Reviewerly/exHarmony huggingface dataset:
Generalized neural network trained with a small amount of base samples: Application to event detection and phase picking in downhole microseismic monitoring

The deep-learning method has been successfully applied to many geophysical problems to extract features from seismic big data. However, some applications may not have sufficient available data to directly train a generalized neural network. We have applied data augmentation on a significantly small number of samples to train a generalized neural network for microseismic event detection and phase picking, which could be used in different project settings and areas. We use the U-Net architecture consisting of 2D convolutional layers to create the prediction function, and we map the waveforms recorded by using multiple receivers to the P/S arrival time labels; thus, the neural network can learn the P/S moveout features from multiple receivers. The training set is generated by simulating various realizations of the data based on 10 original samples from the beginning of a hydraulic fracturing stage. The trained neural network is then used to detect the events and pick the P/S phases from the continuous data for different stages and projects. A grid search from a precalculated traveltime table is performed to determine the event location after an event is detected. We build a real-time event detection and location workflow without human intervention by combining the neural network and grid search method, and we apply the workflow to a different stage from the training events and a completely independent project that the neural network has not encountered. The results indicate that microseismic events are successfully detected and located, and the picking performance of the neural network is superior to that of a traditional autoregression picker.
Graph-Based Knowledge Acquisition With Convolutional Networks for Distribution Network Patrol Robots

With the popularization of smart grids, patrol robots have become critical devices in the distribution networks to check the state of equipment. In order to enrich the knowledge of patrol robots in this complex scenario, this article presents the graph-based knowledge acquisition method with convolutional networks for distribution network patrol robots. The proposed method uses a graph convolutional network-based path-related embedding algorithm to complete the knowledge of the distribution network knowledge graph. The proposed algorithm generates the embeddings of entities and relations through aggregating the associated entities in the associated paths, instead of only the connected entities. The graph convolutional network consists of multiple graph convolution layers, and the message-passing process treats different entities discriminatorily according to the association strengths. For determining the plausibility of the knowledge, a scoring function is provided with the convolution operator. The experimental datasets are from a real grid company and contain four kinds of equipment. The experiments apply the proposed method to the equipment defects analysis, including the defect gradation, the coarse defect classification, and the fine defect classification. The proposed method is compared with some embedding methods. The experimental results verify that the proposed method outperforms the other methods for the real distribution network datasets, and the proposed method can analyze the defects effectively.
Sequential Recommendation on Dynamic Heterogeneous Information Network

The sequential recommendation has been widely used to predict users' preferences in the near future by utilizing their dynamic interactions with items. However, existing methods only consider single-typed interactions (e.g., purchase), ignoring the rich heterogeneous information such as multi-typed interactions (e.g., click, purchase) and item attributes (e.g, category), which leads to a suboptimal model. We can integrate this rich information by introducing Dynamic Heterogeneous Information Networks (DHINs). Our solution contains three special designs: 1) Static Initialization; 2) Heterogeneous User Memory Network; 3) Two-level attention mechanism. Extensive experiments conducted on two real-world datasets show that our model outperforms other state-of-the-art solutions. Furthermore, we provide some insights into parameter settings and model interpretability.
Accurate Item Recommendation Algorithm of itemrank based on tag and context information

The traditional itemrank recommendation algorithm only uses the two-dimensional relationship between user and item to achieve recommendation, without considering the important information (such as label information and context information) that plays an important role in the association between user and item, so the accuracy of recommendation needs to be improved. Therefore, in this paper, tag information and context information are integrated into the link relationship between user and item, and a user–context–item tag association graph is constructed. An itemrank recommendation algorithm is proposed by integrating tag and context information. Firstly, ap-ml-rbf-relm model is used to determine user labels, and then deep neural network is used to determine the relationship between tags. Finally, item and label are calculated The association weight between the signatures is used to implement the recommendation service for the target users. Experimental results on public datasets show that the proposed algorithm is better than the traditional recommendation algorithm in recommendation accuracy.
Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

