Reviewerly/exHarmony huggingface dataset:
Virtualization, Virtual Reality, and Ethics

This chapter discusses the new developments and consequences of virtualization technology and its implications on our participation and how the technology informs our behavior based on our traditional moral and ethical values. In a more detailed way, we define virtualization as a process that embodies both abstraction and reconstruction and as it creates a sense of complete participants' immersion yet with the autonomyAutonomy of participants to vary their chosen new environments to suit individual likings. As defined, virtualization, therefore, conjures uncertainty and fear not of the environment but of the individual who partakes in the activities of the environment. Our discussion of both social and ethical issuesEthical:issues that arise within and outside the environment focused on two types of virtualization: computing resources virtualization and virtual reality (VR)Virtual Reality (VR). We note that virtualization is also bringing about the easy creation of new human identities in the new virtual environments which makes authenticationAuthentication more difficult but at the same time creates unprecedented potential in self-creation and self-presentation. We note, however, that while these benefits may bring new opportunities and new potential that may empower individuals to new levels of creativity, these unparalleled opportunities of virtualization may come at a price to society.
Intelligent Information Technology for Structural Optimization of Fuzzy Control and Decision-Making Systems

This chapter is devoted to the development and research of intelligent information technology (IIT) for structural optimization of fuzzy systems (FS) based on the evolutionary search of the optimal membership functions. The proposed IIT uses a combination of different (two or more) bioinspired evolutionary algorithms and allows finding the optimal membership functions of linguistic terms at solving the compromise problems of multi-criteria structural optimization of various FSs to increase their efficiency, as well as to reduce the degree of complexity of further parametric optimization. To study the effectiveness of the considered IIT, the search of the optimal membership functions is conducted for a FS of the multi-purpose mobile robot (MR) designed to move along inclined and vertical ferromagnetic surfaces, with the implementation of three evolutionary algorithms: genetic, artificial immune systems, biogeographic. The analysis of the obtained results showed that the usage of the proposed IIT gives the opportunity to significantly increase the efficiency of the MR control, as well as to reduce the total number of parameters at further parametric optimization of linguistic terms, which confirms its high efficiency.
Swing-Up and Balance Control of Cart-Pole Based on Reinforcement Learning DDPG

As a typical strong artificial intelligence method, reinforcement learning has been applied to real control tasks. Cart-pole system is an ideal controlled object, which is often used to verify the feasibility of control theory. In order to explore the effect of continuous reinforcement learning in the control of physical systems, this paper proposes a full control method for the swing up and stabilization of the cart-pole based on reinforcement learning DDPG. By interacting the cart-pole model with the main body of reinforcement learning, the Actor-Critic framework and the deterministic gradient algorithm DDPG are used to complete the learning of the cart-pole and realize the whole process of swing-up and balance control. Finally, the stability and effectiveness of reinforcement learning for the control of cart-pole are verified by simulation and experiment.
Flexible Formation Control of Multiple Unmanned Vehicles Based on Artificial Potential Field Method

Aiming at the problems of high complexity, strong coupling, and difficulty in carrying out experiments in the unmanned boat formation control algorithm at the present stage, this paper designs a multi-unmanned boat flexible formation controller based on the artificial potential field method, which will have nonlinear dynamic characteristics motion control problem of flexible formation of unmanned boats is transformed into the problem of tracking dynamic targets by unmanned boats. The controller adopts the idea of hierarchical cascading control, the planning layer introduces a virtual leader, and designs the swarm motion control mode under the condition of maintaining connectivity; the control layer uses a PID controller to help the unmanned vehicle track the dynamic continuous target generated by the planning layer, so as to realize the flexible formation motion control of multiple unmanned vehicles. Finally, the simulation verification of the flexible formation motion control algorithm is carried out for the static and dynamic virtual piloting situations.
An intelligent recommendation method based on multi-interest network and adversarial deep learning

Recommender systems have shown to popular in many Internet communities, as they could help users discover interesting items based on their history behaviors. However, with the explosive growth of data-intensive tasks and online information, cybersecurity risks become larger, conventional collaborative recommendation algorithms may not meet users’ security requirements. Besides, the sparsity issue and the cold-start issue also hinder the performance of conventional recommendation methods. Recently, deep learning has shown to outperform traditional modeling techniques, which can be employed in Recommender systems (RSs) to improve user behavior prediction. In light of these challenges and observations, an intelligent recommendation method based on multi-interest network and adversarial deep learning is proposed, where multi-source behavior information is applied for multi-view embedding extraction for better prediction performance. Specifically, multi-view preference embeddings, including self-embedding, interaction-aware embedding, and neighbor-based embedding, are combined to model users’ interests at a finer granularity. Besides, in neighbor-based embedding learning, an adversarial search scheme is adopted for fast similarity searching and privacy preservation. Finally, a DNN-based prediction mechanism is adopted for embedding aggregation and final prediction. Extensive experiments on real-world datasets show that our proposal achieves decent prediction performance with security concerns compared with state-of the-art baselines.
SherVine: A graphical dependency modeling for shearlet transform and its application in image quality assessment

