nils-herrmann/clean_scirepeval huggingface dataset:
Multiprocessor approximate message passing with column-wise partitioning

Solving a large-scale regularized linear inverse problem using multiple processors is important in various real-world applications due to the limitations of individual processors and constraints on data sharing policies. This paper focuses on the setting where the matrix is partitioned column-wise. We extend the algorithmic framework and the theoretical analysis of approximate message passing (AMP), an iterative algorithm for solving linear inverse problems, whose asymptotic dynamics are characterized by state evolution (SE). In particular, we show that column-wise multiprocessor AMP (C-MP-AMP) obeys an SE under the same assumptions when the SE for AMP holds. The SE results imply that (i) the SE of C-MP-AMP converges to a state that is no worse than that of AMP and (ii) the asymptotic dynamics of C-MP-AMP and AMP can be identical. Moreover, for a setting that is not covered by SE, numerical results show that damping can improve the convergence performance of C-MP-AMP.
Monte Carlo Non-Local Means: Random Sampling for Large-Scale Image Filtering

We propose a randomized version of the nonlocal means (NLM) algorithm for large-scale image filtering. The new algorithm, called Monte Carlo nonlocal means (MCNLM), speeds up the classical NLM by computing a small subset of image patch distances, which are randomly selected according to a designed sampling pattern. We make two contributions. First, we analyze the performance of the MCNLM algorithm and show that, for large images or large external image databases, the random outcomes of MCNLM are tightly concentrated around the deterministic full NLM result. In particular, our error probability bounds show that, at any given sampling ratio, the probability for MCNLM to have a large deviation from the original NLM solution decays exponentially as the size of the image or database grows. Second, we derive explicit formulas for optimal sampling patterns that minimize the error probability bound by exploiting partial knowledge of the pairwise similarity weights. Numerical experiments show that MCNLM is competitive with other state-of-the-art fast NLM algorithms for single-image denoising. When applied to denoising images using an external database containing ten billion patches, MCNLM returns a randomized solution that is within 0.2 dB of the full NLM solution while reducing the runtime by three orders of magnitude.
A Hybrid Recognition Scheme Based on Partially Labeled SOM and MLP

We propose a hybrid system for Bangla handwritten numeral recognition based on partially labeled two-layer SOM and MLP classifiers. Partially labeled mechanism is introduced to the Kohonen's SOM for reducing recognition error rate, and two-layer structure is applied for improving the performance of the SOM classifier. The directional and density features are utilized in our system, and the partially labeled SOM is applied first. In the case that the character cannot be recognized by the partially labeled SOM, it will be feed to a multi-layer perceptron classifier for further processing. The experiments on the Bangla handwritten numeral samples captured from real envelopes have found that the hybrid system achieves 96.7% correct recognition rate
On validation of simulation models in timing analysis of complex real-time embedded systems

In this paper, we present work toward validating simulation models extracted from complex real-time embedded systems, from the perspective of response time and execution time of adhering tasks, by using the non-parametric two-sample Kolmogorov-Smirnov test. Moreover, we introduce a method of reducing the number of samples used in the analysis, while keeping the accuracy of results. The evaluation using a fictive but representative system model inspired by a real robotic control system with a set of change scenarios, shows a promising result: the proposed algorithm has the potential of assessing whether the extracted simulation model is a suficiently accurate approximation of the target system.
A distributed Gauss-Newton method for power system state estimation

We propose a fully distributed Gauss-Newton algorithm for state estimation of electric power systems. At each Gauss-Newton iteration, matrix-splitting techniques are utilized to carry out the matrix inversion needed for calculating the Gauss-Newton step in a distributed fashion. In order to reduce the communication burden as well as increase robustness of state estimation, the proposed distributed scheme relies only on local information and a limited amount of information from neighboring areas. The matrix-splitting scheme is designed to calculate the Gauss Newton step with exponential convergence speed. The effectiveness of the method is demonstrated in various numerical experiments.
A powder transport system based on piezoelectric ceramic stack actuators

A novel actuator-driven powder transport system is proposed in this paper. It has two sets of piezoelectric ceramic stacks whose vibrations couple together and make the powder transport pipe do elliptical vibration. The powder on the pipe wall can be thrown up, leading to the relative motions between the powder and pipe wall and thus the powder is transported. To get higher transport efficiency and speed, a displacement amplifier is adopted. The movement of the whole system is analyzed and discussed in theory and the mathematic model is established. The relationship between the input voltage and the output speed is obtained by means of simulation analysis. Theory foundation is provided for further study and practical application.
An Approximate Timing Analysis Framework for Complex Real-Time Embedded Systems

