Reviewerly/exHarmony huggingface dataset:
Research on Discrete Dynamic System Modeling of Vocal Performance Teaching Platform Based on Big Data Environment

The traditional teaching model of national vocal music in colleges and universities has some problems, such as low quality of teaching, poor diversity of teaching, and low interest of students. Based on this, this study studies the innovation of the teaching model of national vocal music in colleges and universities based on the deep recurrent neural network algorithm and designs the teaching quality evaluation model based on the deep recurrent neural network algorithm. The collection of data and information is realized from the aspects of students’ class state, vocal music examination results, classroom interaction, etc., and the comprehensive analysis is carried out using the deep recursive neural network algorithm, to realize the multiple analysis and objective evaluation of the whole process of different teaching modes of national vocal music in music colleges and universities, according to the different characteristics and teaching objectives of the teaching process of national vocal music in music colleges and universities The standard requires accurate analysis and evaluation. The results show that the optimization model based on the deep recurrent neural network algorithm has the advantages of high feasibility, high intelligence, and wide range of applications. The experiments show that the deep recurrent neural network algorithm can analyze the effectiveness of the innovation model, and the fuzzy evaluation method can realize the comprehensive evaluation of the innovation mode, which is conducive to the improvement of students’ vocal music learning ability.
Improving Mandarin End-to-End Speech Recognition With Word N-Gram Language Model

Despite the rapid progress of end-to-end (E2E) automatic speech recognition (ASR), it has been shown that incorporating external language models (LMs) into the decoding can further improve the recognition performance of E2E ASR systems. To align with the modeling units adopted in E2E ASR systems, subword-level (e.g., characters, BPE) LMs are usually used to cooperate with current E2E ASR systems. However, the use of subword-level LMs will ignore the word-level information, which may limit the strength of the external LMs in E2E ASR. Although several methods have been proposed to incorporate word-level external LMs in E2E ASR, these methods are mainly designed for languages with clear word boundaries such as English and cannot be directly applied to languages like Mandarin, in which each character sequence can have multiple corresponding word sequences. To this end, we propose a novel decoding algorithm where a word-level lattice is constructed on-the-fly to consider all possible word sequences for each partial hypothesis. Then, the LM score of the hypothesis is obtained by intersecting the generated lattice with an external word N-gram LM. The proposed method is examined on both Attention-based Encoder-Decoder (AED) and Neural Transducer (NT) frameworks. Experiments suggest that our method consistently outperforms subword-level LMs, including N-gram LM and neural network LM. We achieve state-of-the-art results on both Aishell-1 (CER 4.18%) and Aishell-2 (CER 5.06%) datasets and reduce CER by 14.8% relatively on a 21K-hour Mandarin dataset.
Survey of Object Tracking Algorithm Based on Siamese Network

Abstract The network model using deep learning have been widely used in the sphereof visual object tracking for the past few years. The Siamese network can utilize the model based on deep learning to achieve a balance between the tracking accuracy and speed in the visual object tracking. This work mainly introduces the development process of the visual target tracking field and traditional target tracking algorithms. It focuses on the Siamese network structure and the improved the Siamese algorithm, and compares tracking results of related algorithms. Aiming at the deficiencies of existing Siamese object tracking algorithms, the future development trend is prospected.
Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy

With the large-scale optimization problems in the real world becoming more and more complex, they also require different optimization algorithms to keep pace with the times. Particle swarm optimization algorithm is a good tool that has been proved to deal with various optimization problems. Conventional particle swarm optimization algorithms learn from two particles, namely, the best position of the current particle and the best position of all particles. This particle swarm optimization algorithm is simple to implement, simple, and easy to understand, but it has a fatal defect. It is hard to find the global optimal solution quickly and accurately. In order to deal with these defects of standard particle swarm optimization, this paper proposes a particle swarm optimization algorithm (SHMPSO) based on the hybrid strategy of seed swarm optimization (using codes available from https://gitee.com/mr-xie123234/code/tree/master/). In SHMPSO, a subpopulation coevolution particle swarm optimization algorithm is adopted. In SHMPSO, an elastic candidate-based strategy is used to find a candidate and realize information sharing and coevolution among populations. The mean dimension learning strategy can be used to make the population converge faster and improve the solution accuracy of SHMPSO. Twenty-one benchmark functions and six industries-recognized particle swarm optimization variants are used to verify the advantages of SHMPSO. The experimental results show that SHMPSO has good convergence speed and good robustness and can obtain high-precision solutions.
Circumferential Local Ternary Pattern: New and Efficient Feature Descriptors for Anti-Counterfeiting Pattern Identification