Many applications of image processing, statistical modeling of frequency domain coefficients is considered by researchers as an approach. In this paper, we present a probabilistic graphical model based on the Vine Copula approach called SherVine(Shearlet Vine) to model the shearlet transform coefficients jointly. The proposed model is an extension of joint modeling with the help of Copula’s theory, which can diversely model a pair of shearlet coefficients. Also, the graphical representation of SherVine leads to a better understanding of dependencies types between shearlet coefficients. In the second part of this paper, we propose a novel image quality assessment(IQA) approach based on SherVine called IQA-SherVine. IQA-SherVine uses the parameters of the SherVine model as a feature in the IQA framework. The extracted features are modeled using the decision tree algorithm in IQA-SherVine. The results show that IQA-SherVine performs very well compared to the statistical and deep learning based state-of-the-art methods.
Pulsar Candidate Classification Using a Computer Vision Method from a Combination of Convolution and Attention

Abstract Artificial intelligence methods are indispensable to identifying pulsars from large amounts of candidates. We develop a new pulsar identification system that utilizes the CoAtNet to score two-dimensional features of candidates, implements a multilayer perceptron to score one-dimensional features, and relies on logistic regression to judge the corresponding scores. In the data preprocessing stage, we perform two feature fusions separately, one for one-dimensional features and the other for two-dimensional features, which are used as inputs for the multilayer perceptron and the CoAtNet respectively. The newly developed system achieves 98.77% recall, 1.07% false positive rate (FPR) and 98.85% accuracy in our GPPS test set.
Evaluation of thermomechanical behavior of electronic devices through the use of a reduced order modelling approach

Warpage is an issue in the manufacturing of electronic packages. The main driver for this effect is the mismatch of material properties especially during temperature cycling. To predict warpage before building samples, the FEM (Finite Element Method) is used in this study. PCBs (Printed Circuit Boards) are assembled in array format. Thus the simulation should be done in this format. Studies investigated PCBs thru a detailed representation [1]. However, no studies were found which simulate an array of PCBAs (Assembled PCBs). This is mainly due limitation of the hardware memory. The array of m2x modules in this work is not feasible to be calculation thru a usual modelling approach, even not through cloud computing. Thus this study investigates the use of substructures to overcome the hardware limitations. The array was manufactured and the simulation results were validated by using DIC (Digital Image Correlation). The predicted warpage of the novel modelling approach is well aligned with the measurement results.The establishment of the method for array format warpage assessment significantly decreases the development times for electronic packages, due to the mitigation of reliability risks and thus design cycles.
Development of an Algorithm for Optimal Encoding of WAV Files Using Genetic Algorithms

The purpose of this scientific work is to develop an algorithm for optimal encoding of WAV files using genetic algorithms, which simultaneously combines an acceptable encoding speed and a higher compression ratio of audio data than classical encoding methods. The article discusses the structure of WAV files; the principle of Huffman coding has been studied, and the process of compressing WAV files using this algorithm has been described; on the basis of Huffman coding with the help of genetic algorithms, an algorithm for optimal encoding of WAV files was developed and tested; based on the obtained results, conclusions are drawn about the work done.
Remote sensing using drone and machine learning for computation of rooftop solar energy potential

The decision to use a renewable form of energy generation was unavoidable due to the rapidly rising demand for energy and to achieve sustainability goals. Rooftop solar photovoltaic energy generation utilizes space for panel installation effectively and with the least amount of transmission losses. Using conventional methods, determining the appropriate rooftop space for solar PV systems is a laborious operation. The use of orthophotos and aerial imageries has shown to be effective for mapping effective roofs and taking accurate measurements. Utilizing automated machine learning approaches would make it easier to obtain the footprints for large geographical areas. The data collected a remote sensing imaging drone is used in this study to create an orthophoto and digital surface model. The development of deep learning and growing computational power have benefited remote sensing images and geospatial analysis. This research experiment is to extract building roofs appropriate for solar PV installations in a village setting. For the entire research region chosen, the model predicts against manually created ground truth maps. The potential for electricity generation from the existing, vacant building roofs is demonstrated via solar rooftop energy assessment. Such outputs would be used by authorities for complex spatial analysis in decision-making in coming days. The models created are intended to automatically generate vital spatial data sets incorporating geographic as well as geometric properties.
Local Path Planning with Turnabouts for Mobile Robot by Deep Deterministic Policy Gradient

