Reviewerly/exHarmony huggingface dataset:
Intelligent Technique for Moving Object Detection from Problematic Video Captured through Camera Sensor

Authors:
raw_author_name: Dileep Kumar Yadav
raw_affiliation_string: Department of Computer Science &amp; Engineering, School of Computing Science &amp; Engineering, Galgotias University,
Greater Noida, India
raw_author_name: Sneha Mishra
raw_affiliation_string: Department of Computer Science &amp; Engineering, School of Computing Science &amp; Engineering, Galgotias University,
Greater Noida, India

Abstract: Aim: The significant aim of the proposed work is to develop an adaptive method to compute the threshold during run-time and update it adaptively for each pixel in the testing phase. It classifies motion-oriented pixels from the scene for moving objects using background subtraction and enhances using post-processing. Background: According to the huge demand for surveillance system, society is looking towards an intelligent video surveillance system that detect and track moving objects from video captured through a surveillance camera. So, it is very crucial and highly recommended throughout the globe in numerous domains such as video-based surveillance, healthcare, transportation, and many more. Practically, this research area faces lots of challenging issues such as illumination variation, cluttered background, camouflage, etc. So, this paper has developed an adaptive background subtraction method to handle such challenging problems. Objective: To focus and study the problematic video data captured through the camera sensor. To handle challenging issues available in real-time video scenes. To develop a background subtraction method and update the background model adaptively for moving object detection. Method: The proposed method has been accomplished using the following sections: Background model construction Automatic generation of threshold Background subtraction Maintenance of background model Results: The qualitative analysis of the proposed work is experimented with publicly available datasets and compared with considered state-of-the-art methods. In this work, library sequence (thermal data) of CDNET and other color video frame sequences Foreground aperture, Waving Tree and Camouflage are considered from Microsoft’s Wallflower. The quantitative values depicted in Table- 1. This work demonstrate the better performance of the proposed method as compared to state-ofthe- art methods. It also generates better outcomes and handles the problem of a dynamic environment and illumination variation. Conclusion: Currently, the world is demanding computer vision-based security and surveillancebased applications for society. This work has provided a method for the detection of moving information using an adaptive method of background subtraction approach for moving object detection in video scenes. The performance evaluation depicts better average results as compared to considered peer methods.
Identifying serpentine minerals by their chemical compositions with machine learning

Authors:
raw_author_name: Fang Huang
raw_affiliation_string: CSIRO Mineral Resources, Kensington, Western Australia 6151, Australia
raw_author_name: Xu Chu
raw_affiliation_string: Department of Earth Sciences, University of Toronto, Toronto, Ontario M5S 3B1, Canada
raw_author_name: J ZhangZhou
raw_affiliation_string: School of Earth Sciences, Zhejiang University, Hangzhou 310058, China

Abstract: Abstract The three main serpentine minerals, chrysotile, lizardite, and antigorite, form in various geological settings and have different chemical compositions and rheological properties. The accurate identification of serpentine minerals is thus of fundamental importance to understanding global geochemical cycles and the tectonic evolution of serpentine-bearing rocks. However, it is challenging to distinguish specific serpentine species solely based on geochemical data obtained by traditional analytical techniques. Here, we apply machine learning approaches to classify serpentine minerals based on their chemical compositions alone. Using the Extreme Gradient Boosting (XGBoost) algorithm, we trained a classifier model (overall accuracy of 87.2%) that is capable of distinguishing between low-temperature (chrysotile and lizardite) and high-temperature (antigorite) serpentines mainly based on their SiO2, NiO, and Al2O3 contents. We also utilized a k-means model to demonstrate that the tectonic environment in which serpentine minerals form correlates with their chemical compositions. Our results obtained by combining these classification and clustering models imply the increase of Al2O3 and SiO2 contents and the decrease of NiO content during the transformation from low-to high-temperature serpentine (i.e., lizardite and chrysotile to antigorite) under greenschist–blueschist conditions. These correlations can be used to constrain mass transfer and the surrounding environments during the subduction of hydrated oceanic crust.
Multikeyword-Ranked Search Scheme Supporting Extreme Environments for Internet of Vehicles

