nils-herrmann/clean_scirepeval huggingface dataset:
Reusable Reinforcement Learning via Shallow Trails

Reinforcement learning has shown great success in helping learning agents accomplish tasks autonomously from environment interactions. Meanwhile in many real-world applications, an agent needs to accomplish not only a fixed task but also a range of tasks. For this goal, an agent can learn a metapolicy over a set of training tasks that are drawn from an underlying distribution. By maximizing the total reward summed over all the training tasks, the metapolicy can then be reused in accomplishing test tasks from the same distribution. However, in practice, we face two major obstacles to train and reuse metapolicies well. First, how to identify tasks that are unrelated or even opposite with each other, in order to avoid their mutual interference in the training. Second, how to characterize task features, according to which a metapolicy can be reused. In this paper, we propose the MetA-Policy LEarning (MAPLE) approach that overcomes the two difficulties by introducing the shallow trail. It probes a task by running a roughly trained policy. Using the rewards of the shallow trail, MAPLE automatically groups similar tasks. Moreover, when the task parameters are unknown, the rewards of the shallow trail also serve as task features. Empirical studies on several controlling tasks verify that MAPLE can train metapolicies well and receives high reward on test tasks.
Research of Secure Anycast Group Management

Because group management protocol and routing protocol of anycast are not so mature, when communicating it is easy to be attacked by malicious programs such as masquerading and denial of service, etc. Aiming at security of anycast, we propose a new secure anycast group management architecture based on others' research in this paper. We setup a Certificate Authentication Server in the group management; it provides authentication service for any anycast server and anycast member to be joining in its group using PKI technology and secure communication mode. This architecture uses the advantages of related secure technology of multicast for reference and overcomes existing shortcoming of anycast group management protocols. This architecture has been proved to be feasible and effective through large numbers of simulation and experiments.
Integrate C&P Operation Enabling Systems Using Reference Semantic Model

The chemical and petroleum (oil and gas, C&P) is a fast growing and diversified industry. Various applications, from field technique equipment data exchange to business execution with Enterprise Resource Planning (ERP) system, have been deployed to manage and record operation performance. Various industry standards have been defined to create a commonly accepted nomenclature for representing structural and operational aspects of oil and gas enterprises. All of these industry standards represent valuable sources of information and industry best practice, which is accepted by large community and helps enterprise building IT systems in flexible and inter-operable way. However, the overlaps and gaps between standards bring inconsistency for implementation. This article introduces Reference Semantic Model (RSM), a key component of IBM C&P solution and basis of C&P integration framework, which pull together concepts from various standards using a semantic modeling approach, fill gaps and build bridge between the standards, and presents a uniform information model with rich context quantity for C&P enterprises. It then brought up a RSM based model driven integration solution for solving typical C&P system integration problem, which is flexible, interoperable and standards implemented.
Cognitive radar waveform design with low range sidelobes and high Doppler tolerance

This paper mainly considers the unimodular Linear-FM synthesized (LFM-Syn) waveform design for cognitive radar. To achieve its low range sidelobes and high Doppler tolerance together, a novel template-based objective function is formulated with constraint on both the quadratic phase and random phase of the waveform. In addition, a novel Alternating Projection Phase Control method (APPC) is proposed to optimize this function. Simulations show some performance of APPC over several existing techniques, and also our waveforms optimized by APPC 1) has Doppler tolerance similar to conventional LFM one and 2) has Low Probability of Intercept (LPI) similar to the random noise one, as well as 3) has low range sidelobes.
Selectively ensembling neural classifiers

Ensembling neural classifiers can significantly improve the generalization ability of classification systems. In this paper, GASEN, a genetic algorithm based selective ensemble method, that has been shown to be excellent in ensembling neural regressors, is applied to neural classifiers. Experiments on four large data sets show that this method can generate ensembles of neural classifiers with stronger generalization ability than those generated by Bagging, Adaboost, or Arc-x4.
Scalable Multi-instance Learning

Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects such as images and genes. However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an efficient and scalable MIL algorithm named miFV. Our algorithm maps the original MIL bags into a new feature vector representation, which can obtain bag-level information, and meanwhile lead to excellent performances even with linear classifiers. In consequence, thanks to the low computational cost in the mapping step and the scalability of linear classifiers, miFV can handle large scale MIL data efficiently and effectively. Experiments show that miFV not only achieves comparable accuracy rates with state-of-the-art MIL algorithms, but has hundreds of times faster speed than other MIL algorithms.
Least Square Incremental Linear Discriminant Analysis