This paper proposes local path planning with turn-abouts for a mobile robot by deep deterministic policy gradient (DDPG). DDPG is one of the actual reinforcement learning methods. This paper focuses on a non-holonomic mobile robot that has a minimum turning radius. Narrow roads exist in human living areas such as homes, commercial facilities, and factories. In this paper, a narrow road is defined as an impassable road with the minimum turning radius of the robot. Therefore, local path planning with turnabouts is needed for a mobile robot to pass a narrow road. However, most conventional local path planning methods do not consider turnabouts, since these methods select only forward velocity. This paper generates the local path planning which consists of forward and backward motion by using DDPG. For the trained model, simulation is used to obtain optimal velocity by minimizing the long-term reward. The reward is set considering goal arrival, number of turnabouts, and obstacle avoidance. The validity of the proposed local path planning by DDPG was confirmed by simulation and experimental results.
SFAF-MA: Spatial Feature Aggregation and Fusion With Modality Adaptation for RGB-Thermal Semantic Segmentation

The fusion of RGB and thermal images has profound implications for the semantic segmentation of challenging urban scenes, such as those with poor illumination. Nevertheless, existing RGB-T fusion networks pay less attention to modality differences; i.e., RGB and thermal images are commonly fused with fixed weights. In addition, spatial context details are lost during regular extraction operations, inevitably leading to imprecise object segmentation. To improve the segmentation accuracy, a novel network named spatial feature aggregation and fusion with modality adaptation (SFAF-MA) is proposed in this paper. The modality difference adaptive fusion (MDAF) module is introduced to adaptively fuse RGB and thermal images with corresponding weights generated from an attention mechanism. In addition, the spatial semantic fusion (SSF) module is designed to tap into more information by capturing multiscale perceptive fields with dilated convolutions of different rates, and aggregate shallower-level features with rich visual information and deeper-level features with strong semantics. Compared with existing methods on the public MFNet dataset and PST900 dataset, the proposed network significantly improves the segmentation effectiveness. The code is available at https://github.com/hexunjie/SFAF-MA.
Soft Sensor Model for Billet Temperature in Multiple Heating Furnaces Based on Transfer Learning

Billet heating temperature directly affects the quality of the billet, but the existing technology cannot measure the billet surface temperature. Therefore, we accurately predict the temperature of the furnace by a soft sensor to approximate the billet temperature. Limited by the complexity of the heating process and the lack of computing resources in factories, the existing research pays little attention to or even cannot meet the temperature prediction requirements of multiple heating zones in multiple heating furnaces. To address the above problems, we propose a temperature prediction method for multi-heating furnaces based on transfer learning and knowledge distillation. The method establishes a multisource domain model of a source domain furnace on a cloud platform. Then, the multisource knowledge is transferred to the target domain heating furnace, and the target domain teacher model is established by fine-tuning the source domain model through the target domain data. Next, to efficiently predict the furnace temperature, a shallow multi-task student model is established at the edge server to predict multiple heating zones in the target furnace. Furthermore, a knowledge distillation method for regression prediction is proposed so that the student model can improve the prediction accuracy under the guidance of the cloud teacher model. The effectiveness of the method is verified by experiments on 20 different heating zone datasets in two heating furnaces and two wind turbine datasets. The consistency of multiple experiments shows that this method can not only improve the accuracy by transferring the source domain knowledge but also reduce the size of model parameters by knowledge distillation on the premise of meeting the requirements of prediction error.
Doubly Stochastic Distance Clustering

In doubly stochastic (DS) clustering, it is common to initialize the DS matrix with a similarity matrix and use the eigen-decomposition of the DS-scaled similarity matrix to obtain the optimal cluster indicators. The selection of a proper initial similarity measure, however, is a difficult problem and the eigen-decomposition is time-consuming, with time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{3})$ </tex-math></inline-formula> where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the data size. In this paper, we propose to replace the DS similarity matrix with the DS Euclidean distance matrix for clustering. We show that the optimal cluster indicators minimize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -medoids error of data with DS Euclidean distance. We propose a fast method to obtain data with DS distance for clustering. Compared with DS similarity clustering, DS distance clustering is kernel-free and of low time complexity, typically <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(nd^{2}+d^{3})$ </tex-math></inline-formula> where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> is the input dimension. Experimental results on real-world datasets and the image segmentation task verify the superiority of our DS distance clustering over several competing methods.
Searching Density-Increasing Path to Local Density Peaks for Unsupervised Anomaly Detection