Authors:
raw_author_name: Dequan Xu
raw_affiliation_string: State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
raw_author_name: Changgen Peng
raw_affiliation_string: State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
raw_author_name: Weizheng Wang
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China
raw_author_name: Kapal Dev
raw_affiliation_string: Department of Computer Science, Munster Technological University, Munster, Ireland
raw_author_name: Sunder Ali Khowaja
raw_affiliation_string: Department of Telecommunication Engineering, Faculty of Engineering and Technology, University of Sindh, Jamshoro, Pakistan
raw_author_name: Youliang Tian
raw_affiliation_string: State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China

Abstract: In recent years, the cloud infrastructure has been developed as a promising sharing system for the Internet of Vehicles (IoV) communication. During the information exchange, search service over ciphertext called searchable encryption (SE) is an extraordinary method to prevent data breaches. However, two open problems still need to be solved for ranked search, which hinders the application practicality in IoV. First, each data owner must store the extra information to distribute weight values dynamically. Second, ranking in the cloud has not been supported by most existing schemes. In this paper, to address the above problems and fit the characteristics of real-time data exchange in IoV, we present a multi-keyword ranked search scheme supporting extreme environments for the IoV. Specifically, our system designs a unique encrypted index tree structure to realize the multi-keyword ranked retrieval, the weight value dynamic adaptive calculation, and dynamic updating in IoV. Moreover, we use a master-slave dual server model to cope with extreme environments and propose a “Greedy Breadth-First Search” algorithm to achieve an effective sub-linear search. Finally, comprehensive security analysis and experimental simulation for the proposed system prove that our system can guarantee user privacy and acceptable efficiency.
A Multi-Objective Evolutionary Approach to Discover Explainability Tradeoffs when Using Linear Regression to Effectively Model the Dynamic Thermal Behaviour of Electrical Machines

Authors:
raw_author_name: Alexandru-Ciprian Zăvoianu
raw_affiliation_string: National Subsea Centre, Robert Gordon University, Aberdeen, UK
raw_author_name: Andrei Petrovski
raw_affiliation_string: National Subsea Centre, Robert Gordon University, Aberdeen, UK
raw_author_name: Gerd Bramerdorfer
raw_affiliation_string: Institute for Electrical Drives and Power Electronics, Johannes Kepler University, Linz, Austria

Abstract: Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving accurate data-driven estimators of key motor temperatures, we propose a multi-objective strategy for creating Linear Regression (LR) models that integrate optimised synthetic features. The main strength of our approach is that it provides decision makers with a clear overview of the optimal tradeoffs between data collection costs, the expected modelling errors and the overall explainability of the generated thermal models. Moreover, as parsimonious models are required for both microcontroller deployment and domain expert interpretation, our modelling strategy contains a simple but effective step-wise regularisation technique that can be applied to outline domain-relevant mappings between LR variables and thermal profiling capabilities. Results indicate that our approach can generate accurate LR-based dynamic thermal models when training on data associated with a limited set of load points within the safe operating area of the electrical machine under study.
Generative Augmentation-Driven Prediction of Diverse Visual Scanpaths in Images

Authors:
raw_author_name: Ashish Verma
raw_affiliation_string: Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, India
raw_author_name: Debashis Sen
raw_affiliation_string: Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, India

Abstract: Visual scanpaths of multiple humans on an image represent the process by which they capture the information in it. State-of-the-art models to predict visual scanpaths on images learn directly from recorded human visual scanpaths. However, the generation of multiple visual scanpaths on an image having diversity like human visual scanpaths has not been explicitly considered. In this paper, we propose a deep network for predicting multiple diverse visual scanpaths on an image. Image-specific hidden Markov model based generative data augmentation is performed in the beginning to increase the number of image-visual scanpath training pairs. Considering a similarity between our generative data augmentation process and the use of long short-term memory (LSTM) for prediction, we propose an LSTM based visual scanpath predictor. A network to predict a single visual scanpath on an image is designed first. The network is then modified to predict multiple diverse visual scanpaths representing different viewer varieties by using a parameter indicating the uniqueness of a viewer. A random vector is also employed for subtle variations within scanpaths of the same viewer variety. Our models are evaluated on three standard datasets using multiple performance measures, which demonstrate the superiority of the proposed approach over the state-of-the-art. Empirical studies are also given indicating the significance of our generative data augmentation method and our multiple scanpath prediction strategy producing diverse visual scanpaths. Code link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ashishverma03.github.io/Diverse-Visual-Scanpath</uri>
A Type-2 Fuzzy-Based Explainable AI System for Predictive Maintenance Within the Water Pumping Industry

