Reviewerly/exHarmony huggingface dataset:
Time Frequency Analysis of Wavelet and Fourier Transform

Signal processing has long been dominated by the Fourier transform. However, there is an alternate transform that has gained popularity recently and that is the wavelet transform. The wavelet transform has a long history starting in 1910 when Alfred Haar created it as an alternative to the Fourier transform. In 1940 Norman Ricker created the first continuous wavelet and proposed the term wavelet. Work in the field has proceeded in fits and starts across many different disciplines, until the 1990’s when the discrete wavelet transform was developed by Ingrid Daubechies. While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.
Scale‐aware limited deformable convolutional neural networks for traffic sign detection and classification

Traffic sign detection and classification is a critical component of intelligent transportation systems, which is applied to inform automatic unmanned driving systems and driving assistance systems about conditions and limits of roads. Although computer vision is widely utilised in traffic sign detection, detection and recognising traffic signs globally remains a great challenge due to the variety of sign types, scale-variance and geometric variations. To address these problems, this study proposes a region-based deep convolutional neural network (CNN) framework for traffic sign detection and classification. Specifically, a multi-branch sample pyramid module is proposed, which is based on multi-branch CNNs for multi-scaled feature exaction. A limited deformable convolutional module is then embedded into the CNN layers to learn the distorted information representation for deformation handing. Moreover, a scale-aware multi-task region proposal network module is applied to detect traffic signs with various scales. The whole network is trained in an end-to-end manner. Finally, experiments are conducted on two public detection data sets to demonstrate the effectiveness of the proposed method.
Delving deep into the imbalance of positive proposals in two-stage object detection

Imbalance issue is a major yet unsolved bottleneck for the current object detection models. In this work, we observe two crucial yet never discussed imbalance issues. The first imbalance lies in the large number of low-quality RPN proposals, which makes the R-CNN module (i.e., post-classification layers) become highly biased towards the negative proposals in the early training stage. The second imbalance stems from the unbalanced ground-truth numbers across different testing images, resulting in the imbalance of the number of potentially existing positive proposals in testing phase. To tackle these two imbalance issues, we incorporates two innovations into Faster R-CNN: 1) an R-CNN Gradient Annealing (RGA) strategy to enhance the impact of positive proposals in the early training stage. 2) a set of Parallel R-CNN Modules (PRM) with different positive/negative sampling ratios during training on one same backbone. Our RGA and PRM can totally bring 2.0% improvements on AP on COCO minival. Experiments on CrowdHuman further validates the effectiveness of our innovations across various kinds of object detection tasks.
Adaptation of Gradient-Based Navigation Control for Holonomic Robots to Nonholonomic Robots

In this letter, we propose a gradient-based nonlinear control approach for stabilizing a nonholonomic Wheeled Mobile Robot (WMR) to a target position in environments with and without obstacles. This approach enables any gradient-based feedback control law (with bounded or unbounded gradients) developed for a holonomic point-mass robot model to be adapted to control a nonholonomic robot. The proposed controller is defined in terms of smooth continuous functions, which produce smooth robot trajectories and can be tuned to stabilize the robot to the goal position at a desired convergence rate. We first prove that the controller will stabilize a nonholonomic robot to a target point in an obstacle-free environment. To stabilize the robot's position in environments with obstacles, we modify our controller to utilize the gradient of an artificial potential function and use Lyapunov stability theory to prove that the robot is guaranteed to converge to the target position under this controller. We demonstrate the effectiveness of our controller for various initial robot positions and environments, and two types of potential fields that are widely used in gradient-based methods for obstacle avoidance, through MATLAB simulations and experiments with a commercial nonholonomic WMR.
Cross-Domain Scene Classification by Integrating Multiple Incomplete Sources

