Reviewerly/exHarmony huggingface dataset:
On the possibility of using quantum annealers to solve problems of computational materials science

Abstract A promising area of application of quantum computing is computational materials science. In addition to the actively discussed use of quantum computers as simulators for modeling quantum systems, the possibility of using quantum computing to solve the problems of determining the parameters of model multiparameter potentials of intermolecular interaction is of great interest. Especially attractive for these purposes is the method of quantum annealing, as currently the most developed quantum computing technology to solve complex optimization problems. As a first step, the paper presents the algorithms developed for determining the parameters of two classical potentials—Lennard-Jones and Buckingham, designed for implementation on a quantum annealer. We demonstrate mathematical methods for the development of such algorithms. One of them seems to be worthy for the further development and promising for solving more complex problems. Also, we evaluate the scalability of the presented algorithms and justify the possibility of their practical implementation on the current version of the D-Wave quantum annealer.
Optimization Algorithm for Steel Surface Defect Detection Based on PP-YOLOE

The fast and accurate detection of steel surface defects has become an important goal of research in various fields. As one of the most important and effective methods of detecting steel surface defects, the successive generations of YOLO algorithms have been widely used in these areas; however, for the detection of tiny targets, it still encounters difficulties. To solve this problem, the first modified PP-YOLOE algorithm for small targets is proposed. By introducing Coordinate Attention into the Backbone structure, we encode channel relationships and long-range dependencies using accurate positional information. This improves the performance and overall accuracy of small target detection while maintaining the model parameters. Additionally, simplifying the traditional PAN+FPN components into an optimized FPN feature pyramid structure allows the model to skip computationally expensive but less relevant processes for the steel surface defect dataset, effectively reducing the computational complexity of the model. The experimental results show that the overall average accuracy (mAP) of the improved PP-YOLOE algorithm is increased by 4.1%, the detection speed is increased by 2.06 FPS, and the accuracy of smaller targets (with a pixel area less than 322) that are more difficult to detect is significantly improved by 13.3% on average, as compared to the original algorithm. The detection performance is also higher than that of the mainstream target detection algorithms, such as SSD, YOLOv3, YOLOv4, and YOLOv5, and has a high application value in industrial detection.
GSQAS: Graph Self-supervised Quantum Architecture Search

Quantum Architecture Search (QAS) is a promising approach for designing quantum circuits specifically tailored for variational quantum algorithms (VQAs). However, existing QAS algorithms require calculating the ground-truth performances of a substantial number of quantum circuits during the search process, rendering them computationally demanding and limiting their applicability to large-scale quantum circuits. Recently, a predictor-based QAS has been proposed to address this challenge by estimating circuit performance directly based on their structures using a predictor trained on a set of labeled quantum circuits. However, the predictor is trained by purely supervised learning, which suffers from poor generalization ability when labeled training circuits are scarce. It is highly time-consuming to obtain a substantial number of labeled quantum circuits because the gate parameters of quantum circuits need to be optimized until convergence to obtain their ground-truth performances. To overcome these limitations, we propose GSQAS, a graph self-supervised QAS that trains a predictor by self-supervised learning. Specifically, we first pre-train a graph encoder using a well-designed pretext task on a large number of unlabeled quantum circuits, aiming to generate meaningful representations of quantum circuits. Subsequently, the downstream predictor is trained on a small set of quantum circuits’ representations and their corresponding labels. Once the encoder is trained, it becomes applicable to various downstream tasks. To effectively encode spatial topology information and avoid huge dimensions of feature vectors for large-scale quantum circuits, we propose a graph-based encoding scheme for quantum circuits. Simulation results on QAS for variational quantum eigensolver and quantum state classification demonstrate that GSQAS outperforms the state-of-the-art predictor-based QAS, yielding superior performance with fewer labeled circuits.
A Framework for Synthetic Agetech Attack Data Generation