Unsupervised anomaly detection (AD) is a challenging problem in the data mining community. Clustering-based AD methods aim to group normal data points into clusters and then regard a point belonging to none of the clusters as an anomaly. However, they may suffer from the problems of unknown cluster numbers and arbitrary cluster shapes. This paper presents a novel clustering-based AD method named Density-increasing Path (DIP) to tackle these challenges. DIP searches a path for each data point. The path starts at the data point itself, passes through several points with monotonically increasing densities, and ends at a density peak. Further, DIP defines the climbing difficulty of each path by combining the distance and density increment of each step along the path, which can be regarded as the anomaly score of the path starting point. DIP can adaptively decide the number of peaks to address the challenge of unknown cluster numbers. Since DIP requires the path to pass several points rather than directly reaching the peak, it handles arbitrary cluster shapes. We also propose the ensemble DIP to improve prediction accuracy. The experimental results on four synthetic datasets and eleven real-world benchmarks demonstrate that DIP outperforms existing methods.
Combination of Different-Granularity Beliefs for Sensor-Based Human Activity Recognition

Human activity recognition (HAR) based on sensor information has become a hot topic of research due to its wide range of applications in health care, fitness, and smart homes. However, the classification of activities with similar sensor signals such as standing and sitting is usually more challenging for the design of efficient activity recognition algorithms. Considering the characteristic of human activities with different granularity, which can provide complementary knowledge for individual granularity activity recognition, we propose a novel approach that combines different-granularity beliefs based on belief functions (BFs) theory for HAR. Specifically, at first, two support vector machines (SVMs) are trained to acquire different-granularity basic belief assignments (BBAs) of human activity, respectively. Then, a prior knowledge library (PKL) is established and the least square method is employed to solve the transformation matrix for mapping coarse-grained BBAs to fine-grained BBAs. Finally, the classic DSm (DSmC) rule is adopted to combine the two sets of fine-grained BBAs and further make decisions. Several experiments are conducted to illustrate the performance of the proposed method using two widely used University of California, Irvine (UCI) datasets: smartphone and mHealth. The experimental results show that the proposed method efficiently improves the classification accuracy with respect to other related methods.
Badminton Player’s Shot Prediction Using Deep Learning

The study of object tracking has substantially advanced thanks to the development of deep learning visual recognition and tracking methods. However, because to the additional difficulties they provide, such as the difficulty in tracking small, swiftly moving objects like a ball or shuttlecock due to the fast camera movement and the existence of swings and spins, sports videos are still understudied. To access these massive archives of sports video data and automatically tag and analyse its properties, such as player performance and stroke and shot analysis, an effective end-to-end solution is needed. The aim of this research is to create a complete deep learning based model that can do object detection and tracking in sports movies as well as classify the played stroke. We employed the SF-YOLOv5 model, a lightweight model for the identification of swiftly moving small objects, for this. Then, we utilised the Deep-Sort algorithm and zero shot learning to follow the objects that had been detected. Finally, we classified the played shot using the CNN classifier.
Unsupervised Adaptive Bipartite Graph Embedding

In traditional graph embedding methods, graph construction is sensitive to high-dimensional data with noise and outliers, making an effective exploration of the neighborhood structure of the data difficult. Besides, with these methods, constructing graphs and reducing dimensions are disconnected and cannot be mutually optimized. To address these problems, we propose an unsupervised dimensionality reduction method based on bipartite graph, named unsupervised adaptive bipartite graph embedding (UABGE). First, the anchors are generated from the raw data by K-means or random sampling. Second, the bipartite graph, which is constructed between the samples and the anchors in the low-dimensional subspace, utilizes the adaptive allocation method to assign neighbors for each sample, so that the local structure of high-dimensional data can be captured effectively. Third, we present an objective function that combines bipartite graph construction and projection matrix learning to achieve mutual optimization between them, which can be solved with an alternating optimization algorithm. Finally, the computational complexity and the convergence of the algorithm are analyzed. Experimental results on synthetic data and publicly available datasets illustrate the effectiveness of the proposed method.