Reviewerly/exHarmony huggingface dataset:
A Study on Cross-Corpus Speech Emotion Recognition and Data Augmentation

Models that can handle a wide range of speakers and acoustic conditions are essential in speech emotion recognition (SER). Often, these models tend to show mixed results when presented with speakers or acoustic conditions that were not visible during training. This paper investigates the impact of cross-corpus data complementation and data augmentation on the performance of SER models in matched (test-set from same corpus) and mismatched (test-set from different corpus) conditions. Investigations using six emotional speech corpora that include single and multiple speakers as well as variations in emotion style (acted, elicited, natural) and recording conditions are presented. Observations show that, as expected, models trained on single corpora perform best in matched conditions while performance decreases between 10-40% in mismatched conditions, depending on corpus specific features. Models trained on mixed corpora can be more stable in mismatched contexts, and the performance reductions range from 1 to 8% when compared with single corpus models in matched conditions. Data augmentation yields additional gains up to 4% and seem to benefit mismatched conditions more than matched ones.
Meta-Pose: Environment-adaptive Human Skeleton Tracking with RFID

Human pose tracking has attracted great interest re-cently. Considerable efforts have been made in Radio-Frequency (RF) sensing techniques for human pose tracking without using a video camera. Although the existing RF based schemes can well protect user privacy, they are usually sensitive to the RF environment and are hard to generalize to new environments. In this paper, we analyze the challenges of generalization of Radio-Frequency Identification (RFID) based human pose tracking systems. We then present an RFID based 3D human pose tracking system, termed Meta-Pose, which incorporates meta-learning and few-shot fine-tuning to achieve high adaptability to new environments. The proposed system is implemented with commodity RFID devices and extensive experiments are conducted for performance evaluation. The experiment results validate the superior human pose tracking performance and high adaptability of the proposed Meta-Pose system.
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In this paper, we give a further study on the permutation behavior of polynomials of a special form by considering the number of solutions of certain equations over finite fields. First, four classes of permutation polynomials of the form (x2m+x+δ)s+x over F22m are presented. Notably, some necessary and sufficient conditions for this kind of polynomials to permute F22m are provided. Second, we present several classes of permutation polynomials of the form (xpm−x+δ)s+x over Fp2m of odd characteristic, some of which can provide complete permutation polynomials of this form over F32m.
Supporting Privacy-Preserving Big Data Analytics on Temporal Open Big Data

Nowadays, valuable big data are generated and collected rapidly from numerous rich data sources. Following the initiatives of open data, many organizations including municipal governments are willing to share their data such as open big data regarding parking violations. While there have been models to preserve privacy of sensitive personal data like patient data for health informatics, privacy of individuals who violated parking regulations should also be protected. Hence, in this article, we present a model for supporting privacy-preserving big data analytics on temporal open big data. This temporally hierarchical privacy-preserving model (THPPM) adapts and extends the traditional temporal hierarchy to generalize spatial data generated within a time interval with an aim to preserve privacy of individuals who violated parking regulations during some time intervals at certain geographic locations. Evaluation on open big data from two North American cities demonstrates the usefulness of our model in supporting privacy-preserving big data analytics on temporal open big data.
Adaptive weight based on overlapping blocks network for facial expression recognition

Facial expressions which contain rich behavioral information are the primary vehicle to express emotions. It is important to analyze people's emotions with computer to achieve human-computer interaction. Feature extraction is the most important factor affecting the recognition effect. However, the existing deep learning for expression recognition is mainly based on global feature extraction. Local feature extraction provides more fine-grained information than global features. To strengthen the local discrimination of the image and pay more attention to the small targets in the local region, we propose an innovative Adaptive Weight Based on Overlapping Blocks Network (AWOBNet) for learning feature representation. First, we spatially overlay the feature maps to obtain the local features of the face. Considering the correlation and proportion between different features, we model the correlation between feature channels after overlapping blocks. Moreover, a new adaptive weighting method is developed to enhance significant features. We evaluate the proposed network on two public datasets, including the Real-World Affective Faces Database (RAFDB) and the Static Facial Expressions in the Wild (SFEW), and show the performance using the visualization method. The accuracy rates of our method obtain 89.863% on RAFDB and 62.410% on SFEW, which is significantly higher than the existing technical level.
Not every sample is efficient: Analogical generative adversarial network for unpaired image-to-image translation

