Reviewerly/exHarmony huggingface dataset:
Fast Pooling Based Multi-Scale Graph Neural Network and Its Application in Electric Power System

With the development of power system intelligence, data in the electric power system has shown high complexity and uncertainty. For this reason, the knowledge graph (KG) has been applied in various applications in the power grid. By combining with machine learning, deep learning, and other graph computing technologies, the knowledge graph can effectively find out the useful information in the data, and use the existing data information to optimize the business process and help in decision-making. However, the knowledge graph in the electric power system has multiple nodes, heterogeneous edges, and complex structures, and the applications on the knowledge graph are shallow. The traditional graph neural network models do not consider much on the topological structure of input graphs, and thus the problems of low efficiency and high complexity come out. In order to solve the problems mentioned above, we proposed a multi-scale graph neural network with fast pooling in this paper. This model could improve the computing efficiency through the fast calculation of the pooling layer, and prevent the decrease of the test accuracy by introducing a multi-scale network structure. Also, the model could adapt to different sizes of input graphs, where the pooling layer gave a fixed size of output regardless of the input size. According to the experimental results on the popular benchmark dataset PROTEINS and the self-built dataset CEPRI-Transmission, the proposed model outperforms baseline models when considering both accuracy and efficiency.
Credit Card Assent Using Supervised Learning

A credit card is used to permit the customer to make settlements of huge amount of money without carrying a lot of money which is need by the customers. It has changed the way reforming of making credits only in installments and made making such an installment helpful for the purchaser. One of the biggest problems faced in the banking sector is predicting a credit card client as a solution this paper uses Supervised Learning algorithm that must be applied for credit card approval for solving the problem. Additionally, the accuracy of the outcome is compared to several known standards using Supervised Learning techniques. The result shows that the ideal accuracy for KNN, Decision Tree, Naive Bayes, Logistic Regression are 98.50%, 98.13%, 99.62% and 84.32% respectively. The relative result exhibits that Decision Tree performs superior to other algorithms as in case of credit card assent. A website is created using FLASK python libraries, to connect the bank with the new entry customer. The proposed idea will help to avoid the risk in identifying and approving credit cards for the eligible customers.
Comprehending and Detecting Vulnerabilities using Adversarial Machine Learning Attacks

In today’s world, machine learning is an emerging technology which is being used extensively in different domains. In order to offer effective solutions in the broad area of computer security with the use of machine learning (ML) models, applications which identify and protect against potential adversarial attacks are employed. In the ever-growing field of adversarial machine learning, attackers with different extents of accessibility to a machine learning model can launch a number of attacks to achieve their goals. Concurrently, ML models and algorithms are quite susceptible to various cybersecurity threats. In this paper, an in-depth survey has been carried out on the impact of cybersecurity in machine learning and the adversarial attacks which can be encountered in a ML based system.
Fault Diagnosis of Secondary Equipment Based on Big Data of Smart Substation

In order to improve the capability of fault diagnosis and localization of secondary equipment in smart substation, a fault diagnosis method of secondary equipment based on big data of smart substation is proposed. the self-test status information, power supply status information, telegram information and sampling values of secondary equipment can be monitored and analyzed by establishing a fault diagnosis information database. The characteristics of abnormal information can be studied and the information of communication breakage, data abnormality, synchronization abnormality and alarms can be represented by fault feature set simultaneously. Using deep learning to train big data, a fault diagnosis model of secondary equipment in smart substations based on recurrent neural network (RNN) is established. The effectiveness of the proposed method is verified through simulation, and the precise location of secondary equipment faults is realized.
SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space

Translation distance based knowledge graph embedding (KGE) methods, such as TransE and RotatE, model the relation in knowledge graphs as translation or rotation in the vector space. Both translation and rotation are injective; that is, the translation or rotation of different vectors results in different results. In knowledge graphs, different entities may have a relation with the same entity; for example, many actors starred in one movie. Such a non-injective relation pattern cannot be well modeled by the translation or rotation operations in existing translation distance based KGE methods. To tackle the challenge, we propose a translation distance-based KGE method called SpaceE to model relations as linear transformations. The proposed SpaceE embeds both entities and relations in knowledge graphs as matrices and SpaceE naturally models non-injective relations with singular linear transformations. We theoretically demonstrate that SpaceE is a fully expressive model with the ability to infer multiple desired relation patterns, including symmetry, skew-symmetry, inversion, Abelian composition, and non-Abelian composition. Experimental results on link prediction datasets illustrate that SpaceE substantially outperforms many previous translation distance based knowledge graph embedding methods, especially on datasets with many non-injective relations. The code is available based on the PaddlePaddle deep learning platform https://www.paddlepaddle.org.cn/.
Cases of blasphemy and mental illness on the rise in Pakistan

