Reviewerly/exHarmony huggingface dataset:
Design and Development Considerations for Construction Virtual Reality Training Applications—A Case Study

The emergence of Virtual Reality (VR) in recent years has incepted a new paradigm in education. In the past decade, VR has become a reliable tool in the design, development, and simulation of various scenarios in different industries. The use trend of VR along with other technological advances indicates an ascending growth, which entices more researchers and educators to explore its capabilities, strengths, and challenges and take advantage of opportunities created. VR encompasses a unique set of characteristics and features that are effectively aligned with educational purposes. VR applications provide rich content in a customizable pace and delivery method. VR graphical features intrigue users, especially the new generation of students. VR training applications also minimize the physical risks of real situation-based training tools and provide students with easy access to anytime-anywhere educational sources. Like many other advancements in the construction area, VR has been explored and utilized with a delay. However, the broad scope of construction in industry and education furnishes an appealing path for VR development. Despite this opportunity, the lack of a VR design standard or framework has led construction developers to move forward with trial and error in their design or development processes. This paper provides a background of VR in construction education followed by a framework for VR construction education applications. Also, a retrospective analysis of a design and development of a VR training application for construction safety, derived from a case study, provides lessons learned for further similar applications.
Interactive Assessment Tool for Gaze-based Machine Learning Models in Information Retrieval

Eye movements were shown to be an effective source of implicit relevance feedback in information retrieval tasks. They can be used to, e.g., estimate the relevance of read documents and expand search queries using machine learning. In this paper, we present the Reading Model Assessment tool (ReMA), an interactive tool for assessing gaze-based relevance estimation models. Our tool allows experimenters to easily browse recorded trials, compare the model output to a ground truth, and visualize gaze-based features at the token- and paragraph-level that serve as model input. Our goal is to facilitate the understanding of the relation between eye movements and the human relevance estimation process, to understand the strengths and weaknesses of a model at hand, and, eventually, to enable researchers to build more effective models.
Numerical simulations of DMMs

Abstract This Chapter shows how to transition from hardware realization to software simulation of digital MemComputing machines. It shows that explicit methods of integration are enough to simulate the ordinary differential equations of DMMs efficiently. It provides several numerical examples.
Multi-Joint Motion Estimation of a Soft Gait Assistive Suit Using Pneumatic Reservoir Computing

In recent years, wearable power assistive devices to support human walking have been actively developed. A soft gait assistive suit actuated by pneumatic artificial muscles (PAMs) can detect gait phases from the pressure information of the PAMs. In the previous gait phase detection method of this suit, only a specific phase in the gait cycle has been detected. However, for more advanced assisting, it is desirable to be able to continuously detect the state of multiple parts of the leg during walking. In this study, we applied pneumatic reservoir computing, which is a computational method using the pressure responses of a flexible pneumatic pipeline system as a computational resource, to estimate the leg state of a soft gait assistive suit. As the state estimation of multiple parts of the leg, we attempted to continuously and simultaneously estimate the joint angles and joint angular velocities of the thigh and knee. The results showed that angles and angular velocities of multiple joints could be obtained continuously from a single pneumatic pipeline network with tubes that had adequate diameters and connected to multiple PAMs by using past pressure response data in the calculation.
Enhancing Photography Management Through Automatically Extracted Metadata

The tremendous increase in photographs that are captured each day by common users has been favoured by the availability of high quality devices at accessible costs, such as smartphones and digital cameras. However, the quantity of captured photos raises new challenges regarding the access and management of image repositories. This paper describes a lightweight distributed framework intended to help overcome these problems. It uses image metadata in EXIF format, already widely added to images by digital acquisition devices, and automatic facial recognition to provide management and search functionalities. Moreover, a visualization functionality using a graph-based strategy was integrated, enabling an enhanced and more interactive navigation through search results and the corresponding relations.
The Modelling of the Production Factors' Prediction in the Radio- Electronic Industry

The need to increase the competitiveness of enterprises of the radio-electronic industry of the Russian Federation impose qualitatively new requirements on the instrumental apparatus of the economic analysis. To a certain extent, this applies to the problems of factor analysis. This paper proposes a method of applying the Box-Jenkins model to predict the possible values of increments of production factors. An interpretation of the results is given. The proposed predicitve factor analysis tool can be used in practice to support managerial decision-making on the strategic development of industrial enterprises and increase their economic efficiency.
Advanced Smart Gloves for Physically Disabled Persons Using LABVIEW

People with disabilities like deaf and dumb will find it challenging to communicate with ordinary people; there are various causes for these disabilities, we aim to overcome this issue. The proposed system consists of gloves where FLEX sensors are attached to each finger, the input from Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software. The output is processed, and it is used to distinguish and identify the letters, and it can be concatenated to form a word. The word and letters are identified and displayed in two formats. One is utilizing the LCD, and the next is audio format. This model mainly focuses on trainers and the deaf and dumb people who just started to learn Sign Language (SL), basically beginners. This work is LabVIEW, a User Interface (UI) platform built quickly and gives the most accurate results. American Sign Language (ASL) is used in this model because most SL schools in India use this language for teaching students. Students more understand these languages compared to British SL. The alphabets are displayed in LCD and audio format; later, the letters can concatenate to form a single word. In this model, a maximum of 6 length words is obtained.
Age-related Difference in Conversational Search Behavior: Preliminary Findings