Image translation is to learn an effective mapping function that aims to convert an image from a source domain to another target domain. With the proposal and further developments of generative adversarial networks (GANs), the generative models have achieved great breakthroughs. The image-to-image (I2I) translation methods can mainly fall into two categories: Paired and Unpaired. The former paired methods usually require a large amount of input-output sample pairs to perform one-side image translation, which heavily limits its practicability. To address the lack of the paired samples, CycleGAN and its extensions utilize the cycle-consistency loss to provide an elegant and generic solution to perform the unpaired I2I translation between two domains based on unpaired data. This thread of dual learning-based methods usually adopts the random sampling strategy for optimizing and does not consider the content similarity between samples. However, not every sample is efficient and effective for the desired optimization and leads to optimal convergence. Inspired by analogical learning, which is to utilize the relationships and similarities between sample observations, we propose a novel generic metric-based sampling strategy to effectively select samples from different domains for training. Besides, we introduce a novel analogical adversarial loss to force the model to learn from the effective samples and alleviate the influence of the negative samples. Experimental results on various vision tasks have demonstrated the superior performance of the proposed method. The proposed method is also a generic framework that can be easily extended to other I2I translation methods and result in a performance gain.
Findings of Shared Task on Offensive Language Identification in Tamil and Malayalam

We present the results of HASOC-Dravidian-CodeMix shared task1 held at FIRE 2021, a track on offensive language identification for Dravidian languages in Code-Mixed Text in this paper. This paper will detail the task, its organisation, and the submitted systems. The identification of offensive language was viewed as a classification task. For this, 16 teams participated in identifying offensive language from Tamil-English code mixed data, 11 teams for Malayalam-English code mixed data and 14 teams for Tamil data. The teams detected offensive language using various machine learning and deep learning classification models. This paper has analysed those benchmark systems to find out how well they accommodate a code-mixed scenario in Dravidian languages, focusing on Tamil and Malayalam.
Bayesian Gabor Network With Uncertainty Estimation for Pedestrian Lane Detection in Assistive Navigation

Automatic pedestrian lane detection is a challenging problem that is of great interest in assistive navigation and autonomous driving. Such a detection system must cope well with variations in lane surfaces and illumination conditions so that a vision-impaired user can navigate safely in unknown environments. This paper proposes a new lightweight Bayesian Gabor Network (BGN) for camera-based detection of pedestrian lanes in unstructured scenes. In our approach, each Gabor parameter is represented as a learnable Gaussian distribution using variational Bayesian inference. For the safety of vision-impaired users, in addition to an output segmentation map, the network provides two full-resolution maps of aleatoric uncertainty and epistemic uncertainty as well-calibrated confidence measures. Our Gabor-based method has fewer weights than the standard CNNs, therefore it is less prone to overfitting and requires fewer operations to compute. Compared to the state-of-the-art semantic segmentation methods, the BGN maintains a competitive segmentation performance while achieving a significantly compact model size (from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$237.6\times $ </tex-math></inline-formula> reduction), a fast prediction time (from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$67.5\times $ </tex-math></inline-formula> faster), and a well-calibrated uncertainty measure. We also introduce a new lane dataset of 10,000 images for objective evaluation in pedestrian lane detection research.
FaceMask: A New Image Dataset for the Automated Identification of People Wearing Masks in the Wild

The rapid spread of the COVID-19 pandemic, in early 2020, has radically changed the lives of people. In our daily routine, the use of a face (surgical) mask is necessary, especially in public places, to prevent the spread of this disease. Furthermore, in crowded indoor areas, the automated recognition of people wearing a mask is a requisite for the assurance of public health. In this direction, image processing techniques, in combination with deep learning, provide effective ways to deal with this problem. However, it is a common phenomenon that well-established datasets containing images of people wearing masks are not publicly available. To overcome this obstacle and to assist the research progress in this field, we present a publicly available annotated image database containing images of people with and without a mask on their faces, in different environments and situations. Moreover, we tested the performance of deep learning detectors in images and videos on this dataset. The training and the evaluation were performed on different versions of the YOLO network using Darknet, which is a state-of-the-art real-time object detection system. Finally, different experiments and evaluations were carried out for each version of YOLO, and the results for each detector are presented.
Wide-Angle Image Rectification: A Survey

