Reviewerly/exHarmony huggingface dataset:
Soil Heavy-Metal Pollution Prediction Methods Based on Two Improved Neural Network Models

Current soil pollution prediction methods need improvement, especially with regard to accuracy in supplementing missing heavy-metal values in soil, and the accuracy and slow convergence speed of methods for predicting heavy-metal content at unknown points. To reduce costs and improve prediction accuracy, this study used two neural network models (SA-FOA-BP and SE-GCN) to supplement missing heavy-metal values and efficiently predict heavy-metal content in soil. The SA-FOA-BP model combines simulated annealing and fruit fly algorithms to optimize the parameter search method in traditional BP neural networks and improve prediction of missing heavy-metal values in soil. A spatial information fusion graph convolutional network prediction model (SE-GCN) constructs a spatial information encoder that can perceive spatial context information, and embeds it with spatial autocorrelation used for auxiliary learning to predict the heavy-metal content in soil. From the experimental results, the SE-GCN model demonstrates improved performance in terms of evaluation indicators compared with other models. Application analysis of the two improved neural network models was conducted; application scenarios and suitability were analyzed, showing that these models have practical application value for soil pollution prediction.
Deep Recurrent Graph Convolutional Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

The rapid adoption of Internet-of-Things (IoT) and digital twins (DTs) technologies within industrial environments has highlighted diverse critical issues related to safety and security. Sensor failure is one of the major threats compromising DTs operations. In this article, for the first time, we address the problem of sensor fault detection, isolation, and accommodation (SFDIA) in large-size networked systems. Current available machine-learning solutions are either based on shallow networks unable to capture complex features from input graph data or on deep networks with overshooting complexity in the case of large number of sensors. To overcome these challenges, we propose a new framework for sensor validation based on a deep recurrent graph convolutional architecture which jointly learns a graph structure and models spatio-temporal interdependencies. More specifically, the proposed two-block architecture 1) constructs the virtual sensors in the first block to refurbish anomalous (i.e., faulty) behavior of unreliable sensors and to accommodate the isolated faulty sensors and 2) performs the detection and isolation tasks in the second block by means of a classifier. Extensive analysis on two publicly available datasets demonstrates the superiority of the proposed architecture over existing state-of-the-art solutions.
An improvised grey wolf optimiser for global optimisation problems

The grey wolf optimisation (GWO) algorithm is one of the popular meta-heuristic algorithms in evolutionary computation. However, the GWO algorithm has many drawbacks such as less accuracy, incapable of local searching competence, and low convergence speed. Therefore, in this paper an improvised grey wolf optimisation algorithm called IGWO is being introduced to compensate for these drawbacks of the GWO method by altering the surrounding behaviour along with the new position updating formula. Several well-known benchmark functions are considered to examine the accurateness and convergence of the modified version. The outcomes are analogised to the well-known algorithms like particle swarm optimisation algorithm, GWO algorithm, mean GWO algorithm, fast evolutionary programming and gravitational search algorithm. The experimental results showed that the newly modified IGWO can produce extremely superior results in terms of optimum objective functions and convergence speediness.
Factors influencing QSR guests’ acceptance of robotic technology

A few studies that investigated the application of robotics in restaurants explored benefits such as improved financial performance and quality control. The COVID-19 pandemic magnified the significance of this topic because robotics may save labor costs, replace unfilled positions, and improve sanitation through decreased human contact. However, previous research found that human interaction and other elements are important to restaurant customers. Built upon diffusion of innovation theory and expectancy-value theory, this study examined psychological, technological, and social factors that might affect diners' intention to use service robots in quick-service restaurants (QSR). A sample of 615 self-reported survey responses was analyzed using regression models. The results indicate that perceived benefits, ease of use, social influence, and attitude toward service robots are positively associated with usage intention. While usage intention is lower for diners who value human interactions, it is not significantly influenced by diners' novelty-seeking tendencies. The findings enhance the restaurant literature and provide important implications for QSR operators.
Financial Development and Economic Growth: Role of Government Intervention