An important aspect of querying whether a product is likely to be forged is to identify its anti-counterfeiting label. However, the use of image processing technology for label-specific texture analysis to quickly and effectively identify the anti-counterfeiting label has been widely studied. Aiming at the defects of the local binary pattern (LBP) and its variants in texture identification, this paper proposes a new texture model for anti-counterfeiting identification, that is, the circular local ternary pattern (CLTP). The highlight of our technology is that it extracts the effective local texture descriptors by using the random features of inkjet printing. This allows for the technology to not only resist the interference of noise and illumination in images of anti-counterfeiting patterns but also to encode and reorganize the fine linear shape structure. Specifically, this paper extracts the CLTP texture feature in the corresponding key areas and forms the final feature histogram vector for comparison through the one-to-one correspondence between the sample image and the inspected image of anti-counterfeiting pattern. Experiments prove that our method not only has high discrimination, stability and effectiveness but also provides a convenient and practical idea for anti-counterfeiting technology.
Low-Light Stereo Image Enhancement

Stereo cameras are now commonly used in more and more devices. Nevertheless, visually unpleasant images captured under low-light conditions hinder their practical application. As an initial attempt at low-light stereo image enhancement, we propose a novel Dual-View Enhancement Network (DVENet) based on the Retinex theory, which consists of two stages. The first stage estimates an illumination map to obtain a coarse enhancement result, which boosts the correlation of two views, while the second stage recovers details by integrating the information from two views to achieve fine image quality improvement with the guidance of the illumination map. To fully utilize the dual-view correlation, we further design a wavelet-based view transfer module to efficiently carry out multi-scale detail recovery. Then, we design an illumination-aware attention fusion module to exploit the complementarity between the fused features from two views and the single-view features. Experiments on both synthetic and real-world stereo datasets demonstrate the superiority of our proposed method over existing solutions. The code and model are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/KevinJ-Huang/Stereo-Low-Light</uri> .
A Framework on the Use of Agile Methods in Logistics Startups

Background: The logistics industry has seen the emergence of many startups in recent years. Many of these logistics startups use new technologies to develop disruptive products, services or platforms that are based on software. This paper presents the results of a Delphi study and a survey that were consolidated in a framework. The purpose is to understand the benefits that logistics startups derive from using agile methods, the difficulties they face in using these methods and the evolution of logistics startups in terms of using agile methods. Methods: A Delphi study with 29 experts and a global survey with 95 participants was conducted to look at the implementation of agile methods. The largest group of participants were members of (top) management, agile coaches and team leaders. Results: The framework consolidates gathered data to demonstrate how logistics startups apply agile methods and practices based on the results of the Delphi study and the survey, and how the usage of agile methods changes over the age of logistics startups. The results indicate that younger logistics startups use agile methods predominantly to design product features and maximise customer value, whereas logistics startups older than five years focus more on the optimisation of internal processes. Conclusions: The value of the present study lies in its contribution to the hitherto hardly examined research field of agility in logistics startups and the notable views of the experienced participants.
Using the virtual data-driven measurement to support the prototyping of hand gesture recognition interface with distance sensor

The performance of a fixed-sensor-based hand gesture recognition system is typically influenced by the position and number of sensors. The traditional development approach to hand gesture recognition systems follows a process of sensor pre-deployment, data collection, and model training, which is highly time-consuming and expensive to calculate how many sensors to place where, and the system has low secondary development flexibility. In this paper, we present a new development flow to assist in prototyping distance sensor-based gesture recognition interfaces. The designed system was able to simulate the position and number of sensors to recognize gestures. Using a reconstructed hand motion, the virtual distance sensor generated simulated signals and trained a convolutional neural network model. In a real-world setting, the sensor system only needed to be configured by transfer learning to recognize gestures at the same sensor layout. The proposed method was able to indicate sensor configuration and the trained classifier via virtual distance data, which can effectively reduce the development cost. We evaluated two prototype interfaces of the proposed method using distance sensors and demonstrated that the system effectively provided deployment recommendations, and models trained using virtual measurement data could effectively recognize real gestures.
IRDCLNet: Instance Segmentation of Ship Images Based on Interference Reduction and Dynamic Contour Learning in Foggy Scenes

