Reviewerly/exHarmony huggingface dataset:
Do We Really Need Gold Samples for Sample Weighting under Label Noise?

Learning with labels noise has gained significant traction recently due to the sensitivity of deep neural networks under label noise under common loss functions. Losses that are theoretically robust to label noise, however, often makes training difficult. Consequently, several recently proposed methods, such as Meta-Weight-Net (MW-Net), use a small number of unbiased, clean samples to learn a weighting function that downweights samples that are likely to have corrupted labels under the meta-learning framework. However, obtaining such a set of clean samples is not always feasible in practice. In this paper, we analytically show that one can easily train MW-Net without access to clean samples simply by using a loss function that is robust to label noise, such as mean absolute error, as the meta objective to train the weighting network. We experimentally show that our method beats all existing methods that do not use clean samples and performs on-par with methods that use gold samples on benchmark datasets across various noise types and noise rates.
RETRACTED: Design of english video learning platform based on FPGA system and sobel algorithm

In order to improve the system performance of image processing and improve the efficiency of image processing, we can use some hardware running image processing algorithms. in practice, we introduce some reconfigurable devices and more advanced programming languages to use FPGA for image processing. following will briefly introduce the design of FPGA in the edge detection system.The design has integrated a 32-bit soft RISC processor as dedicated hardware peripheral micro development and EDK embedded system developed by the system generator. Input is detected from a real-time image obtained from the CMOS camera displayed on the DVI display on the screen.
GraphHop: An Enhanced Label Propagation Method for Node Classification

A scalable semisupervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains all nodes' attributes and link connections but labels of only a subset of nodes. Graph convolutional networks (GCNs) have provided superior performance in node label classification over the traditional label propagation (LP) methods for this problem. Nevertheless, current GCN algorithms suffer from a considerable amount of labels for training because of high model complexity or cannot be easily generalized to large-scale graphs due to the expensive cost of loading the entire graph and node embeddings. Besides, nonlinearity makes the optimization process a mystery. To this end, an enhanced LP method, called GraphHop, is proposed to tackle these problems. GraphHop can be viewed as a smoothening LP algorithm, in which each propagation alternates between two steps: label aggregation and label update. In the label aggregation step, multihop neighbor embeddings are aggregated to the center node. In the label update step, new embeddings are learned and predicted for each node based on aggregated results from the previous step. The two-step iteration improves the graph signal smoothening capacity. Furthermore, to encode attributes, links, and labels on graphs effectively under one framework, we adopt a two-stage training process, i.e., the initialization stage and the iteration stage. Thus, the smooth attribute information extracted from the initialization stage is consistently imposed in the propagation process in the iteration stage. Experimental results show that GraphHop outperforms state-of-the-art graph learning methods on a wide range of tasks in graphs of various sizes (e.g., multilabel and multiclass classification on citation networks, social graphs, and commodity consumption graphs).
Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems

Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student models for specific educational systems. However, systematically developing the data-driven student models from the educational data collected over prior educational experiences remains a challenge. We proposed a systematic and comprehensive machine learning-based modeling methodology to develop high-performance predictive student models from the historical educational data to address this issue. This methodology addresses the fundamental modeling issues, from data processing, to modeling, to model deployment. The said methodology can help developing student models for intelligent educational systems. After a detailed description of the proposed machine learning-based methodology, we introduce its application to an intelligent navigation tutoring system. Using the historical data collected in intelligent navigation tutoring systems, we conduct large-scale experiments to build the student models for training systems. The preliminary results proved that the proposed methodology is useful and feasible in developing the high-performance models for various intelligent education systems.
AdversarialQR: An adversarial patch in QR code format

In this paper, we present a method to camouflage an attack on image recognition system by using an adversarial patch embedded on a scan-ready QR code. Adversarial patch refers to a class of a real-world attack on a machine learning system that adds a `patch' onto the image. However, unlike existing methods, they are highly conspicuous to human perception. As these attacks are performed in the real world, they require users to manipulate the scene. However, not only the patch catches the attention of the classification system but also bystanders' attention as well. We believe that forcing the adversarial patch into the form of a scan-ready QR code can conceal its primary reason to exist in the scene. The main challenge of the research is the process of forcing an adversarial patch into a scan-ready QR code while trying to retain as much information for the patch to work as a real-world adversarial example. The experiments had been done to investigate trade-off compared to training the patch in different shapes.
Exploring the philosophy and practice of collective writing