Cross-domain scene classification identifies scene categories by learning knowledge from a labeled data set (source domain) to an unlabeled data set (target domain), where the source data and the target data are sampled from different distributions. A lot of domain adaptation methods are used to reduce the distribution shift across domains, and most existing methods assume that the source domain shares the same categories with the target domain. It is usually hard to find a source domain that covers all categories in the target domain. Some works exploit multiple incomplete source domains to cover the target domain. However, in such setting, the categories of each source domain are a subset of the target-domain categories, and the target domain contains “unknown” categories for each source domain. The existence of unknown categories results in the conventional domain adaptation unsuitable. Known and unknown categories should be treated separately. Therefore, a separation mechanism is proposed to separate the known and unknown categories in this article. First, multiple-source classifiers trained on the multiple source domains are used to coarsely separate the known/unknown categories in the target domain. The target images with high similarities to source images are selected as known categories, and the target images with low similarities are selected as unknown categories. Then, a binary classifier trained using the selected images is used to finely separate all target-domain images. Finally, only the known categories are implemented in the cross-domain alignment and classification. The target images get labels by integrating the hypotheses of multiple-source classifiers on the known categories. Experiments are conducted on three cross-domain data sets to demonstrate the effectiveness of the proposed method.
Quantum gate verification and its application in property testing

To guarantee the normal functioning of quantum devices in different scenarios, appropriate benchmarking tool kits are quite significant. Inspired by the recent progress on quantum state verification, here we establish a general framework of verifying a target unitary gate. In both the non-adversarial and adversarial scenarios, we provide efficient methods to evaluate the performance of verification strategies for any qudit unitary gate. Furthermore, we figure out the optimal strategy and its realization with local operations. Specifically, for the commonly-used quantum gates like single qubit and qudit gates, multi-qubit Clifford gates, and multi-qubit Controlled-Z(X) gates, we provide efficient verification protocols. Besides, we discuss the application of gate verification for the detection of entanglement-preserving property of quantum channels and further quantify the robustness measure of them. We believe that the gate verification is a promising way to benchmark a large-scale quantum circuit as well as to test its property.
ALSA: Adversarial Learning of Supervised Attentions for Visual Question Answering

Visual question answering (VQA) has gained increasing attention in both natural language processing and computer vision. The attention mechanism plays a crucial role in relating the question to meaningful image regions for answer inference. However, most existing VQA methods: 1) learn the attention distribution either from free-form regions or detection boxes in the image, which is intractable in answering questions about the foreground object and background form, respectively and 2) neglect the prior knowledge of human attention and learn the attention distribution with an unguided strategy. To fully exploit the advantages of attention, the learned attention distribution should focus more on the question-related image regions, such as human attention for both the questions, about the foreground object and background form. To achieve this, this article proposes a novel VQA model, called adversarial learning of supervised attentions (ALSAs). Specifically, two supervised attention modules: 1) free form-based and 2) detection-based, are designed to exploit the prior knowledge for attention distribution learning. To effectively learn the correlations between the question and image from different views, that is, free-form regions and detection boxes, an adversarial learning mechanism is implemented as an interplay between two supervised attention modules. The adversarial learning reinforces the two attention modules mutually to make the learned multiview features more effective for answer inference. The experiments performed on three commonly used VQA datasets confirm the favorable performance of ALSA.
Multi-Camera Color Correction via Hybrid Histogram Matching

With the explosive popularity of multi-camera systems, a challenging problem is how to guarantee the inter-camera color consistency. In this paper, we have developed a hybrid histogram matching (HHM) algorithm that leverages the cumulative color histogram to fulfill this purpose, in which it mainly includes the global color mapping, and local color straightening for uniform color presentation across cameras. Our approach is applicable to any camera pair with sufficient viewing angle overlap, and can be easily extended to support multi-camera setup by pairwisely color map transfer. This HHM method has provided the superior efficiency for color correction when compared with the state-of-the-art algorithms, both objectively and subjectively, via extensive validations on different multi-camera systems. Additional ablation studies have been performed to further examine the capacity of our algorithm in practical applications. We would like to make all materials publicly accessible at https://njuvision.github.io/HHM/ for reproducible research.
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)

Over the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc) or the acquisition technology, which might be unrelated with the target of the analysis. In supervised tasks, failing to match the ground truth targets with respect to such characteristics, called confounders, may lead to very misleading estimates of the predictive performance. Many strategies have been proposed to handle confounders, ranging from data selection, to normalization techniques, up to the use of training algorithm for learning with imbalanced data. However, all these solutions require the confounders to be known a priori. To this aim, we introduce a novel index that is able to measure the confounding effect of a data attribute in a bias-agnostic way. This index can be used to quantitatively compare the confounding effects of different variables and to inform correction methods such as normalization procedures or ad-hoc-prepared learning algorithms. The effectiveness of this index is validated on both simulated data and real-world neuroimaging data.
Improving Accuracy and Speeding Up Document Image Classification Through Parallel Systems