Linear discriminant analysis (LDA) is a well-known dimension reduction approach, which projects high-dimensional data into a low-dimensional space with the best separation of different classes. In many tasks, the data accumulates over time, and thus incremental LDA is more desirable than batch LDA. Several incremental LDA algorithms have been developed and achieved success; however, the eigen-problem involved requires a large computation cost, which hampers the efficiency of these algorithms. In this paper, we propose a new incremental LDA algorithm, LS-ILDA, based on the least square solution of LDA. When new samples are received, LS-ILDA incrementally updates the least square solution of LDA. Our analysis discloses that this algorithm produces the exact least square solution of batch LDA, while its computational cost is O(min(n; d) £ d) for one update on dataset containing n instances in d-dimensional space. Experimental results show that comparing with state-of-the-art incremental LDA algorithms, our proposed LS-ILDA achieves high accuracy with low time cost.
A New Subaperture Nonlinear Chirp Scaling Algorithm for Real-Time UWB-SAR Imaging

This paper presents a new approach for high-resolution airborne SAR real-time imaging, which uses a subaperture nonlinear chirp scaling algorithm (NCSA) to accommodate the correction of the motion errors along track and cross track. The proposed algorithm is computationally efficient and can reduce the memory requirement by half with a great shorten of lag time before block processing. Simulation and real UWB-SAR data image formation show the validity of the proposed subaperture NCSA
Halftone visual cryptography

Visual cryptography encodes a secret binary image (SI) into n shares of random binary patterns. If the shares are xeroxed onto transparencies, the secret image can be visually decoded by superimposing a qualified subset of transparencies, but no secret information can be obtained from the superposition of a forbidden subset. The binary patterns of the n shares, however, have no visual meaning and hinder the objectives of visual cryptography. Extended visual cryptography was proposed recently to construct meaningful binary images as shares using hypergraph colourings, but the visual quality is poor. In this paper, a novel technique named halftone visual cryptography is proposed to achieve visual cryptography via halftoning. Based on the blue-noise dithering principles, the proposed method utilizes the void and cluster algorithm to encode a secret binary image into n halftone shares (images) carrying significant visual information. The simulation shows that the visual quality of the obtained halftone shares are observably better than that attained by any available visual cryptography method known to date.
The evidence framework applied to fuzzy hypersphere SVM for UWB SAR landmine detection

The fuzzy hypersphere support vector machine (FHS-SVM) has stronger generalization capability than the hyperplane SVM (HP-SVM) in UWB SAR landmine detection. In this paper, the evidence framework is applied to optimize the hyperparameters of FHS-SVM. Firstly, the equivalence between FHS-SVM training and the level 1 Bayesian inference of the evidence framework is proved. Next, the FHS-SVM hyperparameter optimization iterative method is proposed based on the evidence framework. The proposed method has been validated with the ultra-wide band synthetic aperture radar (UWB SAR) landmine detection data
Tri-training: exploiting unlabeled data using three classifiers

In many practical data mining applications, such as Web page classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as co-training have attracted much attention. In this paper, a new co-training style semi-supervised learning algorithm, named tri-training, is proposed. This algorithm generates three classifiers from the original labeled example set. These classifiers are then refined using unlabeled examples in the tri-training process. In detail, in each round of tri-training, an unlabeled example is labeled for a classifier if the other two classifiers agree on the labeling, under certain conditions. Since tri-training neither requires the instance space to be described with sufficient and redundant views nor does it put any constraints on the supervised learning algorithm, its applicability is broader than that of previous co-training style algorithms. Experiments on UCI data sets and application to the Web page classification task indicate that tri-training can effectively exploit unlabeled data to enhance the learning performance.
A coupled memcapacitor emulator based relaxation oscillator

Tremendous efforts have been put into dissecting the inherent characteristics and potential applications of Memcapacitor (MC), which possesses unique abilities of storing both information and energy . Recently, coupling is disclosed as the third relation beyond series and parallel connections of memristive circuits in , of which the mechanical dynamic coupling of MCs is taken into account for illustration purpose. Coupled MCs could provide us more opportunities for developing new electronic devices with unique functions. However, very few works currently focus on the practical implementation of coupled MC emulators and its possible application in electronic circuits. In this letter, a practical emulator of coupled MC is newly proposed and then used for structuring Relaxation Oscillators (ROs), of which the period and duty cycle of output oscillating signal can be purposefully controlled in virtue of the coupling action.
B-Planner: Night bus route planning using large-scale taxi GPS traces

