on various visual classification tasks, and it demonstrates promising performance.
Factorized Similarity Learning in Networks

The problem of similarity learning is relevant to many data mining applications, such as recommender systems, classification, and retrieval. This problem is particularly challenging in the context of networks, which contain different aspects such as the topological structure, content, and user supervision. These different aspects need to be combined effectively, in order to create a holistic similarity function. In particular, while most similarity learning methods in networks such as Sim Rank utilize the topological structure, the user supervision and content are rarely considered. In this paper, a Factorized Similarity Learning (FSL) is proposed to integrate the link, node content, and user supervision into an uniform framework. This is learned by using matrix factorization, and the final similarities are approximated by the span of low rank matrices. The proposed framework is further extended to a noise-tolerant version by adopting a hinge-loss alternatively. To facilitate efficient computation on large scale data, a parallel extension is developed. Experiments are conducted on the DBLP and CoRA datasets. The results show that FSL is robust, efficient, and outperforms the state-of-the-art.
A Dynamic Trust Model Based on Recommendation Credibility in Grid Domain

Grid is the next generation of Internet, and it has attracted the attentions of research communities recently. In practical applications, the nodes in the grid domain need to transact with other strange nodes to acquire the services provided by those nodes. Recently, trust has been recognized as an important factor for Grid security. And it is important for them to make a trust evaluation for those strange nodes before transactions, because some nodes are dishonest. Currently, the method to solve this problem is to establish an effective trust mechanism in grid domain based on reputation. However, there are still some disadvantages in existence in some cases, as the dynamicity of the grid environment, although it can be risk-averse to some extent. Like some malicious nodes contribute high trust value through numerous good interaction behaviors at first, and then change to attack. In view of those problems and disadvantages, this paper proposes a dynamic grid trust model based on recommendation credibility, called DyGridTrust (Dynamic Grid Trust Model). This model can distinguish honest and dishonest recommendation and adjust the weight of trust evaluation dynamically. In addition, this trust model is able to obtain the trust value more objectively, so as to enhance the security of the grid system. Finally, in this paper, those perspectives have been confirmed by simulations, which are developed by Matlab R2008a.
Optimizing Network Configurations Based on Potential Profit Loss

IP routing protocols, such as OSPF (open shortest path first) or BGP (border gateway protocol), have many configurable parameters (e.g., timers) and interact with each other in complex ways. Network operators may need to explore numerous candidate configuration changes before identifying a choice. In this paper, from the point of view of the economics of the Internet backbone, we propose a potential profit loss metric to evaluate configurations. Then we focus on analyzing the potential profit loss caused by link state changes under different configurations. We give quantitative relations between the potential profit loss and the major influential factors: OSPF timers, BGP timers and failure duration. From the quantitative relations we can see that appropriate configurations can reduce the profit loss much.
Multivariable optimal performance under packet loss for networked control systems

This paper focuses on the optimal tracking performance of unstable models. The work is presented for discrete time, linear time-invariant and finite-dimensional networked control systems. An integral square error criterion, between the reference signal and output, is adopted to measure the tracking performance. The explicit expression of optimal tracking error has been obtained. The plant under consideration is assumed to have non-minimum phase (NMP) zeros, unstable poles and data packet loss. The results show that data packet loss, NMP zeros and unstable poles, can worsen the tracking performance. Specifically, the allowable maximum data packet loss rate is calculated in the example.
Notice of RetractionBearing Capacity Analysis of Plate Girder Splice Bolts of the Boiler in Power Station

Plate girder is the main pressed member in boiler steel structure of the power station. Because of its large scale and heavy loads ,the influence of the joints area is complicated to the mechanical behavior of the whole structure. It is needed to be researched whether the design method due to specification is safe. This paper contrasts the result of finite element computation with specification in three sections which include the bolt spliced of web , flange, and laminated layer, also contrasts the actual finite element computed curve with the calculation of specification to judge whether it is safe due to specification and hands in several proper proposals for application.
High-power Pit (Well)-Ground Electromagnetic Automatic Sweep Frequency Transmitter Used for Exploration of Metal Mine

Using the existing pits, wells and tunnels in mine areas, high power electromagnetic transmitters on the ground and underground (pit, well) and receivers on the ground can be integrated as a quasi 3-D measuring device, which provides a new method for exploration of metal mines. Under-research transmitters as actuating devices can automatically transmit high power inverted square wave in sweep frequency ranging from 0.9375Hz to 9600Hz. Maximum transmitting voltage is 700V and supply current is 60A. At the same time, GPS clock and Real-Time Clock (RTC) can be used to ensure transmitting predefined or user-defined power supply frequency at fixed time and on time. Bluetooth and serial communication interfaces can be used to control transmission and running and edit control parameters. Many kinds of information about auxiliary status can be observed. Reliable, smooth and high-efficient performance of instruments, which has been proved by tests, can fully meet the requirements of electromagnetic exploration of metal mines and replacing materials ‘tap deeply and explore the blind’ of crisis mines.
Consolidating virtual machines with dynamic bandwidth demand in data centers

Recent advances in virtualization technology have made it a common practice to consolidate virtual machines(VMs) into a fewer number of servers. An efficient consolidation scheme requires that VMs are packed tightly, yet receive resources commensurate with their demands. However, measurements from production data centers show that the network bandwidth demands of VMs are dynamic, making it difficult to characterize the demands by a fixed value and to apply traditional consolidation schemes. In this work, we formulate the VM consolidation into a Stochastic Bin Packing problem and propose an online packing algorithm by which the number of servers required is within equation of the optimum for any ∈ > 0. The result can be improved to within equation of the optimum in a special case. In addition, we use numerical experiments to evaluate the proposed consolidation algorithm and observe 30% server reduction compared to several benchmark algorithms.