A growing number of research papers shed light on automated machine learning (AutoML) frameworks, which are becoming a promising solution for building complex machine learning models without human expertise and assistance. The key challenge in enabling AutoML frameworks to build an efficient model for anomaly detection tasks is to determine the best underlying model for a given task and optimization metric. The meta-learning approaches based on a set of meta-features that describes data properties can enable efficient model selection in AutoML frameworks. The existing meta-learning approaches based on statistical and information-theoretic meta-features require large amounts of data and computational resources to extract data properties. This paper proposes a novel set of meta-features for model selection in anomaly detection tasks based on domain-specific properties of data which overcomes the shortcomings of existing meta-features by introducing simple but effective meta-features that can be efficiently extracted or estimated by using a low amount of data. Experiments with 63 datasets from different repositories with varying schemas show that the proposed set of meta-features achieves an accuracy of 87% for model selection, while the achieved accuracy for simple meta-features is 74%, for statistical meta-features 68%, for information theory meta-feature 70%, and for a comprehensive set of meta-features by pyMFE 73%. This demonstrates that the proposed set can be adopted by AutoML frameworks across a diverse range of domains.
Single and multiple regions duplication detections in digital images with applications in image forensic

‘With the help of powerful image editing software, various image modifications are possible which are known as image forgeries. Copy-move is the easiest way of image manipulation, wherein an area of the image is copied and replicated in the same image. The major reason for performing this forgery is to conceal undesirable contents of the image. Thus, means are required to unveil the presence of duplicated areas in an image. In this article, an effective and efficient approach for copy-move forgery detection (CMFD) is proposed, which is based on stationary wavelet transform (SWT), speeded-up robust features (SURF), and a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering. The SWT allows the SURF descriptor to extract only energy-rich features from the input image. The SURF features can detect the tampered regions even under post-processing attacks like contrast adjustment, scaling, and affine transformation on the images. On the extracted features, a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering algorithm is applied to detect forged regions with high accuracy as it can easily identify the clusters of arbitrary shapes and sizes and can filter the outliers. For performance evaluation, three publicly available datasets namely MICC-F220, MICC-F2000, and image manipulation dataset (IMD) are employed. The qualitative and quantitative analysis demonstrates that the proposed approach outperforms state-of-the-art CMFD approaches in the presence of different post-processing attacks.
Neural network‐assisted robotic vision system for industrial applications

Abstract This paper investigates the performance of neural networks in positioning an industrial robot manipulator based on image feedback. Visual servoing regulates the pose of the robot manipulator in accordance with the target image data. As the servoing proceeds, the end‐effector positions to its final pose as the image feature error exponentially decreases to zero. In this paper, the trained network moves the robot manipulator from an arbitrary initial pose to an intermediate posture based on the reference image features given. Then, fine‐tuning based on the traditional visual servoing method is performed to achieve an accurate pick‐and‐place task with minimum iterations. The experimental results prove the capability of the neural network architectures to predict the desired pose using local image descriptor, corner points. This paper investigates the performance of two neural network designs applied to visual positioning and compares them with the traditional image‐based visual servoing (IBVS) method in terms of execution time.
TransNAS-Bench-101: Improving transferability and Generalizability of Cross-Task Neural Architecture Search

Recent breakthroughs of Neural Architecture Search (NAS) extend the field’s research scope towards a broader range of vision tasks and more diversified search spaces. While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks. Many of them leverage transfer learning and seek to preserve, reuse, and refine network design knowledge to achieve higher efficiency in future tasks. However, the enormous computational cost and experiment complexity of cross-task NAS are imposing barriers for valuable research in this direction. Existing NAS benchmarks all focus on one type of vision task, i.e., classification. In this work, we propose TransNAS-Bench-101, a benchmark dataset containing network performance across seven tasks, covering classification, regression, pixel-level prediction, and self-supervised tasks. This diversity provides opportunities to transfer NAS methods among tasks and allows for more complex transfer schemes to evolve. We explore two fundamentally different types of search space: cell-level search space and macro-level search space. With 7,352 backbones evaluated on seven tasks, 51,464 trained models with detailed training information are provided. With TransNAS-Bench-101, we hope to encourage the advent of exceptional NAS algorithms that raise cross-task search efficiency and generalizability to the next level. Our dataset and code will be available at Mindspore <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and VEGA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Group Whitening: Balancing Learning Efficiency and Representational Capacity