Blasphemy has been a sin since the time of the Bible. Anti-blasphemy laws still exist in about a quarter of the world's countries and territories, the majority of which are Muslim. In Pakistan, blasphemy is punished by death. Frequently, crowds assassinate the accused before the matter even reaches the courtroom. Blasphemy charges are frequently leveled against people with mental conditions and religious minorities. In a blasphemy case, a psychiatric evaluation might inform the court of mental disorder symptoms. Psychiatrists can also assist asylum seekers who have been accused of blasphemy in Pakistan or another country that punishes it.
Recordism: A Social-Scientific Prospect of Blockchain from Social, Legal, Financial, and Technological Perspectives

Blockchain has the potential to reform the architecture of cyberspace and transform the storage, circulation and exchange of information through decentralization, transparency and de-identification. Meaning that ordinary participants can become traders, miners, retailers, and customers simultaneously, breaking the barriers and reducing the information gap between participants in the community, contributing to the futuristic metaverse with an open progressive and equal ideology. Such information transformation empowered by blockchain also profoundly impacts our methodological cognition, legal governance on cyberspace and financial and technological development.This study explores the main question: what are the implications of the blockchain-driven information revolution for society and social sciences? In order to answer this main question, this paper chooses four perspectives, which are methodological, legal, financial and technical. By analysis of these four perspectives, this paper is expected to provide a more comprehensive analysis of the blockchaindriven impact on society, social sciences, and technology to contribute to current scholarships. Additionally, regarding blockchain as an innovative methodological cognition, it grows on top of other technologies while helping advance other technologies. This paper concludes that although there are few frictions between blockchain and current social architecture, blockchain is so much more than the technology itself, that can be a representative of the community, acting as the source of trust, watcher of governance, law enforcer for virtual activities, and an incubator for future technologies.
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow

Super-resolution suffers from an innate ill-posed, problem that a single low-resolution (LR) image can be from multiple high-resolution (HR) images. Recent studies on the flow-based algorithm solve this ill-posedness by learning the super-resolution space and predicting diverse HR outputs. Unfortunately, the diversity of the super-resolution outputs is still unsatisfactory, and the outputs from the flow-based model usually suffer from undesired artifacts which causes low-quality outputs. In this paper, we propose FS-NCSR which produces diverse and high-quality super-resolution outputs using frequency separation and noise conditioning compared to the existing flow-based approaches. As the sharpness and high-quality detail of the image rely on its high-frequency information, FS-NCSR only estimates the high-frequency information of the highresolution outputs without redundant low-frequency components. Through this, FS-NCSR significantly improves the diversity score without significant image quality degradation compared to the NCSR, the winner of the previous NTIRE 2021 challenge.
CorrGAN: Input Transformation Technique Against Natural Corruptions

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be denoised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.
An End-to-End Chinese Text Normalization Model Based on Rule-Guided Flat-Lattice Transformer

Text normalization, defined as a procedure transforming nonstandard words to spoken-form words, is crucial to the intelligibility of synthesized speech in text-to-speech system. Rule-based methods without considering context can not eliminate ambiguation, whereas sequence-to-sequence neural network based methods suffer from the unexpected and uninterpretable errors problem. Recently proposed hybrid system treats rule-based model and neural model as two cascaded sub-modules, where limited interaction capability makes neural network model cannot fully utilize expert knowledge contained in the rules. Inspired by Flat-LAttice Transformer (FLAT), we propose an end-to-end Chinese text normalization model, which accepts Chinese characters as direct input and integrates expert knowledge contained in rules into the neural network, both contribute to the superior performance of proposed model for the text normalization task. We also release a first publicly accessible large-scale dataset for Chinese text normalization. Our proposed model has achieved excellent results on this dataset.
Bounding Box Distribution Learning and Center Point Calibration for Robust Visual Tracking

