nils-herrmann/clean_scirepeval huggingface dataset:
A Metaheuristic Approach for Best Effort Timing Analysis Targeting Complex Legacy Real-Time Systems

Many companies developing real-time systems today have today no means for response time analysis, as their systems violate the assumptions of traditional analytical methods for response-time analysis and are too complex for exhaustive analysis using model checking. This paper presents a novel approach for best effort response time analysis targeting such systems, where probabilistic simulation is guided by a search algorithm of metaheuristic type, similar to genetic algorithms. The best effort approach means that the result is not guaranteed to be the worst-case response time, but also that the method scales to large industrial systems. The proposed method should be regarded as a form of testing, focusing on timing properties. An evaluation is presented which indicates that the proposed approach is significantly more efficient than traditional probabilistic simulation in finding extreme task response times. The paper also presents a method for finding good parameters for the search algorithm, in order to improve its efficiency.
Correlation-based joint acquisition and demosaicing of visible and near-infrared images

Joint processing of visible (RGB) and near-infrared (NIR) images has recently found some appealing applications, which make joint capturing a pair of visible and NIR images an important problem. In this paper, we propose a new method to design color filter arrays (CFA) and demosaicing matrices for acquiring NIR and visible images using a single sensor. The proposed method modifies the optimum CFA algorithm proposed in  by taking advantage of the NIR/visible correlation in the design process. Simulation results show that by applying the proposed method, the quality of demo-saiced NIR and visible images is increased by about 1 dB in peak signal-to-noise ratio over the results of the optimum CFA algorithm. It is also shown that better visual quality can be obtained by using the proposed algorithm.
Three Convolutional Neural Network-based models for learning Sentiment Word Vectors towards sentiment analysis

With the development of deep learning, word vectors (i.e., word embeddings) have been extensively explored and applied to many Natural Language Processing tasks (e.g., parsing, Named Entity Recognition, etc). However, the semantic word vectors learned from context have insufficient sentiment information for performing sentiment analysis at different text levels. In this work, we present three Convolutional Neural Network (CNN)-based models to learn sentiment word vectors (SWV), which integrate sentiment information with semantic and syntactic information into word representations in three different strategies. Experimental results on benchmark datasets showed that sentiment word vectors are able to capture both sentiment and semantic information and outperform semantic word vectors for word-level and sentence-level sentiment analysis. Moreover, in combination with traditional NLP features, the sentiment word vectors achieve the best performance so far.
6.2 A 60Gb/s 288mW NRZ transceiver with adaptive equalization and baud-rate clock and data recovery in 65nm CMOS technology

The demand for ultra-high speed transceivers continues to explode, and while the data-rate for high-speed I/O standards has increased accordingly, the historically constant or even decreasing power budgets available for the transceivers push these designs to be extremely energy efficient. To begin addressing this need, recently published NRZ transceivers operating up to 56.5Gb/s with a 1-tap DFE and/or CTLE  in 28–40nm CMOS processes have been demonstrated for <20dB loss channels with energy efficiencies ranging from 4.4 to 11.96pJ/b. In order to support channels with higher losses, the half-rate 65nm receiver front-end design described in  includes a 3-tap DFE, a 2-tap FFE, and a CTLE at ∼2.88pJ/b, but neither closed-loop equalizer adaptation nor clock and data recovery were demonstrated. Closing these loops is especially critical in such an interleaved architecture, since at these data-rates each interleaved datapath can result in a noticeably different overall channel response (ISI), and the need for closed-loop CDR is self-evident. This paper therefore presents a fully adaptive 60Gb/s transceiver, supporting >20dB channel loss, implemented in a 65nm CMOS technology . The full transceiver (including baud-rate CDR) achieves 60Gb/s and 4.8pJ/b energy efficiency over a 0.7m Twinax cable.
Graph-based regularization for color image demosaicking

We present a novel regularization framework for demosaicking by viewing image as smooth signal on a weighted graph. The restoration problem is formed as a minimization of variation of the signal on graph. As an initial step, we build a weight matrix which measures the similarity between every pair of pixels, from an estimate of the full color image. After that, a two-stage optimization is carried out: first, we assume that graph Laplacian is signal dependent and solve a non-quadratic problem by gradient descent; then, we pose a variational problem on graph with a static Laplacian, under the constraint of consistency with the available samples in each color component. Performance evaluation shows that our approach can improve the previous demosaicking methods both quantitively and visually, by alleviating the artifical effect. Moreover, the mapping from image to signal on graph provides a general method for image processing.
A Semi-supervised Method for Classification of Semantic Relation between Nominals