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Towards Adaptive Multi-Scale Intermediate Domain via Progressive Training for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) involves the transfer of knowledge from a labelled source domain to an unlabelled target domain. Recent studies have introduced the concept of intermediate domains to handle significant domain discrepancies between source and target domains. Constructing an appropriate intermediate domain is a crucial step in handling scenarios with substantial domain differences. In this paper, we propose a novel progressive UDA method called adaptive multi-scale intermediate domain via progressive training (AMPT), which has achieved remarkable effectiveness in alleviating large discrepancies between domains. We design a multi-scale similarity metrics module to solve the issue of different scales across different domains by simultaneously computing the pairwise distance between source domain images and the target domain images on multiple scales. Furthermore, we explore a progressive training strategy that facilitates smooth adaptation of the source domain to the target domain by utilizing the intermediate domain. During the progressive training process, considering a positive feedback mechanism, we iteratively leverage task losses and distillation loss through a dynamic threshold. This ensures that the trained intermediate domain branch progressively constrains the target domain branch, and the intermediate domain can be generated dynamically, leading to a smoother gradual adaptation from the source domain to the target domain. Extensive experimental results demonstrate the superiority of our proposed method AMPT on well-known UDA datasets, including Office-Home, Office-31 and DomainNet.
NLP for Enterprise Asset Management: An Emerging Paradigm

In the field of asset management, a Work Order refers to a document that outlines the necessary steps to carry out a maintenance operation on a specific physical asset. The text on this Work orders providing details about the problem and the actions required are open-ended, not normalized, and Technician’ dependant, presenting challenges for automating asset management Work Order processing. To address the issue of automating the analysis of Work Orders, Natural Language Processing techniques are employed to process the content of these documents. The aim is to identify and extract relevant information related to actions and components within the sentences. This paper presents the Reliability Centred Maintenance for Assets solution, which utilizes a semi-automatic, human-in-the-loop approach to determine a standardised and condensed set of actions and components. The results indicate a significant increase in the number of annotations, reaching a ratio of 1:14. By implementing this solution, the manual workload associated with analysing Work Orders can be reduced, thereby improving decision support and analytical processing of the data contained within these documents.
Retraction Notice: DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation

Text-driven image manipulation remains challenging in training or inference flexibility. Conditional generative models depend heavily on expensive annotated training data. Meanwhile, recent frameworks, which leverage pre-trained vision-language models, are limited by either per text-prompt optimization or inference-time hyper-parameters tuning. In this work, we propose a novel framework named DeltaEdit to address these problems. Our key idea is to investigate and identify a space, namely delta image and text space that has well-aligned distribution between CLIP visual feature differences of two images and CLIP textual embedding differences of source and target texts. Based on the CLIP delta space, the DeltaEdit network is designed to map the CLIP visual features differences to the editing directions of StyleGAN at training phase. Then, in inference phase, DeltaEdit predicts the StyleGAN's editing directions from the differences of the CLIP textual features. In this way, DeltaEdit is trained in a text-free manner. Once trained, it can well generalize to various text prompts for zero-shot inference without bells and whistles. Code is available at https://github.com/Yueming6568/DeltaEdit.
Customers' perception of cybersecurity risks in E-commerce websites

This study investigates impact on customers&apos; perceptions due to a cyber-attack by mapping STRIDE threat modelling to confidentiality, integrity, and availability (CIA) cybersecurity requirements of an e-commerce platform. Further, it studies the influence of loyalty programs and its customer segmentation. The study is based on response from 120 e-commerce customers. Results indicate, confidentiality compromise has the highest negative impact on customers&apos; perception. About 82% respondents expressed perceived privacy risk and concerns as most impacted due to a cyber-attack. Despite these concerns, non-loyal customers are more concerned about purchasing securely by at least 10%. Whereas, loyal customers are affected more emotionally by at most 13%. These findings contribute to the body of literature on customer perception of security, privacy, and trust due to a cyber-attack on digital platforms (such as e-commerce websites) impacting the organisational reputation within the wider digital economy. The result of this work is valuable for information security and socio-technical experts, as well as regulatory authorities, in the progressing digital economy.
Efforts To Sustain Fresh Water Production in Order to Improve The Performance of Fresh Water Generator On The Mt. Rubra

Fresh water generator is an auxiliary aircraft aboard a ship that produces fresh water by evaporating seawater in the evaporator and the seawater is cooled by condensation in a distillation vessel or condenser (condenser), thus producing condensed water called condensate. Fresh water generator running optimally is one of the important factors to support ship operations, especially the results of the fresh water generator used for cooling systems and daily needs on board. This study aims to find out the causes of the problems and efforts that occur in the fresh water generator so that it can maintain fresh water production results in the fresh water generator. While data analysis is to identify the data set that has been obtained, so that the data can be analyzed and the purpose is analyzed in order to get a clearer picture in the preparation of this research both from the problems and the final results. It is hoped that this research research can produce ideas, solutions and appropriate problem solving, both in observing and dealing with the problems raised in this study. Based on research on fresh water production on fresh water generators in MT. Rubra has decreased from 19 tonnes/day to 12 tonnes/day. This is caused by the occurrence of salt deposits on the evaporator plate so that evaporation becomes late and the low pressure of sea water leading to the fresh water generator causes fresh water production to decrease
A Comprehensive Review of Machine Learning-Based Intrusion Detection Techniques for IoT Networks