Taxi GPS traces provide us with rich information about the human mobility pattern in modern cities. Instead of designing the bus route based on inaccurate human survey regarding people's mobility pattern, we intend to address the night-bus route planning issue by leveraging taxi GPS traces. In this paper, we propose a two-phase approach based on the crowd-sourced GPS data for night-bus route planning. In the first phase, we develop a process to cluster “hot” areas with dense passenger pick-up/drop-off, and then propose effective methods to split big “hot” areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops as well as bus operation time constraints, we derive several effective rules to build bus routing graph and prune the invalid stops and edges iteratively. We further develop two heuristic algorithms to automatically generate candidate bus routes, and finally we select the best route which expects the maximum number of passengers under the given conditions. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set which contains more than 1.57 million passenger delivery trips, generated by 7,600 taxis for a month in Hangzhou, China.
Fault detection based on belief rule base with online updating attribute weight

In engineering practice, fault detection for complex system is becoming more and more difficult, because enough quantitative observation data can not be obtained. Hence, it is necessary to combine the experts' knowledge and historical data. Belief rule based expert systems have shown excellent performance in modeling complicated relationships with different types of information. However, in current studies, the attribute weights in belief rule base (BRB) are usually determined by experts or system designers. When the engineering environment changes, the attribute weights can not be updated online and this will lose some environment information. In order to solve this problem, this paper aims to propose a BRB model with online updating attribute weight. For the calculation method, the coefficient of variation-based weighting (CVBW) method has been used to calculate the attribute weight and when the new input data are available, the attribute weight can be updated online. A case study for pipeline leak detection has been studied to validate the efficiency of the online updating attribute weight and the experiment has shown that BRB with online updating attribute weight can estimate the leak size and time of pipeline accurately.
Hidden Behavior Prediction of Complex Systems Under Testing Influence Based on Semiquantitative Information and Belief Rule Base

Compared with the observable behavior, it is difficult to predict the hidden behavior of a complex system. In the existing methods for predicting the hidden behavior, a lot of testing data (usually quantitative information) are needed to be sampled. However, some complex engineering systems have the following characteristics: 1) The systems cannot be tested periodically, and the observable information is incomplete; 2) the change process of hidden behavior may be affected by the test; and 3) only part of quantitative information and qualitative knowledge (i.e., semiquantitative information) may be obtained. These characteristics all related to the test are named as testing influence for simplicity. Although a model and a corresponding optimal algorithm for training the model parameters have been proposed to predict the hidden behavior on the basis of semiquantitative information and belief rule base (BRB), the testing influence has not been considered. In order to solve the above problems, a new BRB-based model, which can use the semiquantitative information, is proposed under testing influence in this paper. In the newly proposed forecasting model, there are some parameters of which the initial values are usually assigned by experts and may not be accurate, which can lead to the inaccurate prediction results. As such, an improved optimal algorithm for training the parameters of the forecasting model is further developed on the basis of the expectation-maximization idea and the covariance matrix adaption evolution strategy (CMA-ES). By using the semiquantitative information, the proposed BRB-based model and the improved CMA-ES algorithm can operate together in an integrated manner so as to improve the forecasting precision. A case study is examined to demonstrate the ability and applicability of the newly proposed BRB-based forecasting model and the improved CMA-ES algorithm.
Halftone visual cryptography

Visual cryptography encodes a secret image SI into n shares of random patterns. If the shares are xeroxed onto transparencies, we can visually decode the secret image by superimposing a qualified subset of transparencies, but no secret information can be obtained from the superposition of a forbidden subset. Such a scheme is mathematically secure, however, it produces random patterns which have no visual meaning, raising the suspicion of data encryption. In this paper, to achieve a higher level of security, we propose halftone visual cryptography, where all shares are halftones of grey level images carrying significant visual information. The proposed methods utilize blue-noise dithering principles to construct halftone shares having visually pleasing attributes.
Refraction and Dispersion Effects Compensation for UWB SAR Subsurface Object Imaging