The task of classifying the semantic relation between two nominals in a sentence is quite challenging due to lack of a large amount of labeled data. Existing models of semantic relation classification were built on either synthetic training data generated from unlabeled data or hand-annotated training data. Meanwhile, previous work showed that the preposition and verb in the sentences indicate important clues to discover the semantic relation between nominals. In this paper we attempt to exploit both labeled and unlabeled data for semantic relation classification under the framework of semi-supervised multi-task learning. Specifically, to improve the generalization performance of a semantic relation classification model, we leverage the information contained in the training signals of multiple related tasks, e.g, prediction of preposition and verb labels. Results on SemEval 2007 task 4 and SemEval 2010 task 8 indicate that semi-supervised multi-task learning method can help semantic relation classification, resulting in comparable or even better performance than the state of art systems in SemEval 2007 task 4.
Fabrication of Capacitive Frisch Grid CZT Detector

The weighting potential distribution on a capacitive Frisch grid CZT detector and fabrication processing of the device have been investigated in this paper. The simulation results show that a bigger ratio of L/H helps to compress the weighting potential and the primary experimental results show that FWHM of the energy spectra of 241 Am increases by 4% when the L/H of the device increases from 50% to 100% and the capacitive Frisch grid can largely improve the detector performance, compared with the conventional planar geometry radiation detectors
ALARM: A logistic auto-regressive model for binary processes on networks

We introduce the ALARM model, a logistic autoregressive model for discrete-time binary processes on networks, and describe a technique for learning the graph structure underlying the model from observations. Using only a small number of parameters, the proposed ALARM can describe a wide range of dynamic behavior on graphs, such as the contact process, voter process, and even some epidemic processes. Under ALARM, at each time step, the probability of a node having value 1 is determined by the values taken by its neighbors in the past; specifically, its probability is given by the logistic function evaluated at a linear combination of its neighbors' past values (within a fixed time window) plus a bias term. We examine the behavior of this model for 1D and 2D lattice graphs, and observe a phase transition in the steady state for 2D lattices. We then study the problem of learning a graph from ALARM observations. We show how a regularizer promoting group sparsity can be used to efficiently learn the parameters of the model from a realization, and demonstrate the resulting ability to reconstruct the underlying network from the data.
Error-free near-threshold adiabatic CMOS logic in presence of process variation

This paper provides the first analysis of process variation effect on the adiabatic logic combined with nearthreshold operation. One of the significant concerns is whether reliable performance is retained. We find that typical variations of process parameters do not affect error-free operation at the minimum-energy frequency. Monte Carlo simulations of a 4- bit full adder using ECRL logic with 0.45 V supply voltage show that, in the presence of typical process variations, energy consumption of the circuit operating at 25 MHz increases by 10.2% in the worst case while a 100% error-free operation is maintained. The maximum operating frequency(208 MHz) is reduced to nearly half of the nominal value(385 MHz). To further improve the robustness of the adder against process variation, a bit-serial adiabatic adder is considered with an even lower energy consumption per cycle.
Investigation of control and applications of modular multilevel converter with sub-modular series IGBTs

Modular Multilevel Converter (MMC) has been an attractive option for power transmission and has been studied and used for HVDC applications. However, it still has limitations when the number of Sub-Modules per arm is large. To avoid the limitations and exploit the advantages, a method is proposed in this paper which integrates an MMC with series-connected IGBTs in each Sub-Module (SM). The proposed MMC is able to withstand a higher voltage with devices of lower voltage ratings. The series-connected IGBTs are self-balancing. Simulink and real-time simulations both show that, with the proposed method, series IGBTs are able to share a much higher MMC sub-module voltage and recover from voltage imbalances in a timely manner. The proposed series IGBT balancing method causes considerably less loss compared to RCD snubber circuit-based balancing methods.
Face Recognition Using Scale Invariant Feature Transform and Support Vector Machine

Face recognition has received significant attention in the last decades for many potential applications. Recently, the scale invariant feature transform (SIFT) becomes an interesting technique for the task of object recognition. This paper investigated the application of the SIFT approach to the face recognition and proposed a new method based on SIFT and support vector machine (SVM) for the face recognition problem. First the SIFT features are generated and then SVM is used for the classification. The presented method has been tested with the ORL database and the Yale face database, and the recognition results demonstrate its robust performance under different expression conditions.
A transconductor-capacitor video filter and equaliser design

A design technique for a ladder-based transconductor-capacitor (transconductor-C) lowpass video filter and equalizer is presented. The software package for a general transconductor-C filter and equalizer design is described. A fifth-order elliptic lowpass filter and a sixth-order group delay equalizer are designed. It is shown that the order of the equalizer can be reduced by the application of special techniques for the approximation of the filter transfer function. The comparison of sensitivity between a cascade of biquadratic stages and the ladder-based realization is given. Lower sensitivities to component tolerance for both filter and equalizer are displayed by the ladder-based design. The fifth-order amplitude and sixth-order group delay equalizer are incorporated in a layout for fabrication.<<ETX>>
Opportunistic forwarding in energy harvesting mobile delay tolerant networks