We are about to publish in the Routledge Editor’s Choice series a collection called The Philosophy, Methodology and Pedagogy of Collective Writing. This collection of collections represents the dev...
A Metric Learning Approach to Eye Movement Biometrics

Metric learning is a valuable technique for enabling the ongoing enrollment of new users within biometric systems. While this approach has been heavily employed for other biometric modalities such as facial recognition, applications to eye movements have only recently been explored. This manuscript further investigates the application of metric learning to eye movement biometrics. A set of three multilayer perceptron networks are trained for embedding feature vectors describing three classes of eye movements: fixations, saccades, and post-saccadic oscillations. The network is validated on a dataset containing eye movement traces of 269 subjects recorded during a reading task. The proposed algorithm is benchmarked against a previously introduced statistical biometric approach. While mean equal error rate (EER) was increased versus the benchmark method, the proposed technique demonstrated lower dispersion in EER across the four test folds considered herein.
Traffic Signal Transition Time Prediction Based on Aerial Captures during Peak Hours

Owing to flexibility of Unmanned Aerial Vehicles (UAVs) and high efficiency of image processing technology, the combined systems become increasingly popular and important in the smart city operations. However, the application scenarios of this technology, especially on the traffic system prediction and multi-vehicle information extraction, still need to be explored. Besides, vehicle's detailed attributes need to be considered when building models. The smart traffic system can be broadly divided into two parts, traffic facilities (e.g. traffic signals, signs and sensors) and participants (e.g. vehicles and pedestrians). Many related works are presented about traffic parameters measurements using UAVs. In this paper, the prediction and traffic signal system analysis through different categories of vehicles' dynamic characteristics extracted from UAVs is presented. The motivation and related work is introduced. A stochastic process framework is presented for multi-vehicle speed extraction and signal transition time distributions at a signalized intersection. Detection and tracking methods/algorithms are proposed. To verify the mathematical model, the experimental data is collected at one intersection, in the city of Singapore during peak hours. After data collection, aerial images are processed to extract information. The regression method and processed parameters help to fit the required dynamic functions for different types vehicles. The estimated distributions reflect the traffic signal transition time provided by ground truths nicely. Moreover, the future research is presented on enhancing the system prediction accuracy and robustness.
Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shape which impose a real challenge on classification process. This paper introduces a new statistical framework to discriminate patients who are either negative or positive for certain kinds of virus and pneumonia. We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM). This mixture model is encouraged by its effectiveness and robustness recently obtained in various image processing applications. Unlike frequentist learning methods, our developed Bayesian framework has the advantage of taking into account the uncertainty to accurately estimate the model parameters as well as the ability to solve the problem of overfitting. We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model. The current work shows excellent results when dealing with the challenging problem of biomedical image classification. Indeed, extensive experiments have been carried out on real datasets and the results prove the merits of our Bayesian framework.
Fast Pose Graph Optimization via Krylov-Schur and Cholesky Factorization

Pose Graph Optimization (PGO) is an important problem in Computer Vision, particularly in motion estimation, whose objective consists of finding the rigid transformations that achieve the best global alignment of visual data on a common reference frame. The vast majority of PGO approaches rely on iterative techniques which refine an initial estimate until convergence is achieved. On the other hand, recent works have identified a global constraint which has cast this problem into the matrix completion domain. The success which both these formulations have had in computing accurate solutions efficiently has been overshadowed by large-scale industrial applications such as autonomous flight, self-driving cars and smart-cities, where it is necessary to fuse numerous images covering large areas but where each one of them has few pairwise observations. We propose a highly efficient algorithm to solve PGO which leverages the sparsity of the data by combining the Krylov- Schur method for spectral decomposition with Cholesky LDL factorization. Our method allows for high scalability, low computational cost and high precision, simultaneously.
A non-local propagation filtering scheme for edge-preserving in variational optical flow computation