This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions. We show in the RVL-CDIP dataset that we can improve previous results with a much lighter model and present its transfer learning capabilities on a smaller in-domain dataset such as Tobacco3482. Moreover, we present an ensemble pipeline which is able to boost solely image input by combining image model predictions with the ones generated by BERT model on extracted text by OCR. We also show that the batch size can be effectively increased without hindering its accuracy so that the training process can be sped up by parallelizing throughout multiple GPUs, decreasing the computational time needed. Lastly, we expose the training performance differences between PyTorch and Tensorflow Deep Learning frameworks.
Graph-Embedding Empowered Entity Retrieval

In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings. The paper shows that graph embeddings are useful for entity-oriented search tasks. We demonstrate empirically that encoding information from the knowledge graph into (graph) embeddings contributes to a higher increase in effectiveness of entity retrieval results than using plain word embeddings. We analyze the impact of the accuracy of the entity linker on the overall retrieval effectiveness. Our analysis further deploys the cluster hypothesis to explain the observed advantages of graph embeddings over the more widely used word embeddings, for user tasks involving ranking entities.
Robust hierarchical feature selection driven by data and knowledge

Feature selection is facing great challenges brought by the enlarging label space and the inevitable noisy data. Flat feature selection methods fail to obtain a compact feature subset because of the numerous classes. In addition, these data-driven methods are sensitive to the data outliers. Fortunately, many practical tasks usually organize the classes by a hierarchical structure in a coarse-to-fine manner and can be solved by using the divide-and-conquer strategy. In this paper, we propose a hierarchical feature selection method driven by data and knowledge (HFSDK), which is robust to the data outliers and produces compact feature subsets by splitting the original large label space. Firstly, HFSDK decomposes a large-scale classification task into a group of small subclassification tasks with multiple granularities, which is driven by knowledge of the hierarchical class structure. Then, the corresponding datasets are constructed from the bottom to the top using the class labels of data, which is a data-driven process. Finally, robust and discriminative feature subsets are selected recursively for those subtasks by eliminating the data outliers and adding a semantic relation constraint. Experiments on six real-world datasets validate the superior performance of the proposed method.
Imputation of clinical covariates in time series

Missing data is a common problem in longitudinal datasets which include multiple instances of the same individual observed at different points in time. We introduce a new approach, MedImpute, for imputing missing clinical covariates in multivariate panel data. This approach integrates patient specific information into an optimization formulation that can be adjusted for different imputation algorithms. We present the formulation for a K-nearest neighbors model and derive a corresponding scalable first-order method med.knn. Our algorithm provides imputations for datasets with both continuous and categorical features and observations occurring at arbitrary points in time. In computational experiments on three real-world clinical datasets, we test its performance on imputation and downstream predictive tasks, varying the percentage of missing data, the number of observations per patient, and the mechanism of missing data. The proposed method improves upon both the imputation accuracy and downstream predictive performance relative to the best of the benchmark imputation methods considered. We show that this edge is consistently present both in longitudinal and electronic health records datasets as well as in binary classification and regression settings. On computational experiments on synthetic data, we test the scalability of this algorithm on large datasets, and we show that an efficient method for hyperparameter tuning scales to datasets with 10,000's of observations and 100's of covariates while maintaining high imputation accuracy.
Cryptanalzing a Novel Hyper-Chaotic Image Encryption Scheme Based on Pixel-Level Filtering and DNA-Level Diffusion

As we know, improving security is one of the most important motivations for designing new image encryption schemes.However, the security of some new image encryption schemes has not been fully analyzed and evaluated.In view of this, a novel image encryption scheme based on the pixel-level filtering and the DNA-level diffusion (PFDD) is analyzed and evaluated in this paper.After comprehensive analysis and study, we find that PFDD does not have its claimed ability to resist chosen-plaintext attacks (CPAs).Therefore, under the conditions of CPAs, we cryptanalyze the entire encryption process of PFDD, and successfully crack it by our proposed attack algorithm.Then, The effectiveness and feasibility of the proposed attack algorithm are verified theoretically and experimentally.Finally, for several representative problems in current chaotic image encryption schemes, we put forward some improvement suggestions for future researchers.
Disaggregation of household solar energy generation using censored smart meter data