The vast expansion of the Internet of Things (IoT) exposes it to the threat of massive financial and reputational harm. The imperative to develop an emergency action plan and countermeasures means that current concerns can no longer be put on hold. While many security concerns are present in the layers of IoT, Distributed Denial of Service (DDoS) attacks receive specific treatment, and a part of the survey is on the awareness, network, and application layers. These issues work together to create multifaceted problems. DDoS attacks pose significant problems for the internet, as they can easily bring down vulnerable targets. Different types of DDoS attacks, as well as various IoT DDoS attacks, the effects of these attacks, and responses to them are extensively discussed. This paper outlines different intrusion detection techniques developed within the IoT network. It has already been agreed that the IoT will be used in all areas of our daily lives, including education, vehicles, and healthcare. Furthermore, the diversity of connected devices, and its balance with the quality of service, presents a challenge, and new issues are being developed for IoT devices, such as scale and security needs. These issues must be examined, as security often gets sidelined due to the cost, large-scale and real-time development, and continuous use of IoT. A lack of security poses a significant risk, as consumers do not feel confident about using IoT products with less security.
Innovation and path of teacher literacy in basic education in the era of artificial intelligence

In the era of artificial intelligence, the rapid development of information technology has transformed the way of education and teaching, and put forward higher requirements for the teachers' quality. The teachers' quality performance in education and teaching is their competitiveness based on artificial intelligence, so teachers need to update the quality structure in time. From the perspective of basic education teachers, based on the background of the era of artificial intelligence, this paper discusses three dimensions that need to be improved in teacher quality, including education quality, information digital literacy and information intelligence usage quality. It also proposes paths for improving basic teachers' quality to promote the development of teachers.
A Novel Multi-Qubit Quantum Key Distribution Ciphertext-Policy Attribute-Based Encryption Model to Improve Cloud Security for Consumers

With the growing adoption of cloud computing, ensuring data security in cloud environments has become a critical concern for business organizations. Quantum cryptography utilizes the principles of quantum mechanics to guarantee secure communication, as any attempt to eavesdrop will change the quantum states, alerting the parties of the intrusion. This paper proposes a multi-qubit Quantum Key Distribution (QKD) ciphertext-policy attribute-based encryption (CP-ABE) for cloud security. The proposed multi-qubit QKD model for secure cloud data using quantum cryptography involves the use of a quantum key distribution protocol to generate a secure key for encryption and decryption. This protocol involves sending quantum signals through a quantum channel to distribute a secret key between the sender and the receiver. The key is then used for the encryption and decryption of data using the CP-ABE technique. This technique allows the encryption and decryption of data based on attributes rather than an explicit key exchange, making it particularly suitable for cloud environments where data is stored and processed by multiple users with varying levels of access. The positive results from the proposed simulation model suggest the potential of quantum cryptography in securing cloud data.
Local Bayesian Dirichlet mixing of imperfect models

To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations.
Confounding in Educational Research: An Overview of Research Approaches Investigating Virtual and Augmented Reality

&#x0D; Media comparison studies are often confounded because relevant factors of the learning scenario are not considered.&#x0D; Media comparison studies would be more meaningful if the conditions during investigations were standardized, and additional moderator and mediator variables were incorporated into the experimental design.&#x0D; Other research approaches, e.g., value added studies, may offer insights relevant to educational practice.&#x0D;
QASMTrans: A QASM Quantum Transpiler Framework for NISQ Devices