The median filtering heuristic is considered to be an indispensable tool for the currently popular variational optical flow computation. Its attractive advantages are that outliers reduction is attained while image edges and motion boundaries are preserved. However, it still may generate blurring at image edges and motion boundaries caused by large displacement, motion occlusion, complex texture, and illumination change. In this paper, we present a non-local propagation filtering scheme to deal with the above problem during the coarse-to-fine optical flow computation. First, we analyze the connection between the weighted median filtering and the blurring of image edge and motion boundary under the coarse-to-fine optical flow computing scheme. Second, to improve the quality of the initial flow field, we introduce a non-local propagation filter to reduce outliers while preserving context information of the flow field. Furthermore, we present an optimization combination of non-local propagation filtering and weighted median filtering for the flow field estimation under the coarse-to-fine scheme. Extensive experiments on public optical flow benchmarks demonstrate that the proposed scheme can effectively improve the accuracy and robustness of optical flow estimation.
Zero-Shot Recognition via Optimal Transport

We propose an optimal transport (OT) framework for generalized zero-shot learning (GZSL), seeking to distinguish samples for both seen and unseen classes, with the assist of auxiliary attributes. The discrepancy between features and attributes is minimized by solving an optimal transport problem. Specifically, we build a conditional generative model to generate features from seen-class attributes, and establish an optimal transport between the distribution of the generated features and that of the real features. The generative model and the optimal transport are optimized iteratively with an attribute-based regularizer, that further enhances the discriminative power of the generated features. A classifier is learned based on the features generated for both the seen and unseen classes. In addition to generalized zero-shot learning, our framework is also applicable to standard and transductive ZSL problems. Experiments show that our optimal transport-based method outperforms state-of-the-art methods on several benchmark datasets.
Fair Face Recognition Using Data Balancing, Enhancement and Fusion

Racial bias is an important issue in biometrics, while has not been thoroughly studied in deep face recognition. By reducing the influence of gender and skin colour, this paper proposes a fair face recognition system with low bias. First, multiple preprocessing methods are added to improve the dual shot face detector for obtaining target face from a given test image. Then, a data re-sampling approach is employed to balance the data distribution and reduce the bias based on the analysis of training data. Moreover, multiple data enhancement methods are used to increase the accuracy performance. Finally, a linear-combination strategy is adopted to benefit from mutil-model fusion. ChaLearn Looking at People Fair Face Recognition challenge is supported by ECCV 2020. Our team (ustc-nelslip) ranked 1st in the development stage and 2nd in the test stage of this challenge. The code is available at https://github.com/HaoSir/ECCV-2020-Fair-Face-Recognition-challenge_2nd_place_solution-ustc-nelslip- .
The Efficacy of Financial Ratios for Fraud Detection Using Self Organising Maps

In recent times, financial statement fraud has resulted in billions of dollars being lost from the financial system. Financial statement fraud is a problem for both listed and local government entities. The present focus in the literature has been on analysing listed entities, and the analysis is typically framed as a supervised learning problem with the labels being audit opinions. In this paper we assess the efficacy of using financial ratios for detecting fraud in financial statements of local government entities. The problem is framed as an unsupervised learning problem. Self organising maps are used due to their visual nature and the resulting accessibility of information to decision makers. The analysis shows that financial ratios are useful in the detection of fraud in the public sector. Using qualified audit opinions as an indication of fraud, the analysis shows that a high current ratio is associated with entities that have unqualified audits (i.e. non-fraudulent), while entities that are fraudulent have a high debt to revenue ratio.
Non-Associative Algebra Redesigning Block Cipher with Color Image Encryption

The substitution box (S-box) is a fundamentally important component of symmetric key cryptosystem. An S-box is a primary source of non-linearity in modern block ciphers, and it resists the linear attack. Various approaches have been adopted to construct S-boxes. S-boxes are commonly constructed over commutative and associative algebraic structures including Galois fields, unitary commutative rings and cyclic and non-cyclic finite groups. In this paper, first a non-associative ring of order 512 is obtained by using computational techniques, and then by this ring a triplet of 8 × 8 S-boxes is designed. The motivation behind the designing of these S-boxes is to upsurge the robustness and broaden the key space due to non-associative and non-commutative behavior of the algebraic structure under consideration. A novel color image encryption application is anticipated in which initially these 3 S-boxes are being used to produce confusion in three layers of a standard RGB image. However, for the sake of diffusion 3D Arnold chaotic map is used in the proposed encryption scheme. A comparison with some of existing chaos and S-box dependent color image encryption schemes specs the performance results of the anticipated RGB image encryption and observed as approaching the standard prime level.
SecTEP: Enabling secure tender evaluation with sealed prices and quality evaluation in procurement bidding systems over blockchain