Wide field-of-view (FOV) cameras, which capture a larger scene area than narrow FOV cameras, are used in many applications including 3D reconstruction, autonomous driving, and video surveillance. However, wide-angle images contain distortions that violate the assumptions underlying pinhole camera models, resulting in object distortion, difficulties in estimating scene distance, area, and direction, and preventing the use of off-the-shelf deep models trained on undistorted images for downstream computer vision tasks. Image rectification, which aims to correct these distortions, can solve these problems. In this paper, we comprehensively survey progress in wide-angle image rectification from transformation models to rectification methods. Specifically, we first present a detailed description and discussion of the camera models used in different approaches. Then, we summarize several distortion models including radial distortion and projection distortion. Next, we review both traditional geometry-based image rectification methods and deep learning-based methods, where the former formulates distortion parameter estimation as an optimization problem and the latter treats it as a regression problem by leveraging the power of deep neural networks. We evaluate the performance of state-of-the-art methods on public datasets and show that although both kinds of methods can achieve good results, these methods only work well for specific camera models and distortion types. We also provide a strong baseline model and carry out an empirical study of different distortion models on synthetic datasets and real-world wide-angle images. Finally, we discuss several potential research directions that are expected to further advance this area in the future.
Segmentation of HE-stained meningioma pathological images based on pseudo-labels

Biomedical research is inseparable from the analysis of various histopathological images, and hematoxylin-eosin (HE)-stained images are one of the most basic and widely used types. However, at present, machine learning based approaches of the analysis of this kind of images are highly relied on manual labeling of images for training. Fully automated processing of HE-stained images remains a challenging task due to the high degree of color intensity, size and shape uncertainty of the stained cells. For this problem, we propose a fully automatic pixel-wise semantic segmentation method based on pseudo-labels, which concerns to significantly reduce the manual cell sketching and labeling work before machine learning, and guarantees the accuracy of segmentation. First, we collect reliable training samples in a unsupervised manner based on K-means clustering results; second, we use full mixup strategy to enhance the training images and to obtain the U-Net model for the nuclei segmentation from the background. The experimental results based on the meningioma pathology image dataset show that the proposed method has good performance and the pathological features obtained statistically based on the segmentation results can be used to assist in the clinical grading of meningiomas. Compared with other machine learning strategies, it can provide a reliable reference for clinical research more effectively.
Probabilistic REpresentatives Mining (PREM): A Clustering Method for Distributional Data Reduction

Complex computations and analyses on massive data sets can be impractical or infeasible. Data reduction is a crucial problem in the era of big data to obtain a reduced representation of the data set to facilitate more efficient yet accurate analyses. To best preserve the integrity of the original data set, a reduced representation aims to best maintain the same data distribution, referred to as a probabilistically representative subset. This paper considers the problem of reducing a large data set to very small such subsets at which random sampling does not perform well enough. We propose a data mining approach called Probabilistic Representatives Mining (PREM) to tackle this challenge. PREM uses balanced clustering to prevent undersampling and oversampling issues and multistage computing strategy to achieve better scalability and consistency. Numerical experiments on typical probability distributions and real-world data sets in the field of aeronautics and astronautics demonstrate PREM's superiority over the baselines. An uncertainty quantification case study from aviation environmental impact modeling further shows PREM's effectiveness and accuracy in generating probabilistically representative small samples for costly computations. Potential limitations and extensions of the method are also discussed in the paper.
Xor-And-Inverter Graphs for Quantum Compilation

Abstract Quantum compilation is the task of translating a high-level description of a quantum algorithm into a sequence of low-level quantum operations. We propose and motivate the use of Xor-And-Inverter Graphs (XAG) to specify Boolean functions for quantum compilation. We present three different XAG-based compilation algorithms to synthesize quantum circuits in the Clifford + T library, hence targeting fault-tolerant quantum computing. The algorithms are designed to minimize relevant cost functions, such as the number of qubits, the T -count, and the T -depth, while allowing the flexibility of exploring different solutions. We present novel resource estimation results for relevant cryptographic and arithmetic benchmarks. The achieved results show a significant reduction in both T -count and T -depth when compared with the state-of-the-art.
SegDQ: Segmentation assisted multi-object tracking with dynamic query-based transformers

Multi-Object Tracking (MOT) has been one of the most important topics in computer vision. The traditional tracking-by-detection framework of MOT is severely suffered from the poor detection results. In this paper, based on Transformer, we introduce the tracking-by-query MOT framework, and propose to apply semantic segmentation as an auxiliary task to optimize the training of MOT trackers, which addresses more on extracted foreground features. In addition, a feature-dependent dynamic object query (DOQ), instead of a fixed-learned object query (LOQ), is put forward to retrieve the new detections, improving the flexibility and constringency of the framework. We tested our SegDQ method on various scenarios including MOTChallenge 15, 16 and 17 datasets. The experimental results show that it obviously improves the MOTA and IDF1 indexes of tracking results.
Few-shot object detection via baby learning