When it comes to emerging technologies, older adults are often those who can greatly benefit from the advancements but are vastly under-represented in research and designs. This study presents preliminary findings of older adults' search behavior with a spoken conversational search agent which represents the next generation search paradigm. Our findings show that, compared with their younger counterparts, older adults' search conversations lasted longer and included more requests. Their requests had greater length and tended to have a lower proportion of unique words, more grammatically complex sentences and short pauses. In addition, the older subjects preferred to start a request with "I" and request questions with modal verbs were less frequent. They reformulated spoken requests as competently as did younger adults but elaborations on requests were uniquely founded among older adults. They also tended to have more than one query or question in a single request and rephrasing requests was more frequently observed than younger adults. System implications and future research directions are discussed.
Weak and Small Face Target Recognition Based on MTCNN Network in Dynamic Platform

The face targets in the dynamic scene video stream avoidably have external factors such as occlusion, fast movement, blurred background, uncontrollable lighting, etc., which are likely to cause target missing, results in the decrease of accuracy and real-time of the face detection algorithm. In this paper, we propose an improved algorithm combining Kalman filter and MTCNN deep learning network, using Kalman filter to predict the center position of the face in the next frame, and provide the MTCNN network with the recommended detection of the next frame based on the position of the predicted center point Area, MTCNN's R-net and O-net then detect the area and extract the target. Through experiments, we prove that the algorithm improves the detection speed while ensuring accuracy. Aiming at the problem that the facial structure of a single face sample is complex, while for groups, it tends to be similar, which makes it difficult to extract features suitable for classification, we propose an improved method based on the MobileFaceNet network. With the introduction of style attention in the MobileFaceNet network Mechanism, it enhances the abilities to extract local area features of the face, and the Arcface loss function is used to train the model. Experiments show that our method effectively improves the accuracy of face recognition and reduces misrecognition.
The Chinese Word Automatic Classification Technology Based on the Improved Ant Colony Algorithm

The main study mainly on automatic classification of Chinese vocabulary. To solve the problem that traditional ant algorithm is low in Chinese word classification, an ant colony algorithm is applied to Chinese word automatic classification. Methods based on the statistics and calculation of large-scale corpus text, the single and binary information of words was obtained, we then classify information using ant group algorithm. Experimental results show that this algorithm effectively improves the accuracy of word classification.
Coarse-to-fine cascade framework for cross-modality super-resolution on clinical/micro CT dataset

This paper proposes a super-resolution (SR) method, for performing SR of medical images training on a newly-built lung clinical CT / micro CT dataset. Conventional SR methods are always trained on bicubic downsampled images (LR) / original images (HR) image pairs. However, registration precision between LR and HR images is not satisfying for SR. Low precision of registration results in conventional SR methods’ unsatisfactory performance in medical imaging. We propose a coarse-to-fine cascade framework for performing SR of medical images. First, we design a coarse SR network to translate LR medical images into coarse SR images. Next, we utilize a fully convolutional network (FCN) to perform fine SR (translate coarse SR images to fine SR images). We conducted experiments using a newly-built clinical / micro CT lung specimen dataset. Experimental results illustrated that our method obtained PSNR of 27.30 and SSIM of 0.75, outperforming conventional method’s PSNR 19.08 and SSIM 0.63.
Surveying definitions of election verifiability

We explore definitions of verifiability by Juels et al. (2010), Cortier et al. (2014), and Kiayias et al. (2015). We discover that voting systems vulnerable to attacks can be proven to satisfy each of those definitions and conclude they are unsuitable for the analysis of voting systems. Our results will fuel the exploration for a new definition.
Distributed Deep Learning Approach for Optimal Hyper-Parameter Values

With the development of artificial intelligence, there are great changes especially in technology and industry sectors. The fact that deep learning and reinforcement learning studies are popular topics by researchers accelerates this change. In this article, a distributed system is presented to determine the hyper-parameters of the deep learning algorithm used for object detection at the most accurate value. One of the most important factors affecting the accuracy rate in object recognition approaches using deep learning algorithms is the determination of hyper-parameters with correct values. It may be necessary to carry out very long experiments to determine the optimum of these parameters. To solve this problem, a deep learning network used for object detection has been trained by combining the RAY distributed architecture with a deep learning algorithm. The accuracy rate is observed by changing the parameters in each iteration. For object detection, the training of the neural network we created with the CIFAR-10 dataset was carried out using CPU. In addition, thanks to the distributed architecture, each process is trained by 4 different workers. The training results and the properties of the artificial neural network are given in detail in the following sections. Accordingly, we can highlight the main contributions of this article in three points. Firstly; to show that long processes are completed in a short time, thanks to the integration of deep learning algorithms with the distributed system; training the model used to determine the optimal hyper-parameter values and the third is the presentation of the distributed deep learning approach.
OCtS: an alternative of the t-Score method sensitive to outliers and correlation in feature selection