To maintain, analyze and reuse many of today¡¯s Complex Real-Time Embedded Systems (CRTES) is very difficult and expensive, which, nevertheless, offers high business value in response to great concern in industry. In such context, not only functional behavior but also non-functional properties of systems have to be assured, i.e., Worst-Case Response Time (WCRT) of tasks has to be known. However, due to high complexity of such systems and the nature of the problem, the exact WCRT of tasks is impossible to find in practice, but may only be bounded. In addition, the existing relatively well developed theories for modeling and analysis of real-time systems are having problems which limit their application in the context. In this paper, we address this challenge by presenting a framework for approximate timing analysis of CRTES, namely AESIR-CORES, which provides a tight interval of WCRT estimates of tasks by the usage of two novel contributions. Our evaluation using three models inspired by two fictive but representative industrial CRTES indicates that AESIR-CORES can either successfully obtain the actual WCRT values, or have the potential to bound the unknown actual WCRT values from a statistical perspective.
Deadbeat control scheme for three phase current-fed PFC converter with an optimum PWM strategy

A novel deadbeat control of a three-phase current-fed PFC power converter with an optimum PWM pattern is presented to improve its dynamic state performance. In order to improve the utility-side current THD characteristics of this converter, the feasible optimum PWM pattern under conditions of lower switching frequency for large capacity applications is introduced. By using a moving-average that provides a digital filter function for measured variables, the ripple component of carrier frequency can be removed. This deadbeat control scheme with proposed optimum PWM for current-fed three-phase PWM converter could be successfully applied to improve the transient response of utility-power source line current. It is proven from a simulation point of view that sinewave line current shaping can be achieved for a three-phase current source converter operating under unity power factor and proposed control system provides a fast transient response.
Handwritten Bangla digit recognition using hierarchical Bayesian network

A hierarchical Bayesian network is used for handwritten Bangla digit recognition. Rather than extracted feature vectors, original digit images are used as the network input directly. The network is trained on handwritten samples. And then it¿s tested on untrained images and hand-drawn digits. An average recognition accuracy of 87.5 is achieved. The system exhibits robust invariant recognition with respect to considerable object noise which is quite common in handwritten digits.
HoG based two-directional Dynamic Time Warping for handwritten word spotting

We present a Histogram of Oriented Gradient (HoG) based two-directional Dynamic Time Warping (DTW) matching method for handwritten word spotting. Firstly, we extract HoG descriptors from each cell in the normalized images. Then we connect the HoG descriptors in the same column and get a sequence of feature vectors. We do the same operation for the HoG descriptors in the same row. We then apply the two-directional DTW method to calculate the distance between the feature vectors sequences extracted from the query word and the candidate one. The experimental results show that the two-directional DTW is more robust to word deformation than the traditional DTW. And the local features such as HoG, LBP and SIFT combined with the two-directional DTW method outperform the method using the local feature descriptors directly. The HoG based two-directional DTW get the highest mean average precision on both the George Washington dataset and the CASIA-HWDB 2.1 dataset.
Modeling Local Word Spatial Configurations for Near Duplicate Document Image Retrieval

The issue of near duplicate document image retrieval is addressed in this paper, which is characterized by not only encoding each individual word in the image but also modeling its local spatial configuration. On representing each word in the image as a string in terms of its shape characteristics, a lexicon is first learnt from a training set. Then a word in an arbitrary document image can be soft assigned to a weighted combination of several nearest neighbors in the lexicon. The rationale behind soft-assignment is to tolerate the distortions induced by character segmentations which are error-prone in degraded document images. Most importantly, we look beyond the single word and capture the local spatial configuration for each word which plays a very important role in human perception. It provides much useful information in discriminating between different document images compared with the single word. A graph, benefitting from its great representative power, is built for each word to model its relationships with the neighborhoods locally. The local word spatial configurations are integrated within the inverted file index structure to achieve scalable retrieval. Thus the retrieval of near duplicate document images is formulated as a voting problem. Experimental results on 45,000 document images demonstrate that the proposed approach brings significant improvements in successful retrieval of near duplicate images.
Interference suppression method for space-time navigation receivers based on samples selection Householder multistage wiener filter

Due to suppression the influence of impulsive noise spikes (outliers) from desired signal direction of arrival in GPS signals. The Householder multistage wiener filter was improved, a samples selection Householder multistage wiener Alter was proposed. The enhanced algorithm employed by space-time anti-jamming processing in GPS receiver, with the ability of avoiding the impulsive noise spikes influence of weights calculation, and hold the desired signal unchanged. Simulation results indicate the algorithm achieved favorable anti-jamming performance.
Analytical Model for Mesh-Based P2PVoD