To address the lack of datasets for agetech, this paper presents an approach for generating synthetic datasets that include traces of benign and attack datasets for agetech. The generated datasets could be used to develop and evaluate intrusion detection systems for smart homes for seniors aging in place. After reviewing several resources, it was established that there are no agetech attack data for sensor readings. Therefore, in this research, several methods for generating attack data were explored using attack data patterns from an existing IoT dataset called TON_IoT weather data. The TON_IoT dataset could be used in different scenarios, but in this study, the focus is to apply it to agetech. The attack patterns were replicated in a normal agetech dataset from a temperature sensor collected from the Information Security and Object Technology (ISOT) research lab. The generated data are different from normal data, as abnormal segments are shown that could be considered as attacks. The generated agetech attack datasets were also trained using machine learning models, and, based on different metrics, achieved good classification performance in predicting whether a sample is benign or malicious.
Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

This study proposes the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to correct the latitude and longitude of Global Positioning System (GPS) used in locating towed vehicle system for underground imaging. The input used was the collected data from a developed Real-time Kinematic Global Positioning System sensor integrated with Inertial Measurement Unit. Different ANFIS models were developed and evaluated. For latitude correction, ANFIS model with hybrid optimization trained at 300 epochs was chosen, whereas for longitude correction, ANFIS model with hybrid optimization trained at 100 epochs was selected. Both models achieved the lowest Mean Squared Error (MSE), the highest Coefficient of Determination (R2), and lowest Mean Absolute Error (MAE). Moreover, selected best ANFIS models were compared to Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) models, but the results showed that the ANFIS models have superior performances. The selected ANFIS models were verified by testing on the collected actual dataset and the visualized map demonstrated that the corrected GPS latitude and longitude have significantly reduced error, indicating that the fuzzy system with neural network capabilities is a cost-effective and convenient method for error reduction in vehicle localization making it applicable to be integrated for capacitive resistivity underground imaging systems.
Enhancing manufacturing education based on controller-free augmented reality learning

Based on several exemplary use-cases, the technological limits as well as the educational impact of controller-free Augmented Reality learning, using the Microsoft HoloLens 2, were investigated. The field of application was manufacturing engineering, and here in particular, the aluminum extrusion process. Compared to traditional learning environments, such as lectures and exercises, the developed test environments included the additional use of learning setups at the actual forming machine in the laboratory, enhanced by Augmented Reality. A total of 12 students from various academic years participated in the impact analysis study. The study comprised a general survey regarding various aspects of the controller-free learning, including visual and acoustic appearance of the developed scenarios as well as details regarding the control of the augmented objects. It was found that, even though none of the students had used such a device before, interacting with the holograms by hand, and not through controllers, was intuitive for the majority of students. In contrast, the interaction mode for long distances, the hand ray control mode, was assessed as unintuitive and frustrating at times. The visual and acoustic quality of the developed applications was rated high and sufficient for the use in education and production environments. Specific features developed for the extrusion press application, such as the display of Finite-Element-Simulation results, kinematic process animations, live sensor data through webservers and virtual safety walls were regarded as fundamental for the beneficial use of Augmented Reality in education and production. Based on the findings, future Augmented Reality applications in the field of manufacturing education can improve in usability and acceptance by engineering students without prior testing.
The use of virtual reality in manufacturing education: State-of-the-art and future directions

Innovative technologies such as virtual reality and additive manufacturing have been drastically changing our society, from how we design and manufacture products to how to educate and train the next-generation workforce. This paper reviews scientific studies on virtual reality assisted manufacturing education published from 2015 to 2022 from three different perspectives: targeted manufacturing disciplines/topics, virtual environment development, and outcome evaluation methods. This paper also summarizes the critical limitations of existing studies and identifies the key challenges in the field. Furthermore, some future research directions are discussed aiming to advance current manufacturing education and deliver a highly skilled workforce for U.S. future manufacturing.
Real-time human-computer interaction using eye gazes

Eye gaze emerges as a unique channel in human–computer interaction (HCI) that recognizes human intention based on gaze behavior and enables contactless access to control and operate software interfaces on computers. In this paper, we propose a real-time HCI system using eye gaze. First, we capture and track eyes using the Dlib 68-point landmark detector, and design an eye gaze recognition model to recognize four types of eye gazes. Then, we construct an instance segmentation model to recognize and segment tools and parts using the Mask Region-Based Convolutional Neural Network (R-CNN) method. After that, we design an HCI software interface by integrating and visualizing the proposed eye gaze recognition and instance segmentation models. The HCI system captures, tracks, and recognizes the eye gaze through a red–green–blue (RGB) webcam, and provides responses based on the detected eye gaze, including the tool and part segmentation, object selection and interface switching. Experimental results show that the proposed eye gaze recognition method achieves an accuracy of >99 % in a recommended distance between the eyes and the webcam, and the instance segmentation model achieves an accuracy of 99 %. The experimental results of the HCI system operation demonstrate the feasibility and robustness of the proposed real-time HCI system.
Characterizing heterogenous microstructures of fiber-reinforced composite materials using an advanced image processing-based approach through optical microscopic images