Authors:
raw_author_name: Shreyas J. Upasane
raw_affiliation_string: Computational Intelligence Centre, School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.
raw_author_name: Hani Hagras
raw_affiliation_string: Computational Intelligence Centre, School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.
raw_author_name: Mohammad Hossein Anisi
raw_affiliation_string: Computational Intelligence Centre, School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.
raw_author_name: Stuart Savill
raw_affiliation_string: Aquatronic Group Management Plc, Copford, U.K.
raw_author_name: Ian Taylor
raw_affiliation_string: Aquatronic Group Management Plc, Copford, U.K.
raw_author_name: Kostas Manousakis
raw_affiliation_string: Aquatronic Group Management Plc, Copford, U.K.

Abstract: Industrial maintenance has undergone a paradigm shift due to the emergence of artificial intelligence (AI), the Internet of Things (IoT), and cloud computing. Rather than accepting the drawbacks of reactive maintenance, leading firms worldwide are embracing "predict-and-prevent" maintenance. However, opaque box AI models are sophisticated and complex for the average user to comprehend and explain. This limits the AI employment in predictive maintenance, where it is vital to understand and evaluate the model before deployment. In addition, it's also important to comprehend the maintenance system's decisions. This paper presents a type-2 fuzzy-based Explainable AI (XAI) system for predictive maintenance within the water pumping industry. The proposed system is optimised via Big-Bang Big-Crunch (BB-BC), which maximises the model accuracy for predicting faults while maximising model interpretability. We evaluated the proposed system on water pumps using real-time data obtained by our hardware placed at real-world locations around the United Kingdom and compared our model with Type-1 Fuzzy Logic System (T1FLS), a Multi-Layer Perceptron (MLP) Neural Network, an effective deep learning method known as stacked autoencoders (SAEs) and an interpretable model like decision trees (DT). The proposed system predicted water pumping equipment failures with good accuracy (outperforming the T1FLS accuracy by 8.9% and DT by 529.2% while providing comparable results to SAEs and MLPs) and interpretability. The system predictions comprehend why a specific problem may occur, which leads to better and more informed customer visits to reduce equipment failure disturbances. It will be shown that 80.3% of water industry specialists strongly agree with the model's explanation, determining its acceptance.
Virtual, augmented, and mixed reality: Potential clinical and training applications in pediatrics

Authors:
raw_author_name: Meong Hi Son
raw_affiliation_string: . Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea; . Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

Abstract: Background: COVID-19 pandemic has significantly impacted the field of medical training, necessitating innovative approaches to education and practice. During this period, the use of novel technologies like virtual reality (VR), augmented reality (AR), and mixed reality (MR) has become increasingly vital. These technologies oﬀer the advantage of transcending the limitations of time and space, thus enabling medical professionals to access various personalized programs for both education and service delivery. This shift is particularly relevant in the realm of pediatric medicine, where traditional training and clinical methods face unique challenges.Purpose: The primary aim of this study is to explore the application of VR, AR, and MR technologies in pediatric medical settings, with a focus on both clinical applications and the training of pediatric medical professionals. We aim to comprehensively search and review studies that have utilized these technologies in the treatment of pediatric patients and the education of healthcare providers in this field.Methods: Peer-reviewed articles published in PubMed, the Cochrane Library, ScienceDirect, Google Scholar, and Scopus from January 1, 2018, to March 1, 2023, were comprehensively searched. The review was conducted according to the PRISMA (Preferred Reporting Items for Systematic review and Meta-Analyses) guidelines. Among the 89 studies, 63 investigated the clinical applications of VR (n=60) or AR (n=3) in pediatric patients, and 25 investigated the applications of VR (n=19), AR (n=5), or MR (n=1) for training medical professionals.Results: A total of 36 randomized controlled trials (RCTs) for clinical application (n=31) and medical training (n=5) were retrieved. Among the RCTs, 21 reported significant improvements in clinical applications (n=17) and medical training (n=4).Conclusion: Despite a few limitations in conducting research on innovative technology, such research has rapidly expanded, indicating that an increasing number of researchers are involved in pediatric research using these technologies.
STAGED: A Spatial-Temporal Aware Graph Encoder–Decoder for Fault Diagnosis in Industrial Processes