Quantifying small scale domestic solar (PV) generation from energy consumption is becoming increasingly important as the install base of small solar (PV) panels rapidly grows. Unfortunately, it is often the case that the only insight into the consumption and generation of energy within a house comes from smart-meter readings. The smart meter records the amount of energy the house takes from the grid, and does not independently measure and report the local generation that might be consumed by the home, or fed back to the grid. To address this issue, we propose a novel approach to disaggregate PV generation from energy consumption that also infers installed PV capacity. This is done by disaggregating PV generation from censored smart meter readings, and specifically by finding the most likely distribution for the energy consumption and using it to infer the solar generation. We extend this approach to propose the first technique to infer PV capacity without weather data or a solar proxy, using instead only smart meter readings given a group of houses in close proximity. We evaluate the algorithm on two datasets: (i) the US Pecan Street dataset is adapted so that net energy meter readings are censored; and (ii) a constructed dataset, combining smart meter readings from UK households and solar energy generation from locations across the UK. Our results show comparable accuracy at inferring PV capacity compared to existing approaches, which cannot deal with censored readings which represent over 50% of PV panel installations in the UK.
Prolog Technology Reinforcement Learning Prover

We present a reinforcement learning toolkit for experiments with guiding automated theorem proving in the connection calculus. The core of the toolkit is a compact and easy to extend Prolog-based automated theorem prover called plCoP. plCoP builds on the leanCoP Prolog implementation and adds learning-guided Monte-Carlo Tree Search as done in the rlCoP system. Other components include a Python interface to plCoP and machine learners, and an external proof checker that verifies the validity of plCoP proofs. The toolkit is evaluated on two benchmarks and we demonstrate its extendability by two additions: (1) guidance is extended to reduction steps and (2) the standard leanCoP calculus is extended with rewrite steps and their learned guidance. We argue that the Prolog setting is suitable for combining statistical and symbolic learning methods. The complete toolkit is publicly released.
GSA for machine learning problems: A comprehensive overview

The rapidly increasing data volume produced daily is encouraging to generate novel procedures for extracting suitable information from such data. Machine learning is an application of artificial intelligence which is employed to provide relevant knowledge extracted from data, due to such characteristics, the adoption of machine learning approaches is one of the most accepted alternatives for this purpose nowadays. On the other hand, many machine learning applications turn into complex tasks due to the nature of data and the procedure that these must be subjected to collecting appropriate information. Alternatively, metaheuristic techniques are optimization algorithms widely used for treating complex tasks efficiently. The Gravitational Search Algorithm (GSA) is an optimization method based on the Newtonian gravitational laws and the interaction of masses, this procedure has proved interesting results due to the employed operators for correct balancing the exploration and exploitation stages, avoiding the common flaws present in existing optimization techniques such as the premature convergence onto local minimal. In this study, a comprehensive overview of the GSA applied in several machine learning applications is carried out.
Software-Defined Design Space Exploration for an Efficient DNN Accelerator Architecture

Deep neural networks (DNNs) have been shown to outperform conventional machine learning algorithms across a wide range of applications, e.g., image recognition, object detection, robotics, and natural language processing. However, the high computational complexity of DNNs often necessitates extremely fast and efficient hardware. The problem gets worse as the size of neural networks grows exponentially. As a result, customized hardware accelerators have been developed to accelerate DNN processing without sacrificing model accuracy. However, previous accelerator design studies have not fully considered the characteristics of the target applications, which may lead to sub-optimal architecture designs. On the other hand, new DNN models have been developed for better accuracy, but their compatibility with the underlying hardware accelerator is often overlooked. In this article, we propose an application-driven framework for architectural design space exploration of DNN accelerators. This framework is based on a hardware analytical model of individual DNN operations. It models the accelerator design task as a multi-dimensional optimization problem. We demonstrate that it can be efficaciously used in application-driven accelerator architecture design. Given a target DNN, the framework can generate efficient accelerator design solutions with optimized performance and area. Furthermore, we explore the opportunity to use the framework for accelerator configuration optimization under simultaneous diverse DNN applications. The framework is also capable of improving neural network models to best fit the underlying hardware resources.
WITHDRAWN: A Reliable Modified Whale Optimization Algorithm based Approach for Feature Selection to Classify Twitter Spam Profiles

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Hameem Shanavas, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.