Recently, there has been a growing interest in academic and commercial environments for video-on-demand (VoD) using peer-to-peer (P2P) technology. Unlike centralized solutions for VoD services, P2P technology lets the clients distribute video content among themselves. In this paper,we propose an analytical model for P2PVoD and we compare that model to a realistic P2PVoD simulator. With our model, parameters that affect the system performance can be observed, and the system stability can be investigated. Our model leads to design rules for achieving a good and stable system performance. This work is, to our knowledge,the first analytical work to model mesh-based P2PVoD.
Security authentication mechanism based on symmetric polynomials for SCADA systems

With rapid development of information technology, integration of industrialization and information is becoming closer and closer. SCADA (Supervisory Control and Data Acquisition) systems have been widely used in industrial control systems for promoting social productivity efficiently. In recent years, attacks against SCADA systems have caused serious damage and economic loss. Many robust cryptographic mechanisms have been introduced in SCADA systems in order to improve their defense ability of attacks. However, industry control systems have requirements of high availability, real-time and stability. Thus, designing a lightweight cryptographic mechanism is necessary for SCADA systems to have rapid emergency-response and fault-recovery in some critical situations. This paper puts forward an approach of security authentication for SCADA systems which achieves the two-way authentication and ensures the confidentiality of communication between master and slave stations. The proposed approach employs a bivariate symmetric polynomial to obtain the session key by letting master and slave stations' identity values participate in the polynomial calculation. And further the session key is extended to meet the length requirement of AES encryption key. The extended session key is further used to encrypt communication data transmitted in SCADA systems. The proposed lightweight security authentication method is effective in mutual authentication for master and slave stations and in secure communication, and also meet some special requirements of SCADA systems, including fast response in an emergency situation, real-time data communication, and high availability.
Enhanced Selected Mapping Via Candidates Combination for PAPR Reduction in OFDM

A new scheme to increase the number of the candidates without increasing the number of IFFTs in selected mapping (SLM) method is proposed to reduce the peak-to-average power ratio (PAPR) of OFDM signals. The proposed scheme is equivalent to obtaining the new phase sequences via combining the phase sequences in normal SLM, while the newly generated candidates are obtained directly by the suitable combination of the existing candidates. Simulations show that the proposed enhanced SLM (E-SLM) greatly outperforms normal SLM for the same number of IFFTs, and has a little performance loss for the same candidates' number compared to SLM, but ESLM is much less complex.
Finite dimensional FRI

Traditional Finite Rate of Innovation (FRI) theory has considered the problem of sampling continuous-time signals. This framework can be naturally extended to the case where the input is a discrete-time signal. Here we present a novel approach which uses both the traditional FRI sampling scheme, based on the annihilating filter method, and the fact that in this new setup the null space of the problem to be solved is finite dimensional. In the noiseless scenario, we show that this new approach is able to perfectly recover the original signal at the critical sampling rate. We also present simulation results in the noisy scenario where this new approach improves performances in terms of the mean squared error (MSE) of the reconstructed signal when compared to the canonical FRI algorithms and compressed sensing (CS).
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization

In vector space model (VSM), text representation is the task of transforming the content of a textual document into a vector in the term space so that the document could be recognized and classified by a computer or a classifier. Different terms (i.e. words, phrases, or any other indexing units used to identify the contents of a text) have different importance in a text. The term weighting methods assign appropriate weights to the terms to improve the performance of text categorization. In this study, we investigate several widely-used unsupervised (traditional) and supervised term weighting methods on benchmark data collections in combination with SVM and kNN algorithms. In consideration of the distribution of relevant documents in the collection, we propose a new simple supervised term weighting method, i.e. tf.rf, to improve the terms' discriminating power for text categorization task. From the controlled experimental results, these supervised term weighting methods have mixed performance. Specifically, our proposed supervised term weighting method, tf.rf, has a consistently better performance than other term weighting methods while other supervised term weighting methods based on information theory or statistical metric perform the worst in all experiments. On the other hand, the popularly used tf.idf method has not shown a uniformly good performance in terms of different data sets.
An environment-driven ontological approach to requirements elicitation for safety-critical systems