Authors:
raw_author_name: Shizhong Li
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, and the Key Laboratory of CS&amp;AUS of Zhejiang Province, Zhejiang University, Hangzhou, China
raw_author_name: Wenchao Meng
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, and the Key Laboratory of CS&amp;AUS of Zhejiang Province, Zhejiang University, Hangzhou, China
raw_author_name: Shibo He
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, and the Key Laboratory of CS&amp;AUS of Zhejiang Province, Zhejiang University, Hangzhou, China
raw_author_name: Jichao Bi
raw_affiliation_string: Zhejiang Institute of Industry and Information Technology, Hangzhou, China
raw_author_name: Guanglun Liu
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, and the Key Laboratory of CS&amp;AUS of Zhejiang Province, Zhejiang University, Hangzhou, China

Abstract: Data-driven fault diagnosis for critical industrial processes has exhibited promising potential with massive operating data from the supervisory control and data acquisition system. However, automatically extracting the complicated interactions between measurements and subtly integrating it with temporal evolutions have not been fully considered. Besides, with the increasing complexity of industrial processes, accurately locating fault roots is of tremendous significance. In this article, we propose an unsupervised spatial-temporal aware graph encoder-decoder (STAGED) model for industrial fault diagnosis. Firstly, the high-dimensional measurements are constructed as a weighted graph to depict the complicated interactions. Then, the graph convolutional network, long short-term memory network and attention mechanism are applied to learn a comprehensive representation for multi-series. To enforce the model to better capture the temporal evolution, the dual decoder that performs reconstruction and prediction tasks simultaneously is adopted with a well-designed comprehensive loss function. By learning the spatial-temporal evolutions of datasets, faults can be diagnosed and located at a fine-grained level based on reconstruction deviations. To verify the performance of STAGED, experiments on Cranfield three-phase flow facility and secure water treatment datasets are implemented and the results indicate that it can provide insight into fault evolution and accurately diagnose faults.
ICOE: A Lightweight Group-Consensus-Based Off-Chain Execution Model for Smart Contract-Based Industrial Applications

Authors:
raw_author_name: Dibo Xian
raw_affiliation_string: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
raw_author_name: Xuetao Wei
raw_affiliation_string: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Abstract: Programmable smart contracts facilitate the application of blockchain to many areas of industry, e.g., supply chains, energy, and healthcare. The off-chain execution model for blockchain smart contracts is a promising approach to achieve scalability for industrial applications, which executes each smart contract in one of the small-scale consensus groups. However, previous work based on such a model cannot efficiently handle cross-group contract transactions that involve multiple cross-group invocations between smart contracts, because of the heavyweight state synchronization and repeated executions. In this paper, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE</small> , a lightweight group-consensus based off-chain execution model for cross-group contract transactions without the need of state synchronization and duplicate executions, and also prove its correctness. The key novelty of our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE</small> is to let the invoked smart contract only executed in its residing group and only return the execution result to the invoking smart contract, while leveraging the two-phase commit(2PC) protocol guarantees consistent commits across multiple groups within the same invoking smart contract transaction. We implement <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE</small> and extensive experimental results show that our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE</small> achieves a 14x improvement of throughput on average compared to the state-of-the-art model.
Robotic Inserting a Moving Object Using Visual-Based Control With Time-Delay Compensator

Authors:
raw_author_name: Lili Wang
raw_affiliation_string: Institute of Automation, Chinese Academy of Science, Beijing, China
raw_author_name: Chuankai Liu
raw_affiliation_string: Beijing Aerospace Control Center, Beijing, China
raw_author_name: Hong Qiao
raw_affiliation_string: Institute of Automation, Chinese Academy of Science, Beijing, China

Abstract: Tracking-and-inserting a moving peg using a robot manipulator is a challenging task in manufacturing. In the past decades, various visual-based methods have been proposed for robotic manipulating static targets, which usually ignore the time delay in robot command transmission and image processing. However, for tracking and inserting a moving peg, time delays cannot be overlooked because they can reduce the tracking performance and even cause manipulations to fail. In this paper, a robot visual-based control with a time-delay compensator is presented to solve the problem of inserting a moving peg. The time-delay compensator was designed using RBFNNs, a feedback compensator aimed at eliminating the tracking errors caused by the time delays. Thus, we could manipulate a moving object using a commercial industry robot, even with the time-variant delays in the control loop. Furthermore, the visual-based controller with the pseudo-inverse image Jacobian matrix was designed using a linearization model. Thus, the matrix could be efficiently updated using the model. In the experiment, we inserted a peg into a moving hole using an eye-in-hand robot with precision.