Opportunistic forwarding assisted by mobile relays is an effective way of improving network capacity and packet delivery ratio in delay tolerant networks (DTNs). However, such performance gain comes at the price of increased energy consumption due to the duplicated transmissions at relays. In this paper, we investigate how energy harvesting, a promising technique of enabling sustainable communications, can be exploited to improve the performance of opportunistic forwarding in mobile DTNs. Specifically, we formulate the problem using a Markov Decision Process (MDP) framework in which each source should strike a balance between exploitation, by forwarding the packet to the relay currently in contact, and exploration, by waiting for possible better relays in the future, given the harvested energy constraint. The formulated MDP having exponential complexity, we devise a heuristic relay-assisted opportunistic forwarding scheme, termed as adaptive M-step lookahead scheme, to alleviate the computation complexity, where M can be adjusted adaptively according to both the current energy and the energy that might be harvested in the future. Simulation results show that our proposed algorithm can use the harvested energy more efficiently, especially for the circumstance where the energy harvesting rate is low.
Randomized Kaczmarz algorithms: Exact MSE analysis and optimal sampling probabilities

The Kaczmarz method, or the algebraic reconstruction technique (ART), is a popular method for solving large-scale overdetermined systems of equations. Recently, Strohmer et al. proposed the randomized Kaczmarz algorithm, an improvement that guarantees exponential convergence to the solution. This has spurred much interest in the algorithm and its extensions. We provide in this paper an exact formula for the mean squared error (MSE) in the value reconstructed by the algorithm. We also compute the exponential decay rate of the MSE, which we call the "annealed" error exponent. We show that the typical performance of the algorithm is far better than the average performance. We define the "quenched" error exponent to characterize the typical performance. This is far harder to compute than the annealed error exponent, but we provide an approximation that matches empirical results. We also explore optimizing the algorithm's row-selection probabilities to speed up the algorithm's convergence.
Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective

Many patch-based image denoising algorithms can be formulated as applying a smoothing filter to the noisy image. Expressed as matrices, the smoothing filters must be row normalized, so that each row sums to unity. Surprisingly, if we apply a column normalization before the row normalization, the performance of the smoothing filter can often be significantly improved. Prior works showed that such performance gain is related to the Sinkhorn–Knopp balancing algorithm, an iterative procedure that symmetrizes a row-stochastic matrix to a doubly stochastic matrix. However, a complete understanding of the performance gain phenomenon is still lacking. In this paper, we study the performance gain phenomenon from a statistical learning perspective. We show that Sinkhorn–Knopp is equivalent to an expectation-maximization (EM) algorithm of learning a Gaussian mixture model of the image patches. By establishing the correspondence between the steps of Sinkhorn–Knopp and the EM algorithm, we provide a geometrical interpretation of the symmetrization process. This observation allows us to develop a new denoising algorithm called Gaussian mixture model symmetric smoothing filter (GSF). GSF is an extension of the Sinkhorn–Knopp and is a generalization of the original smoothing filters. Despite its simple formulation, GSF outperforms many existing smoothing filters and has a similar performance compared with several state-of-the-art denoising algorithms.
Blind calibration of multi-channel samplers using sparse recovery

We propose an algorithm for blind calibration of multi-channel samplers in the presence of unknown gains and offsets, which is useful in many applications such as multi-channel analog-to-digital converters, image super-resolution, and sensor networks. Using a subspace-based rank condition developed by Vandewalle et al., we obtain a set of linear equations with respect to complex harmonics whose frequencies are determined by the offsets, and the coefficients of each harmonic are determined by the discrete-time Fourier transforms of outputs of each of the channels. By discretizing the offsets over a fine grid, this becomes a sparse recovery problem where the signal of interest is sparse with an additional structure, that in each block there is only one nonzero entry. We propose a modified CoSaMP algorithm that takes this structure into account to estimate the offsets. Our algorithm is scalable to large numbers of channels and can also be extended to multi-dimensional signals. Numerical experiments demonstrate the effectiveness of the proposed algorithm.
Discriminative common vectors based on the Gram-Schmidt reorthogonalization for the small sample size problem

The discriminative common vectors (DCV) algorithm shows better face recognition effects than some commonly used linear discriminant algorithms, which uses the subspace methods and the Gram-Schmidt orthogonalization (GSO) procedure to obtain the DCV. However, the Gram-Schmidt technique may produce a set of vectors which is far from orthogonal so that sometimes the orthogonality may be lost completely. Hence, the effectiveness of the DCV is also decreased. In this paper, we proposed an improved DCV method based on the GSO. For obtaining an accurate projection onto the corresponding space, the orthogonal basis problem is usually solved with the Gram-Schmidt process with reorthogonalization. Thus, the effectiveness of the DCV can be improved and the experimental results show that the proposed method is better for the small sample size problem as compared to the DCV.
Compact LTCC filter with end-coupled resonators