The success of a quantum algorithm hinges on the ability to orchestrate a successful application induction. Detrimental overheads in mapping general quantum circuits to physically implementable routines can be the deciding factor between a successful and erroneous circuit induction. In QASMTrans, we focus on the problem of rapid circuit transpilation. Transpilation plays a crucial role in converting high-level, machine-agnostic circuits into machine-specific circuits constrained by physical topology and supported gate sets. The efficiency of transpilation continues to be a substantial bottleneck, especially when dealing with larger circuits requiring high degrees of inter-qubit interaction. QASMTrans is a high-performance C++ quantum transpiler framework that demonstrates 3-1111 × speedups compared to the commonly used Qiskit transpiler. We observe speedups on large dense circuits such as ‘uccsd_n24’ which require gates. QASMTrans successfully transpiles the aforementioned circuits in 7.9s, whilst Qiskit needs 502 seconds with optimization 1 and exceeds an hour of transpilation time with optimization 3. With QASMTrans providing transpiled circuits in a fraction of the time of prior transpilers, potential design space exploration, and heuristic-based transpiler design becomes substantially more tractable. QASMTrans is released at http://github.com/pnnl/qasmtrans.
Impact of Climate Change on Photovoltaic Performance: Case Study in French Cities

With the photovoltaic (PV) market significantly growing thanks to the support of environmental incentives, it is essential to address the influence of climate change on PV performances. In the literature, several methodologies have been suggested to measure the effect of climate change through module temperature losses and natural degradation rates, but no work has been found to combine both of them. This article tackles this issue through a numerical approach in order to quantify to which extent climate change impacts the performance of a PV installation over its lifetime. The methodology has been applied in different French cities where climate change is found to have a moderate effect on the performance ratio.
End-to-End Video Gaze Estimation via Capturing Head-Face-Eye Spatial-Temporal Interaction Context

In this letter, we propose a new method, Multi-Clue Gaze (MCGaze), to facilitate video gaze estimation via capturing spatial-temporal interaction context among head, face, and eye in an end-to-end learning way, which has not been well concerned yet. The main advantage of MCGaze is that the tasks of clue localization of head, face, and eye can be solved jointly for gaze estimation in a one-step way, with joint optimization to seek optimal performance. During this, spatial-temporal context exchange happens among the clues on the head, face, and eye. Accordingly, the final gazes obtained by fusing features from various queries can be aware of global clues from heads and faces, and local clues from eyes simultaneously, which essentially leverages performance. Meanwhile, the one-step running way also ensures high running efficiency. Experiments on the challenging Gaze360 dataset verify the superiority of our proposition. The source code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zgchen33/MCGaze</uri> .
Universal Properties of Partial Quantum Maps

We provide a universal construction of the category of finite-dimensional C*-algebras and completely positive trace-nonincreasing maps from the rig category of finite-dimensional Hilbert spaces and unitaries.This construction, which can be applied to any dagger rig category, is described in three steps, each associated with their own universal property, and draws on results from dilation theory in finite dimension.In this way, we explicitly construct the category that captures hybrid quantum/classical computation with possible nontermination from the category of its reversible foundations.We discuss how this construction can be used in the design and semantics of quantum programming languages.
MAU-Net: Mixed attention U-Net for MRI brain tumor segmentation

&lt;abstract&gt;&lt;p&gt;Computer-aided brain tumor segmentation using magnetic resonance imaging (MRI) is of great significance for the clinical diagnosis and treatment of patients. Recently, U-Net has received widespread attention as a milestone in automatic brain tumor segmentation. Following its merits and motivated by the success of the attention mechanism, this work proposed a novel mixed attention U-Net model, i.e., MAU-Net, which integrated the spatial-channel attention and self-attention into a single U-Net architecture for MRI brain tumor segmentation. Specifically, MAU-Net embeds Shuffle Attention using spatial-channel attention after each convolutional block in the encoder stage to enhance local details of brain tumor images. Meanwhile, considering the superior capability of self-attention in modeling long-distance dependencies, an enhanced Transformer module is introduced at the bottleneck to improve the interactive learning ability of global information of brain tumor images. MAU-Net achieves enhancing tumor, whole tumor and tumor core segmentation Dice values of 77.88/77.47, 90.15/90.00 and 81.09/81.63% on the brain tumor segmentation (BraTS) 2019/2020 validation datasets, and it outperforms the baseline by 1.15 and 0.93% on average, respectively. Besides, MAU-Net also demonstrates good competitiveness compared with representative methods.&lt;/p&gt;&lt;/abstract&gt;
Cursor Control using Hand Gestures

Abstract This paper introduces an innovative vision-based cursor control system that utilizes a webcam to capture hand gestures via a color detection technique. With this system, users can navigate the computer cursor by moving their hands adorned with colored caps or tapes. It also enables various cursor functions, such as right and left clicks, double clicks, and scrolling, by recognizing different hand gestures. This system relies solely on a low-resolution webcam as a sensor, tracking the user's hand with colored caps in two dimensions and interpreting up to five distinct hand gestures, which correspond to mouse functions.