Numerous methods have been proposed in the past few years to perform tender evaluation in procurement bidding systems, such as cloud storage procurement bidding systems. However, most of the proposed approaches focus only on the bidding price comparison and bidding efficiency while neglecting supplier quality evaluation and bidding privacy. To the best of our knowledge, the existing studies on tender evaluation in procurement bidding systems either provide insufficient privacy protection or are only concerned with bidding prices. In this paper, for the first time, we propose SecTEP, a Secure Tender Evaluation method with sealed prices and quality evaluation in Procurement bidding systems over blockchain. SecTEP considers not only the bidding price but also the supplier's quality, while protecting the privacy of the bidding and evaluation results. SecTEP is constructed using delicately designed cryptographic tools that perform bidding price validation, bidding price comparison, quality score aggregation, and winner declaration over encrypted values. Moreover, we customize a finer-grained algorithm for bidding price comparison to support price tie identification. Finally, we employ the blockchain technology to ensure a transparent, tamper-proof, and traceable implementation of SecTEP. Theoretical security analysis and performance evaluation ensure that SecTEP maintains a strong privacy of bidding prices while providing quality evaluation services for suppliers. Experimental results further demonstrate the validity of the theoretical analysis as well as the practical application of SecTEP in the real world.
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization

Motion capture (mocap) and time-of-flight based sensing of human actions are becoming increasingly popular modalities to perform robust activity analysis. Applications range from action recognition to quantifying movement quality for health applications. While marker-less motion capture has made great progress, in critical applications such as healthcare, marker-based systems, especially active markers, are still considered gold-standard. However, there are several practical challenges in both modalities such as visibility, tracking errors, and simply the need to keep marker setup convenient wherein movements are recorded with a reduced marker-set. This implies that certain joint locations will not even be marked-up, making downstream analysis of full body movement challenging. To address this gap, we first pose the problem of reconstructing the unmarked joint data as an ill-posed linear inverse problem. We recover missing joints for a given action by projecting it onto the manifold of human actions, this is achieved by optimizing the latent space representation of a deep autoencoder. Experiments on both mocap and Kinect datasets clearly demonstrate that the proposed method performs very well in recovering semantics of the actions and dynamics of missing joints. We will release all the code and models publicly.
RETRACTED: Research on the intelligent model of progress in physical education training based on motion sensor

Physical activity characteristics indicate the degree of mobility that underlies the chronic disease and aging process. Accelerometers have been broadly acknowledged as helpful and viable sensors as wearable gadgets that quantify and assess actual movement. It will consider developing a wearable accelerometer-based motion detector. The proposed Artificial Neural Network (ANN) system operates on the principle of accelerometer measurement. Sensor qualities and sensor show positions are first presented. Different investigations were audited utilizing accelerometer-based wearable movement identifiers and assessed utilizing actual action checking and evaluation, including postural and movement arrangement, energy consumption, fall identification, and assessment of equilibrium control appraisals. Finally, this work is evaluated compared to the current development status provided by a comprehensive outlook of possible technologies with existing commercial products based on the Field Programmable Gate Array (FPGA) tool. Technological advances have created new opportunities to strengthen education and improve the delivery of acquired Physical Education (PE) gaming skills. Determining Location Availability for Students with Practical Background Motion tracking systems can provide the right skills to support motor skills education in athletic teaching.
Efficient Distributed Decryption Scheme for IoT Gateway-based Applications

With the evolvement of the Internet of things (IoT), privacy and security have become the primary indicators for users to deploy IoT applications. In the gateway-based IoT architecture, gateways aggregate data collected by perception-layer devices and upload message packets to platforms, while platforms automatically push different categories of data to different applications. However, security in processes of data transmission via gateways, storage in platforms, access by applications is the major challenge for user privacy protection. To tackle this challenge, this article presents a secure IoT scheme based on a fine-grained multi-receive signcryption scheme to realize end-to-end secure transmission and data access control. To enhance the security of online application decryption keys, we design a distributed threshold decryption scheme based on secret-sharing. Moreover, from the provable security perspective, we demonstrate that the scheme can achieve the expected IND-CCA security and EUF-CMA security. After the performance analysis, evaluation results show that the computational performance is efficient and linearly subject to the number of messages and the number of receivers.