Frequent bad weather at sea severely damages the quality of visual images captured by imaging equipment. Ship instance segmentation in adverse weather conditions remains a major challenge because of poor visibility at sea. Existing approaches for instance segmentation are primarily designed for clear days and rarely consider the aforementioned severe weather. Blurred ship objects can easily cause missed ship detection and decrease the instance segmentation performance on ship images, especially in the case of frequent fog at sea. To this end, we propose a ship instance segmentation framework (IRDCLNet) based on Interference Reduction and Dynamic Contour Learning in foggy scenes. The Interference Reduction Module is proposed to reduce the interference caused by fog and solves the problem of missed ship detection. Meanwhile, we present Dynamic Contour Learning to predict the overall contour of the blurred ships to assist in mask prediction. To handle the scarcity of ocean data in foggy weather, we build the Foggy ShipInsseg dataset, which contains 5,739 real and simulated foggy ship images with 10,900 fine instance mask annotations. Experiments on the Foggy ShipInsseg dataset show that our IRDCLNet outperforms the Mask R-CNN and CondInst baselines and achieves the state-of-the-art performance.
Intelligent trademark recognition and similarity analysis using a two-stage transfer learning approach

The ubiquitous and fast-growing e-marketplaces are causing serious concerns of unauthorized trademark usages, especially their pictorial images. There is a significant need for e-commerce service intermediaries and trading sites to actively check whether product images posted online are not infringing upon others’ intellectual property rights (IPRs). To ensure registered trademark (TM) or copyrighted logo-image (logo) protection, this research develops an intelligent system that can detect, locate, and crop (cut) logos posted online and check whether they are substantially or deceptively similar to registered TM logos. This research integrates two deep learning models to achieve the research goal. The first is the logo detection and localization model for cropping trademark like images from complex online merchandise photos, which could have images of many product views and even worn and displayed on a human model. Only the cropped logo image is needed for comparison to a database of registered trademarks. The second model performs TM similarity analysis using the cropped logos compared to the published TM images. Yolo v4 is adopted as the general logo locator for intelligent logo image cropping. The triplet convolutional neural network model is used to fine-tuned for intelligent trademark similarity analysis. The models are trained with an image dataset, combining image samples from LogoDet-3k and images found through web search. The system performance is quantitatively tested using the FlickrLogos-32 dataset and consumer product images extracted from an e-commerce platform. The test results of the system achieve high precision (0.91) for trademark class matching.
Towards a fully data-driven prospectivity mapping methodology: A case study of the Southeastern Churchill Province, Québec and Labrador

Mineral exploration campaigns are financially risky. Several state-of-the-art methods have been developed to mitigate the risk, including predictive modelling of mineral prospectivity using principal component analysis (PCA) and geographic information systems (GIS). The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets. However, some of its limitations are the dependence on sample stoichiometry (e.g., the existence of minerals), the necessity of log-ratio transformations when dealing with compositional data, and manual interpretation and use of principal components to enhance potential geochemical anomalies for prospectivity mapping. In this study, we generalize the fundamental ideas behind the PCA and GIS approach by developing a new data-driven approach using ML. We showcase a new workflow capable of generating either intermediate evidence layers or final prospectivity maps that depict major regional geochemical anomalies using multi-element geochemical data from Southeastern Churchill Province (Québec and Labrador), Canada. The region is known for its REEs endowment and the data were gathered for prospectivity mapping. A comparison with the established multivariate hybrid data- and knowledge-based approach revealed that on a roughly comparable basis of the amount of manual effort, our new data-driven procedure can much more accurately identify geochemical anomalies in both univariate and multivariate applications. The results of our prospectivity mapping corroborate with the ground truth or known geological anomalies in the studied region. These findings have potentially wider implications on exploration target generation, where project risks (financial, environmental, political, etc.) and geochemical anomalies must be quantified using robust and effective data-driven approaches. In addition, our methodology is more replicable and objective, as manual geoscientific interpretation is not required during the detection of geochemical anomalies.
Monocular contextual constraint for stereo matching with adaptive weights assignment

Matching-based stereo disparity estimation has difficulty in dealing with occlusion, weak and repetitive textures in binocular vision. By contrast, monocular vision, estimating depth from a single image, is not subject to these challenges. Inspired by this, in this study, we propose an adaptive co-learning framework with monocular and stereo branches named CLStereo to improve stereo performance. This framework introduces a monocular branch as contextual constraints to transfer the prior knowledge learned from the monocular branch to the stereo branch. An adaptive weights assignment is further proposed to balance the co-learning of both branches without mutually tuning. CLStereo can be seamlessly embedded into many existing deep stereo models to boost their performance, especially in occluded, weak, and repetitive texture areas. Extensive experiments demonstrate that we achieve the state-of-the-art performance on the Scene Flow dataset and improve deep stereo models by at least 4% on KITTI 2012 and 2015 benchmarks.
Enhancing the Recognition Task Performance of MEMS Resonator-Based Reservoir Computing System via Nonlinearity Tuning