A compact narrow-band low temperature co-fired ceramic (LTCC) filter with a fractional bandwidth of 3% is proposed. The proposed filter consists of five cascaded half-wavelength resonators vertically arrayed on each LTCC layer. End-coupling between adjacent resonators is precisely controlled by rectangle-shaped slot on intermediate ground layer. The overall size of the filter is only 8 × 6 × 0.89 mm and a size reduction of 80% is achieved compared with a planar one. Measured S11 and S21 are better than −14 and −2.5 dB in the passband, respectively.
An approach to word image matching based on weighted Hausdorff distance

An approach to word image matching based on weighted Hausdorff distance (WHD) is proposed in this paper to facilitate the detection and location of the user-specified words in the document images. Preprocessing such as eliminating the space between adjacent characters in the word images and scale normalization is first done before the WHD is utilized to measure the distance between the template image and the word image extracted from the document image. Experimental results in the application of detecting the user-specified words from both English and Chinese document images show that it is a promising approach for word image matching.
A systematic approach for ladder based switched current filter design

A systematic approach for ladder based switched-current (SI) filter design is described. Low sensitivity and low parameter spread SI circuits realised by both existing and novel structures can be derived from the proposed approach. The method can be applied to all the filter types and is well suited for computer aided implementation. Techniques to improve dynamic range and reduce circuit parameter spread are also presented. A 10th-order bandpass filter is used to demonstrate the utility of the approach.
Sampling spherical finite rate of innovation signals

We propose a sampling scheme that can perfectly reconstruct a collection of spikes on the sphere from samples of their low-pass filtered observations. The proposed algorithm can reconstruct K spikes from (K + √K)2 spatial samples, thus improving over previously known FRI sampling schemes on the sphere by a factor of up to four. Further, we show how multiple sound source localization (SSL) by a spherical microphone array can be transformed into a spherical FRI sampling problem. We certify the effectiveness of the proposed algorithm by using it to solve the SSL problem.
Feature Point Analysis for Image Spam E-Mail Detection

Image-based spam is becoming a new threat to the Internet and its users. In our early work, we proposed an image filtering system which detects the spam image by matching with user-specified image content using SIFT algorithm. In order to further improve efficiency, we develop a quick image matching algorithm instead of SIFT. After using difference-of-Gaussian to extract image feature points, we adopt geometry transform to judge whether two images are matched. Experimental results show that the proposed method can identify image spam without the need of OCR and it can achieve a good performance. In addition, we adopt Mean Shift algorithm to locate the highest density area of feature points, which improves the performance of the system.
Integrated tone evaluation in Mandarin CALL systems using competing model based approach

Mandarin is known as a tonal language, mainly because its tone is a distinctive discriminative feature. This paper describes an attempt to automate the tone evaluation for continuous Mandarin speech in CALL systems. A competing model based integrated evaluation approach is introduced to obtain objective tone evaluation results. The evaluation includes two parts: tonal syllable evaluation and tone evaluation. In tonal syllable evaluation, we generate the syllable-based competing models by using the simplified linguistic knowledge based initial/final net. Then the competing model based approach is introduced to get tonal syllable score. In tone evaluation, an HMM forced alignment based tone model is used to get tone score for Mandarin syllables. Finally, we integrate tonal syllable score and tone score together to acquire the overall tone scoring results. The experimental results demonstrate that this proposed method, using forced alignment based tone model and competing models based tone evaluation, gives an accurate tone assessment.
Peak-to-average power ratio reduction in OFDM using cyclically shifted phase sequences

Two approaches for reducing peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) are proposed that are relied on a set of cyclically shifted phase sequences (CSPS) and implemented using the time domain circular convolution. After multiplying CSPS with the frequency domain data, the signal candidates can be expressed as weighted sum of the circularly shifted OFDM time domain data in the first method, which is called CSPS method. In the second method, weighted coefficients for generating the signal candidates in CSPS method are optimally selected to improve its performance; thus, the second method is referred to as optimised CSPS (OCSPS) method. The performances of the CSPS and OCSPS methods are evaluated using simulated data and compared with those of selective mapping (SLM) and partial transmit sequences (PTS). The simulation results show that both the CSPS and OCSPS methods can reduce the PAPR effectively, and that the OCSPS performs even better than the CSPS. The OCSPS can achieve the same performance as compared to the PTS. A distinct feature of the proposed methods is that only one inverse discrete Fourier transform is needed, and thus, the candidates can be calculated in time domain directly.