The microstructures of heterogeneous fiber-reinforced composite materials exhibit high variations, which may affect the localized material mechanical properties, create the material machinability issue, and further cause severe surface integrity issues during machining processes. Most existing approaches to studying the heterogenous microstructure of composite usually require mechanical testing and extensive post-material characterizations. Even worse, experimental testing can damage material permanently, and offline characterizations (e.g., SEMs) are expensive and time-consuming. Therefore, creating a noncontact, fast, accurate, and affordable material characterization tool can help analyze heterogenous microstructures of composite materials and boost their industrial applications. This study presents an image process-based analytic approach to characterizing microstructural features. This approach performs well in extracting microstructures from blurred and translucent optical microscopic images. The further generated features, including fiber volume fraction and other physical descriptors, capture the microstructural features and quantify the material's heterogeneity. An exemplary case study using a two-dimension spatial pattern and an experimental case study show that the presented approach allows rapid and accurate characterizations of fiber-reinforced composites with anomaly detections on the local microstructural heterogeneity.
Mobile-Based augmented reality (AR) module for guided operations of CNC surface roughness machine

Immersive technologies are gaining considerable traction in manufacturing industries to address issues linked with the shortage of skilled workforce. Augmented Reality (AR) is one of the technologies with great potential to address this problem due to simplified hardware requirements and adaptability to various domains. The present work utilizes AR technology in the context of machine operation guidance to solve the problem of skilled human resources for operating delicate manufacturing machines. A mobile-based AR application has been developed using the Vuforia engine and Unity 3D platform for a CNC surface roughness machine. The AR module is designed to assist and guide novice operators in efficiently executing various machine setting, operation, and software utilization tasks. The subjective and objective evaluation of the developed system has been carried out to capture user feedback and review. It has been demonstrated that the developed mobile-based AR module offers extensive utility, ease of operation, and practical utility. © 2023 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.
FairShare: An Incentive-Based Fairness-Aware Data Sharing Framework for Federated Learning

Federated learning protects sensitive data during AI model training, enabling collaboration without sharing raw data. Ensuring fairness and addressing un-shared decisions are crucial for reliable federated learning. This study introduces "FairShare", an incentive mechanism enhancing reliability in applications like financial fraud detection and chatbot customer service. FairShare has two stages: stage one uses the Vickrey-Clarke-Groves (VCG) auction to estimate clients' true costs, ensuring truthfulness, individual rationality, and computational efficiency. Stage two employs the Shapley Value method to allocate fair payments, promoting high-quality data usage. FairShare encourages clients to balance local datasets, reducing bias and increasing reliability. Theoretical proofs and experiments demonstrate FairShare's effectiveness in ensuring fairness and protecting data privacy, leading to reliable outcomes. Experiments confirm the VCG-based mechanism's truthfulness and Shapley Value's fairness in payment allocation. In conclusion, FairShare addresses fairness challenges and fosters reliability in federated learning, facilitating dependable human-machine interactions. Implementing FairShare can enhance sensitive data protection, promote fairness, and improve reliability in human-machine interactions, supporting federated learning adoption across sectors.
Self-supervised representation learning anomaly detection methodology based on boosting algorithms enhanced by data augmentation using StyleGAN for manufacturing imbalanced data