Visual tracking aims at both robust target classification and accurate localization. However, the reliability of the target bounding box and classification score are not properly addressed by most existing trackers, resulting in inaccurate tracking performance. In this paper, we propose to learn bounding box distribution in training and calibrate the center point response in inference for robust online tracking. Specifically, we propose a simple yet effective bounding box distribution learning (BDL) module to model the target bounding box distribution and enhance the localization ability of our network. Furthermore, we propose a center point calibration (CPC) module to calibrate the origin classification score with the predicted localization uncertainty and generate an accurate target center point. The proposed tracking method is referred to as DLPC. The experimental results on four challenging datasets (i.e., OTB100, VOT2019, LaSOT, and TrackingNet) show that DLPC performs favorably against several state-of-the-art trackers while running in real-time at 60 fps.
Double Closed-Loop Network for Image Deblurring

In this paper, a deep learning network with double closed-loop structure is introduced to tackle the image deblurring problem. The first closed-loop in our model is composed of two networks which learn a pair of opposite mappings between the blurry and sharp images. By this way, the solution spaces of possible functions that map a blurry image to its sharp counterpart can be effectively reduced. Furthermore, the first closed-loop also helps our model to deal with the unpaired samples in the training set. The second closed-loop in the proposed approach employed a self-supervision mechanism to constrain the features of intermedia layers in the network, so that the detailed information of sharp images can be well exploited. Through combining the two closed-loops together, our model can address the limitations of existing methods and improve the deblurring performance. Extensive experiments on both benchmark and real-world datasets show that the proposed network achieves state-of-the-art performance. The code will be released in: https://github.com/LiQiang0307/DCLNet.
Global Evolution Neural Network for Segmentation of Remote Sensing Images

The popular convolutional neural networks (CNNs) have been successfully used in very high-resolution remote sensing image semantic segmentation. However, these networks often suffer from performance limitations. First, although deeper networks usually provide better feature representation, they may cause parameter redundancy and the inefficient use of prior knowledge. Secondly, attention-based networks often only focus on weighting different features of a single sample but ignore the correlation of all samples in training set, thus leading to the loss of global information. To address above issues, we propose two simple yet effective global evolution strategies. The first is knowledge enhancement. This strategy can reactivate invalid convolutional kernels through convergence of different models and make full use of prior knowledge from the network to improve its feature representation. The second is a dict-attention module that greatly enhances the generalization of networks by learning and inferring the global relationship among different samples through the dictionary unit. As a result, a novel global evolution network (GENet) is designed based on knowledge enhancement and dict-attention for remote sensing image semantic segmentation. Experiments demonstrate that the proposed GENet is not only superior to popular networks in segmentation accuracy.
What Is The Patient Looking At? Robust Gaze-Scene Intersection Under Free-Viewing Conditions

Locating the user’s gaze in the scene, also known as Point of Regard (PoR) estimation, following gaze regression is important for many downstream tasks. Current techniques either require the user to wear and calibrate instruments, require significant pre-processing of the scene information, or place restrictions on user’s head movements.We propose a geometrically inspired algorithm that, despite its simplicity, provides high accuracy and O(J) performance under a variety of challenging situations including sparse depth maps, high noise, and high dynamic parallax between the user and the scene camera. We demonstrate the utility of the proposed algorithm in regressing the PoR from scenes captured in the Intensive Care Unit (ICU) at Chelsea & Westminster Hospital NHS Foundation Trust <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> .
MEJIGCLU: More Effective Jigsaw Clustering For Unsupervised Visual Representation Learning

Unsupervised visual representation learning aims to learn general features from unlabelled data. Early methods design intra-image pretext tasks as learning targets and can be achieved with low computational overhead but unsatisfactory performance. Recent methods introduce contrastive learning and achieve surprising performance, but multiple views of training data are required in one batch, resulting in high computational overhead. To achieve competitive results to contrastive learning with low computational overhead, we propose a new unsupervised representation learning method with jigsaw clustering and classification as pretext tasks motivate the network to learn discriminative feature. To increase the data diversity, we propose to partition each training image into patches with random overlap, then randomly permute and stitch them into new training batch. Comparing with SOTAs, our method achieves state-of-the-art performance on both image classification/semi-classification on ImageNet and object detection on COCO.
The weight distributions of linear sets in PG(1,q5)