It is a trend to use airborne or vehicle-borne ultrawideband synthetic aperture radar (UWB SAR) to quickly detect subsurface objects, i.e., landmines or minefields, over large areas from a safe standoff distance. The traditional image formations without considering the refraction and dispersion of electromagnetic wave will lead to locating error and defocusing for subsurface objects. In this paper, an efficient refraction and dispersion effects compensation method is proposed, which can be integrated with the traditional three-stage automatic target detection framework for detection of subsurface landmines. The compensation method can focus and correctly locate multiple objects buried in different soils with different depths over large areas, which satisfies the operating requirement of an airborne or vehicle-borne UWB SAR system. The proposed method is proved efficient for subsurface object imaging by the field data collected by a UWB SAR system.
Arranging and Interpolating Sparse Unorganized Feature Points With Geodesic Circular Arc

A novel method to reconstruct object boundaries with geodesic circular arc is proposed in this paper. Within this framework, an energy of circular arc spline is utilized to simultaneously arrange and interpolate each member in the set of sparse unorganized feature points from the desired boundaries. A general form for a family of parametric circular arc spline is firstly derived and followed by a novel method of arranging these feature points by minimizing an energy term depending on the circular arc spline configuration defined on these feature points. With regard to the fact that the energy function is usually nonconvex and nondifferentiable at its critical points, an improved scheme of particle swarm optimizer is given to find the minimum for the energy in this paper. With this improved scheme, each pair of neighboring feature points along the boundaries of the desired objects are picked out from the set of sparse unorganized feature points, and the corresponding directional chord tangent angles are computed simultaneously to finish interpolation. We show experimentally and comparatively that the proposed method can perform effectively to restrict leakage on weak boundaries and premature convergence on long concave boundaries. Besides, it has good noise robustness and can as well extract multiple and open boundaries.
Semisupervised Regression with Cotraining-Style Algorithms

The traditional setting of supervised learning requires a large amount of labeled training examples in order to achieve good generalization. However, in many practical applications, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semisupervised learning has attracted much attention. Previous research on semisupervised learning mainly focuses on semisupervised classification. Although regression is almost as important as classification, semisupervised regression is largely understudied. In particular, although cotraining is a main paradigm in semisupervised learning, few works has been devoted to cotraining-style semisupervised regression algorithms. In this paper, a cotraining-style semisupervised regression algorithm, that is, COREG, is proposed. This algorithm uses two regressors, each labels the unlabeled data for the other regressor, where the confidence in labeling an unlabeled example is estimated through the amount of reduction in mean squared error over the labeled neighborhood of that example. Analysis and experiments show that COREG can effectively exploit unlabeled data to improve regression estimates.
The application of artificial neural network in HVAC system

Because of the complex calculation for traditional PMV index in HVAC field, the prediction of thermal load is inaccurate, and the PID algorithm can hardly be used to control its system accurately and timely. This article sets forth the theory of artificial neural network (ANN) and its application in this field. Then it introduces the improved BP model used in back-propagation training forward multilevel neural network algorithm, and indicates that the ANN system can be widely used in the HVAC field.
3 Phases-3 Devices AC Voltage Regulator With Quasi-Zero Switching

This paper presents a novel voltage regulator with sine waves, which only consists of 3 switch devices in 3 phases configuration. Simplest chopper control is adopted to operate the system in quasi-zero switch state (quasi-ZCS switching on and quasi-ZVS switching off) by means of reasonable parameter-match without zero-switching branch circuit assisted. Analyses and experiment results are presented to prove the high efficiency and its appliance prospect
Automated Software Testing and Analysis: Techniques, Practices and Tools

As computer systems are permeating our society in daily life and are performing an increasing number of critical tasks, research in software testing and analysis has become of paramount importance. Although we are currently not able to prove program correctness for real-world applications, rigorous software development processes in combination with testing provides us with confidence in the quality of software. Software testing and analysis, however, is a very involved task. As the size and complexity of software continue to grow, manual testing becomes very tedious. Automation of software testing and tool support for testing, therefore, have been emerging as a key technology to quality assurance of today¿s software industry. As research in software testing and analysis has become increasingly active, there is also a growing trend towards combining formal methods and informal techniques for program verification.
A Teaching Model Based on Schema Theory in Data Mining Curriculum

Mining hidden valuable knowledge from financial data is more and more important to students in universities of finance and economics. However, data mining curriculum learning is difficult for them because of the lack of computer special skills. This paper proposes a teaching model based on schema theory to teach data mining curriculum. It introduces characteristics of data mining curriculum, and then it depicts data mining teaching model from teaching and practice perspectives based on schema theory. Teaching experiment shows that there is a significant difference in final exam and practice exam between the proposed model and a regular teaching model.