This study proposes a methodology for detecting anomalies in the manufacturing industry using a self-supervised representation learning approach based on deep generative models. The challenge arises from the limited availability of data on defective products compared with normal data, leading to degradation in the performance of deep learning models owing to data imbalances. To address this limitation, we propose a process that leverages the Gramian angular field to transform time-series data into images, applies StyleGAN for image augmentation of anomalous data, and utilizes a boosting algorithm for classifier selection in supervised learning. Additionally, we compared the accuracy of the classifier before and after data augmentation. In experimental cases involving CNC milling machine data and wire arc additive manufacturing data, the proposed approach outperformed the approach before augmentation, resulting in improved precision, recall, and F1-score for anomaly detection. Furthermore, Bayesian optimization of the hyperparameters of the boosting algorithm further enhanced the performance metrics. The proposed process effectively addresses the data imbalance problem, and demonstrates its applicability to various manufacturing industries.
Retail robots as sales assistants: how speciesism moderates the effect of robot intelligence on customer perceptions and behaviour

Purpose Retailers increasingly endeavour to implement artificial intelligence (AI) innovations, such as humanoid social robots (HSRs), to enhance customer experience. This paper investigates the interactive effect of HSR intelligence and consumers' speciesism on their perceptions of retail robots as sales assistants. Design/methodology/approach Three online experiments testing the effects of HSRs' intellectual intelligence on individuals' perceived competence and, consequently, their decision to shop at a retail store that uses HSRs as sales assistants are reported. Furthermore, the authors examine whether speciesism attenuates these effects such that a mediation effect is likely to be observed for individuals low in speciesism but not for those with high levels of speciesism. Data for all studies were collected on Prolific and analysed with SPSS to perform a logistic regression and PROCESS 4.0 (Hayes, 2022) for the mediation and moderated-mediation analysis. Findings The findings show that the level of speciesism moderates the relationship between HSR intellectual intelligence and perceived competence such that an effect is found for low but not for high HSR intelligence. When HSR intellectual intelligence is low, individuals with higher levels of speciesism (vs low) rate the HSR as less competent and display lower HSR acceptance (i.e. customers' decision to shop using retail robots as sales assistants). Originality/value This research responds to calls in research to adopt a human-like perspective to understand the compatibility between humans and robots and determine how personality traits, such as a person's level of speciesism, may affect the acceptance of AI technologies replicating human characteristics (Schmitt, 2019). To the best of the authors' knowledge, the present research is the first to examine the moderating role of speciesism on customer perceptions of non-human retail assistants (i.e. human-like and intelligent service robots). This study is the first to showcase that speciesism, normally considered a negative social behaviour, can positively influence individuals' decisions to engage with HSRs.
SKTR: Trace Recovery from Stochastically Known Logs

Developments in machine learning together with the increasing usage of sensor data challenge the reliance on deterministic logs, requiring new process mining solutions for uncertain, and in particular stochastically known, logs. In this work we formulate trace recovery, the task of generating a deterministic log from stochastically known logs that is as faithful to reality as possible. An effective trace recovery algorithm would be a powerful aid for maintaining credible process mining tools for uncertain settings. We propose an algorithmic framework for this task that recovers the best alignment between a stochastically known log and a process model, with three innovative features. Our algorithm, SKT R, 1) handles both Markovian and non-Markovian processes; 2) offers a quality-based balance between a process model and a log, depending on the available process information, sensor quality, and machine learning predictiveness power; and 3) offers a novel use of a synchronous product multigraph to create the log. An empirical analysis using five publicly available datasets, three of which use predictive models over standard video capturing benchmarks, shows an average relative accuracy improvement of more than 10% over a common baseline.
Histopathologic brain age estimation via multiple instance learning

Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.
A spatio‐temporal Dirichlet process mixture model for coronavirus disease‐19

Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide richer opportunities to understand the mechanism of the disease spread compared to the more often encountered aggregated count data. We propose a spatio-temporal Dirichlet process mixture model to analyze confirmed cases of COVID-19 in an urban environment. Our method can detect unobserved cluster centers of the epidemics, and estimate the space-time range of the clusters that are useful to construct a warning system. Furthermore, our model can measure the impact of different types of landmarks in the city, which provides an intuitive explanation of disease spreading sources from different time points. To efficiently capture the temporal dynamics of the disease patterns, we employ a sequential approach that uses the posterior distribution of the parameters for the previous time step as the prior information for the current time step. This approach enables us to incorporate time dependence into our model in a computationally efficient manner without complicating the model structure. We also develop a model assessment by comparing the data with theoretical densities, and outline the goodness-of-fit of our fitted model.