Few-shot learning is proposed to overcome the problem of scarce training data in novel classes. Recently, few-shot learning has been well adopted in various computer vision tasks such as object recognition and object detection. However, the state-of-the-art (SOTA) methods have less attention to effectively reuse the information from previous stages. In this paper, we propose a new framework of few-shot learning for object detection. In particular, we adopt Baby Learning mechanism along with the multiple receptive fields to effectively utilize the former knowledge in novel domain. The propoed framework imitates the learning process of a baby through visual cues. The extensive experiments demonstrate the superiority of the proposed method over the SOTA methods on the benchmarks (improve average 7.0% on PASCAL VOC and 1.6% on MS COCO).
A New Method of Deep Convolutional Neural Network Image Classification Based on Knowledge Transfer in Small Label Sample Environment

The problem of deep learning network image classification when a large number of image samples are obtained in life and with only a small amount of knowledge annotation, is preliminarily solved in this paper. First, a support vector machine expert labeling system is constructed by using a bag-of-words model to extract image features from a small number of labeled samples. The labels of a large number of unlabeled image samples are automatically annotated by using the constructed SVM expert labeling system. Second, a small number of labeled samples and automatically labeled image samples are combined to form an augmented training set. A deep convolutional neural network model is created by using an augmented training set. Knowledge transfer from SVMs trained with a small number of image samples annotated by artificial knowledge to deep neural network classifiers is implemented in this paper. The problem of overfitting in neural network training with small samples is solved. Finally, the public dataset caltech256 is used for experimental verification and mechanism analysis of the performance of the new method.
Customer Experience towards the Product during a Coronavirus Outbreak

Nowadays, sentimental analysis of consumers' review is becoming much crucial in the marketing world. It is not just giving ideas to the firms that how consumers like their product or service, but it would also help them make their service better. In this article, the statistical method identifies the relationship of many factors in consumer feedback. It introduces a deep-based learning method called DSC (deep sentiment classifier) to determine whether or not to recommend the reviewed product thoroughly. Our suggested method also investigates the effect sizes of the feedback, such as positives, negatives, and neutrals. We used the women's clothing review dataset containing 22,642 records after preprocessing of the results. Experimental studies show that the recommendations are an excellent positive sentiment indicator. In comparison, ratings become fuzzy performance metrics in product reviews. The 10-fold cross-validation analysis shows that the recommended form has the top F1 score (93.56%) in the sentimental classification on average and the recommended classification (88.32%) on average. A comparative description of other classifiers focused on machine learning, for example, KNN, random forest, logistic regression, decision tree, support vector machine multilayer perceptron, and naïve Bayes, also demonstrates that DSC gives the best possible result. We have tested DSC on the dataset IMDB (Internet Video Database), which includes the sentiment of the 50,000 movie reviews (25000 for training and 25000 for testing). In comparison to other baseline methods, DSC obtained an excellent classification score for this experiment.
Development of a Control System for Multilink Manipulators on Unmanned Underwater Vehicles Dynamically Positioned over Seafloor Objects

This article considers an approach to synthesizing a multilevel system to control movements of a multilink manipulator (MM) mounted on an unmanned underwater vehicle (UUV) for performing autonomous manipulative operations in the mode of dynamic positioning over various objects on the seafloor. The system is based on an upgraded method that, using point clouds received from the machine vision systems (MVS), provides high-accuracy determination of the shape and location of the work object relative to UUV. The preset trajectories of the MM working tool are overlaid on the identified surface of the object, with possible silting, fouling or deformation of the latter taken into account. To execute the programmed trajectories with the MM working tool, the following methods have been implemented in software: stabilization of UUV in hovering mode near the object, high-precision control of working tool’s movements, and also corrections of its trajectory taking into account UUV’s displacements relative to the object. The synthesized system has been developed in the C++ programming language. The operation of the system has been numerically simulated using a model of UUV with MM, as well as models of the environment and the target object, in the Matlab/Simulink and V-REP software packages. The results of the study show a high efficiency of the system both in processing sensor information and in providing the dynamic control of movements of an UUV with a MM.