The environment, where a safety critical system (SCS) operates, is an important source from which safety requirements of the SCS can originate. By treating the system under construction as a black box, the environment is typically documented as a number of assumptions, based on which a set of environmental safety requirements will be elicited. However, it is not a trivial task in practice to capture the environmental assumptions to elicit safety requirements. The lack of certain assumptions or too strict assumptions will either result in incomplete environmental safety requirements or waste many efforts on eliciting incorrect requirements. Moreover, the variety of operating environment for an SCS will further complicate the task, since the captured assumptions are at risk of invalidity, and consequently the elicited requirements need to be revisited to ensure safety has not been compromised by the change. This short paper presents an on-going work aiming to 1) systematically organize the knowledge of system operating environment and, 2) facilitate the elicitation of environmental safety requirements. We propose an ontological approach to achieve the objectives. In particular, we utilize conceptual ontologies to organize the environment knowledge in terms of relevant environment concepts, relations among them and axioms. Environmental assumptions are captured by instantiating the environment ontology. An ontological reasoning mechanism is also provided to support elicitation of safety requirements from the captured assumptions.
An Algorithm for License Plate Recognition Applied to Intelligent Transportation System

An algorithm for license plate recognition (LPR) applied to the intelligent transportation system is proposed on the basis of a novel shadow removal technique and character recognition algorithms. This paper has two major contributions. One contribution is a new binary method, i.e., the shadow removal method, which is based on the improved Bernsen algorithm combined with the Gaussian filter. Our second contribution is a character recognition algorithm known as support vector machine (SVM) integration. In SVM integration, character features are extracted from the elastic mesh, and the entire address character string is taken as the object of study, as opposed to a single character. This paper also presents improved techniques for image tilt correction and image gray enhancement. Our algorithm is robust to the variance of illumination, view angle, position, size, and color of the license plates when working in a complex environment. The algorithm was tested with 9026 images, such as natural-scene vehicle images using different backgrounds and ambient illumination particularly for low-resolution images. The license plates were properly located and segmented as 97.16% and 98.34%, respectively. The optical character recognition system is the SVM integration with different character features, whose performance for numerals, Kana, and address recognition reached 99.5%, 98.6%, and 97.8%, respectively. Combining the preceding tests, the overall performance of success for the license plate achieves 93.54% when the system is used for LPR in various complex conditions.
Content-based retrieval of polyphonic music objects using pitch contour

This paper investigates the retrieval of content-based polyphonic music objects in Wav and MP3 format. The system allows user to find an intended song by humming or singing a section of it. In this paper we introduce the baseline system and describe the key components including the pitch extraction in humming/singing clip, the vocal/non-vocal music segmentation, the pitch tracking in polyphonic music, and the DTW based matching algorithm. We conducted evaluations on the system. The experimental results demonstrate the feasibility of retrieving polyphonic music objects by humming/singing.
Distributed Sampling of Signals Linked by Sparse Filtering: Theory and Applications

We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin to a Slepian-Wolf setup, but in the sampling rather than the lossless compression case. Two different scenarios are considered: In the case of universal reconstruction, we look for a sensing and recovery mechanism that works for all possible signals, whereas in what we call almost sure reconstruction, we allow to have a small set (with measure zero) of unrecoverable signals. We derive achievability bounds on the number of samples needed for both scenarios. Our results show that, only in the almost sure setup can we effectively exploit the signal correlations to achieve effective gains in sampling efficiency. In addition to the above theoretical analysis, we propose an efficient and robust distributed sampling and reconstruction algorithm based on annihilating filters. We evaluate the performance of our method in one synthetic scenario, and two practical applications, including the distributed audio sampling in binaural hearing aids and the efficient estimation of room impulse responses. The numerical results confirm the effectiveness and robustness of the proposed algorithm in both synthetic and practical setups.
Online learning for sparse PCA in high dimensions: Exact dynamics and phase transitions

We study the dynamics of an online algorithm for learning a sparse leading eigenvector from samples generated from a spiked covariance model. This algorithm combines the classical Oja's method for online PCA with an element-wise nonlinearity at each iteration to promote sparsity. In the high-dimensional limit, the joint empirical measure of the underlying sparse eigenvector and its estimate provided by the algorithm is shown to converge weakly to a deterministic, measure-valued process. This scaling limit is characterized as the unique solution of a nonlinear PDE, and it provides exact information regarding the asymptotic performance of the algorithm. For example, performance metrics such as the cosine similarity and the misclassification rate in sparse support recovery can be obtained by examining the limiting dynamics. A steady-state analysis of the nonlinear PDE also reveals an interesting phase transition phenomenon. Although our analysis is asymptotic in nature, numerical simulations show that the theoretical predictions are accurate for moderate signal dimensions.