Batch normalization (BN) is an important technique commonly incorporated into deep learning models to perform standardization within mini-batches. The merits of BN in improving a model’s learning efficiency can be further amplified by applying whitening, while its drawbacks in estimating population statistics for inference can be avoided through group normalization (GN). This paper proposes group whitening (GW), which exploits the advantages of the whitening operation and avoids the disadvantages of normalization within mini-batches. In addition, we analyze the constraints imposed on features by normalization, and show how the batch size (group number) affects the performance of batch (group) normalized networks, from the perspective of model’s representational capacity. This analysis provides theoretical guidance for applying GW in practice. Finally, we apply the proposed GW to ResNet and ResNeXt architectures and conduct experiments on the ImageNet and COCO benchmarks. Results show that GW consistently improves the performance of different architectures, with absolute gains of 1.02% ∼ 1.49% in top-1 accuracy on ImageNet and 1.82% ∼ 3.21% in bounding box AP on COCO.
Compiling basic linear algebra subroutines for quantum computers

Efficiently processing basic linear algebra subroutines is of great importance for a wide range of computational problems. In this paper, we consider techniques to implement matrix functions on a quantum computer. We embed given matrices into 3 times larger Hermitian matrices and assume as input a given set of unitary operators generated by the embedding matrices. With the matrix embedding formula, we give Trotter-based quantum subroutines for elementary matrix operations include addition, multiplication, Kronecker sum, tensor product, Hadamard product, and arbitrary real eigenvalue single-matrix functions. We then discuss the composed matrix functions in terms of the estimation of scalar quantities such as inner products, traces, determinants, and Schatten p-norms with bounded errors. We thus provide a framework for compiling instructions for linear algebraic computations into gate sequences on actual quantum computers. The framework for calculating the matrix functions is more efficient than the best classical counterpart for a set of matrices.
Deep Learning Methods for Object Detection in Autonomous Vehicles

The automotive industry and researchers have recently shown an interest in autonomous vehicles. In an autonomous vehicle, different technologies may be used. Radio Detection and Ranging Technology (RADAR), Light Image Detection and Ranging Technology (LiDAR) and computer vision are commonly recognized techniques. Computer vision is a method of extracting significant features from a digital image that allows the computer to perceive the features of objects and interpret the image. In recent research, it has been found that methods of deep learning can detect objects in real time. In the current review, approaches focused on CNN are discussed and their performances are compared.
Blockchain-enabled Tensor-based Conditional Deep Convolutional GAN for Cyber-physical-Social Systems

Deep learning techniques have shown significant success in cyber-physical-social systems (CPSS). As an instance of deep learning models, generative adversarial nets (GAN) model enables powerful and flexible image augmentation, image generation, and classification, thus can be applied to real-world CPSS settings. GAN model training needs a large collection of cyber-physical-social data originating from various CPSS devices. Numerous prevailing GAN models depend on a tacit assumption that several cyber-physical-social data providers present a reliable source to collect training data, which is seldom the case in real CPSS. The existing GAN models also fail to consider multi-dimensional latent structure. In our work, we put forward a novel blockchain-enabled tensor-based conditional deep convolutional GAN (TCDC-GAN) model for cyber-physical-social systems. The blockchain is employed to develop a decentralized and reliable cyber-physical-social data-sharing platform between numerous cyber-physical-social data providers, such that the training data and the model are documented on a ledger that is distributed. Furthermore, a tensor-based generator and a tensor-based discriminator are well designed by employing the tensor model. The results of extensive simulation experiments show the efficacy of the proposed TCDC-GAN model. Compared with the state-of-the-art models, our model gains superior estimation performance.
Finding Facial Forgery Artifacts with Parts-Based Detectors

Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day. In this paper, we seek to develop a generalizable, explainable solution to detecting these manipulated videos. To achieve this, we design a series of forgery detection systems that each focus on one individual part of the face. These parts-based detection systems, which can be combined and used together in a single architecture, meet all of our desired criteria - they generalize effectively between datasets and give us valuable insights into what the network is looking at when making its decision. We thus use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deep-fake Detection Challenge datasets, examining not just what the detectors find but also collecting and analyzing useful related statistics on the datasets themselves.
Some thoughts on knowledge-enhanced machine learning

How can we employ theoretical insights and practical tools from knowledge representation and reasoning to enhance machine learning, and when is it worthwhile to do so? This paper is based on an invited talk delivered at ECSQARU2019 around this question. It emphasizes the knowledge representation and reasoning side of knowledge-enhanced machine learning, looking at a few case studies: the finite model theory of probabilistic languages, the generation of explanations for embeddings, and an "explainable" version of the Winograd Challenge.
Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.
Algorithms for face recognition drones

This paper elucidates diverse algorithms that are used in drones for face identification. Face identification is the thriving technology that utilizes image processing for the identification of human faces. The main reasons for the booming interest in face recognition include the rise in population which demands high security and surveillance systems, requisite for identity verification in the digital world, for the combat in rural areas, alleviation during disasters, and so on. The goal of this paper is to explore, compare and contrast numerous face identification algorithms such as Linear Discriminant Analysis (LDA), Local Binary Pattern Histogram (LBPH), Principal Component Analysis (PCA), Elastic Bunch Graph Matching (EBGM) and neural networks. This study would assist the technologists in the field of face recognition to frame a hybrid algorithm based on the needs of the real time applications.
Phishing Web Page Detection Using N-gram Features Extracted From URLs

Recently, cyber-attacks have increased worldwide, especially during the pandemic period. The number of connected devices in the world and the anonymous structure of the internet enable this security deficit for not only computer networks but also single computing devices. With the connected use of computing device in anytime and anywhere conditions, lots of real-world activities are transferred to the digital world by adapting them to new lifestyles. Thus, the concept of cybersecurity has become more focused not only for security admins but also for academicians/researchers. Phishing attacks, which hackers mostly prefer to use in the last decade, have become even more harmful because its focuses on the weakest part of the security chain: computer user. Therefore, it is extremely important to prevent these cyber-attacks before they reach users. Based on this idea, we aimed to implement a phishing detection system by using a Convolutional Neural Network with n-gram features that are extracted from URLs. There are different n-gram feature extraction techniques, and in this work, it is aimed to determine which of them is more effective for our proposals. As a second goal, it is aimed to discover what parameters of the n-gram work best. In experiments, it is discovered that unigram has the highest accuracy rate. It was observed that, instead of all the characters that are obtained in unigram, the specified 70 characters (regardless of case sensitivity) give the highest accuracy rate of 88.90% with a High-Risk URL dataset. Experimental results also showed that a URL can be classified (either as legitimate or phishing) in about 0.008 seconds. These metrics can be accepted at a very good rate both in accuracy and run-time efficiency.
Technological and knowledge diffusion link: An international perspective 1870–2019

Technology diffusion is a necessary but not sufficient condition for knowledge diffusion. Technological waves' impact on education differs across educational levels. We use data for 104 countries on technology diffusion and education from 1870 to 2019. We find six technology waves from 1870 to 2019 had a substantial and statistically significant educational attainment impact using fixed panel data modeling. The impact differs across time and regions, with the most noticeable impact in Advanced economies and Eastern Europe. Asia and the Pacific, Latin America and the Caribbean, Middle East, and North Africa are catching up with lagging Sub-Saharan Africa. The transportation revolution increased the average years in primary and secondary education. Telecommunications affects college and graduates' schooling. IT waves show a focused impact on tertiary education, like the health technology revolution (health wave having a significant impact on secondary education). Steel production (oxygen blast furnaces) shows no knowledge spillover effect. The invention of electricity is the one to spark knowledge diffusion by increasing average primary schooling by 3.47 months (1930–1950).