Reservoir computing (RC) is a potential neuromorphic paradigm for physically realizing artificial intelligence systems in the Internet of Things society, owing to its well-known low training cost and compatibility with nonlinear devices. Micro-electro-mechanical system (MEMS) resonators exhibiting rich nonlinear dynamics and fading behaviors are promising candidates for high-performance hardware RC. Previously, we presented a non-delay-based RC using one single micromechanical resonator with hybrid nonlinear dynamics. Here, we innovatively introduce a nonlinear tuning strategy to analyze the computing properties (the processing speed and recognition accuracy) of the presented RC. Meanwhile, we numerically and experimentally analyze the influence of the hybrid nonlinear dynamics using the image classification task. Specifically, we study the transient nonlinear saturation phenomenon by fitting quality factors under different vacuums, as well as searching the optimal operating point (the edge of chaos) by the static bifurcation analysis and dynamic vibration numerical models of the Duffing nonlinearity. Our results in the optimal operation conditions experimentally achieved a high classification accuracy of (93 ± 1)% and several times faster than previous work on the handwritten digits recognition benchmark, profit from the perfect high signal-to-noise ratios (quality factor) and the nonlinearity of the dynamical variables.
Online wave direction and wave number estimation from surface vessel motions using distributed inertial measurement arrays and phase-time-path-differences

A common approach for finding the direction of ocean waves is to use the phase-time-path-differences (PTPDs) between a static array of various types of sensors mounted on either the sea surface or seabed. However, some practical drawbacks of such arrays are that they tend to be expensive, difficult to install, and fixed in location. We show that the PTPD approach can be generalized to a portable shipboard array of spatially distributed sensors rendering it more practical. In this respect, we derive a nonlinear PTPD model for shipboard sensor arrays and prove that the wave direction and wave number can be resolved from a minimum of three noncollinear sensors using observability analysis. Moreover, based on our PTPD model, we propose an unscented Kalman filter algorithm for online estimation of the wave direction and wave number, which offers a convenient framework for adding multiple measurements and incorporating uncertainties. Our experimental results from model basin testing with a model ship equipped with several inertial measurement units (IMUs) confirm that the wave direction and wave number can be estimated from the wave-induced motions of a surface vessel with a minimum of three noncollinear IMUs. In this study, we consider parameter estimation for regular waves and assume a dynamically positioned surface vessel with small roll and pitch angles.
Mechanisms to Alleviate Over-Generalization in XCS for Continuous-Valued Input Spaces

Abstract In the field of rule-based approaches to Machine Learning , the XCS classifier system (XCS) is a well-known representative of the learning classifier systems family. By using a genetic algorithm (GA), the XCS aims at forming rules or so-called classifiers which are as general as possible to achieve an optimal performance level. A too high generalization pressure may lead to over-general classifiers degrading the performance of XCS. To date, no method exists for XCS for real-valued input spaces (XCSR) and XCS for function approximation (XCSF) to handle over-general classifiers ensuring an accurate population. The Absumption mechanism and the Specify operator, both developed for XCS with binary inputs, provide a promising basis for over-generality handling in XCSR and XCSF. This paper introduces adapted versions of Absumption and Specify by proposing different identification and specialization strategies for the application in XCSR and XCSF. To determine their potential, the adapted techniques are evaluated in different classification problems, i.e., common benchmarks and real-world data from the agricultural domain, in a multi-step problem as well as different regression tasks. Our experimental results show that the application of these techniques leads to significant improvements of the accuracy of the generated classifier population in the applied benchmarks, data sets, multi-step problems and regression tasks, especially when they tend to form over-general classifiers. Furthermore, considering the working principle of the proposed techniques, the intended decrease in overall classifier generality can be confirmed.
Toward Spatially Unbiased Generative Models

Recent image generation models show remarkable generation performance. However, they mirror strong location preference in datasets, which we call spatial bias. Therefore, generators render poor samples at unseen locations and scales. We argue that the generators rely on their implicit positional encoding to render spatial content. From our observations, the generator’s implicit positional encoding is translation-variant, making the generator spatially biased. To address this issue, we propose injecting explicit positional encoding at each scale of the generator. By learning the spatially unbiased generator, we facilitate the robust use of generators in multiple tasks, such as GAN inversion, multi-scale generation, generation of arbitrary sizes and aspect ratios. Furthermore, we show that our method can also be applied to denoising diffusion probabilistic models. Our code is available at: https://github.com/jychoill8/toward_spatial_unbiased.