In this paper, we study the weight distributions of Fq-linear sets in PG(1,q5). Our Main Theorem proves that a linear set S of rank 5, which is not scattered has the following weight distribution for its points with weight larger than 1: (i) one point of weight 4 or 5, (ii) one point of weight 3 and 0, q, or q2 points of weight 2, (iii) s points of weight 2 where s∈[q−2q+1,q+2q+1]∪{2q,2q+1,2q+2,3q,3q+1,q2+1}. In particular, there are no 2-clubs in PG(1,q5).
DGC-Vector: A New Speaker Embedding for Zero-Shot Voice Conversion

Recently, more and more zero-shot voice conversion algorithms have been proposed. As a fundamental part of zero-shot voice conversion, speaker embeddings are the key to improving the converted speech’s speaker similarity. In this paper, we study the impact of speaker embeddings on zero-shot voice conversion performance. To better represent the characteristics of the target speaker and improve the speaker similarity in zero-shot voice conversion, we propose a novel speaker representation method in this paper. Our method combines the advantages of D-vector, global style token (GST) based speaker representation and auxiliary supervision. Objective and subjective evaluations show that the proposed method achieves a decent performance on zero-shot voice conversion and significantly improves speaker similarity over D-vector and GST-based speaker embedding.
An Anomaly Detection Method Based on Self-Supervised Learning with Soft Label Assignment for Defect Visual Inspection

Recently, local-editing-based transformations are introduced in anomaly detection for defect visual inspection, which construct a pretext task with the paradigm of self-supervised learning. However, supervised information of local-editing-based transformation may be incorrect when invalid trans-formation occurs in the pretext task. The reason is that the conventional method to generate labels ignores the differences of images between before and after the transformation. To address this issue, we propose soft label assignment (SLA) to construct soft labels via measuring the similarity between the original and transformed images. Meanwhile, a novel self-supervised learning-based anomaly detection method is proposed for defect visual inspection, which exploits local-editing-based transformation with SLA as a pretext classification task. A convolutional neural network (CNN) is trained to extract deep features of ambiguity and irregularity by the pretext classification task. In the main task, an anomaly detection is modeled via the deep representations to estimate defects regarded as anomalies. Experimental results demonstrate the effect of SLA, and the proposed method achieves superior performance than state-of-the-art methods in terms of the receiver operating characteristic curve (AUC-ROC).
Maximum Batch Frobenius Norm for Multi-Domain Text Classification

Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning. Recently, many endeavors are devoted to applying adversarial learning to extract domain-invariant features to yield state-of-the-art results. However, these methods still face one challenge: transforming original features to be domain-invariant distorts the distributions of the original features, degrading the discriminability of the learned features. To address this issue, we first investigate the structure of the batch classification output matrix and theoretically justify that the discriminability of the learned features has a positive correlation with the Frobenius norm of the batch output matrix. Based on this finding, we propose a maximum batch Frobenius norm (MBF) method to boost the feature discriminability for MDTC. Experiments on two MDTC benchmarks show that our MBF approach can effectively advance state-of-the-art performance.
Wide-Sense Stationarity and Spectral Estimation for Generalized Graph Signal

We consider a probabilistic model for graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space. We introduce the notion of joint wide-sense stationarity in this generalized GSP (GGSP) framework, which allows us to characterize a random graph process as a combination of uncorrelated oscillation modes across both the vertex and Hilbert space domains. We also propose a method for joint power spectral density estimation in case of missing features. Experiment results corroborate the effectiveness of our estimation approach.
Online Continual Learning Using Enhanced Random Vector Functional Link Networks

We propose an online continual learning algorithm based on an enhanced Random Vector Functional Link Network (OCL-eRVFL), that learns a sequence of tasks continually, where each task is defined by streaming data with each sample arriving once and only once. As data for a new task in domain incremental or class incremental setting streams in, the output weights of an eRVFL is updated through Recursive least squares, such that the representations for the past tasks are not catastrophically forgotten. As the recursive least square update is based only on the currently streaming sample, samples are not stored. Hence, unlike state-of-the-art OCL that avoid catastrophic forgetting through memory replay of samples from past task, the proposed OCL-eRVFL needs no extra memory. The proposed OCL-eRVFL is evaluated on streaming split CIFAR10, split CIFAR100 and split CIFAR10/100 image classification data sets within a class and domain incremental setting. Performance results show that the proposed OCL-eRVFL efficiently learns a sequence of tasks with streaming data, without additional memory expense.