A wide range of issues including missing values, class noise, class imbalance, outliers, correlation and irrelevant variables have the potential to negatively affect the overall performance of disease diagnosis classification algorithms. This study proposes a new technique, alternative to the t-Score method, to increase the performance of ensemble learning classification algorithms by removing irrelevant variables. Therefore, three publicly available datasets from medical domain varying in their sample sizes, number of variables, and data preprocessing problems were selected and processed with our newly proposed feature selection method called Outliers and Correlation t-Score (OCtS). Afterwards, six widely used ensemble learning algorithms including Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting Machine, Light Gradient Boosting Machine, CatBoost, and Bagging were employed for disease diagnosis classification, and performance metrics were measured. Our results indicate that the classification performance of six ensemble learning algorithms significantly increased when the OCtS method was employed, and our feature selection method, OCtS, exhibited higher performance compared to the standard t-score method across all datasets (p = 0.0001). We conclude that, using data preprocessing methods with OCtS offers better algorithm performance when employing ensemble learning algorithms in disease diagnosis classification.
Toward Engineering Design Principles for HCI

This is the second of two books by the authors about engineering design principles for human-computer interaction (HCI-EDPs). The books report research that takes an HCI engineering discipline approac
Coyote–Wolf optimization-based deep neural network for cancer classification using gene expression profiles

Cancer is a deadly disease that affects millions of people all over the world, which stimulates unrestricted segmentation of the cell in the affected tissue. The prior determination of cancerous cells renders on-time healthcare service to the affected persons, which promotes easier life-saving. Hence, there is a need for automatic cancer classification methods. Though there are so many conventional classification methods to determine the cancer cells, these methods fail due to their training issues. In this research, a classification model is developed, named as Coyote–Wolf Optimization-based Deep Neural Network (CoWo-DNN), for the classification of cancerous cell from the normal tissues. The proposed CoWo-DNN for the classification of cancer cell utilizes gene expression data, which is log-transformed for effective processing. The comparative evaluation of the proposed CoWo-DNN with the conventional methods demonstrates the supremacy of the proposed method in terms of the performance parameters such as precision rate, accuracy, recall, [Formula: see text]-measure and TRP. The proposed CoWo-DNN achieves the maximum accuracy of 91.8%, precision of 93.7%, recall of 89.2% and [Formula: see text]-measure of 86.4% in the case of breast cancer, and the accuracy of 96.2%, precision of 93.33%, recall of 97.8% and [Formula: see text]-measure of 98.8% in the case of colon cancer.
Wavefront sensing of interference fringe based on generative adversarial network

To increase the measurement accuracy of optical systems, which are implemented in various applications, an improvement of the optical measurement technique is required. This paper proposes an image-to-image wavefront sensing approach using a deep neural network that directly predicts the phase image from the corresponding interference fringe image instead of reconstruction by the Zernike coefficients. The model is based on a conditional generative adversarial network (CGAN). To train the model, we used the formula-based ideal interference fringe images as the inputs of the CGAN, to conditionally predict the corresponding phase images as the output. We numerically investigated the performance by calculating the similarity between the ideal phase image and model output. In addition, with reference to a previous study, it was determined whether the model can extract more features from the interferogram for the prediction of Zernike coefficients. Moreover, an optical simulation software was introduced to provide an increased number of actual interferograms, to verify the proposed method. Based on the results, the proposed system can obtain the phase image directly and reduce the error, thus improving the measurement accuracy of the interference fringe.
Coupling of Dimensionality Reduction and Stacking Ensemble Learning for Smartphone-Based Human Activity Recognition

Human activity recognition (HAR) plays a vital role in the field of ambient assisted living (AAL) for the welfare of the elders who live alone in the home. AAL provides service through ambient sensors, vision systems, smartphone devices, and wearable sensors. Smartphone devices are familiar, portable, cost-effective, and make the process of monitoring easier. Various research works have proposed smartphone-based HAR systems to recognize basic and complex activities. However, the results are not satisfactory for the case of postural transitions such as stand-to-sit, sit-to-sleep, etc. To improve the recognition rate, this paper couples principal component analysis with stacking ensemble learning for dimensionality reduction and classification respectively. Extensive experimentation of UCI repository datasets such as UCI-HAR has been performed and the performances are measured using familiar metrics such as accuracy, precision, and recall.
Toward the Mentality Accounting in Large Social Systems Models

Modeling and simulation of social phenomena are very important for understanding such phenomena. This paper proposes the consideration of such human qualities as mentality, anticipation, the acquisition and generation of knowledge, especially when considering interacting individuals. Considerations are based on models of society in the form of networks with associative memory with elements that also take into account the internal mental representations of individuals. An example of such model is proposed.
Multi-scale Vehicle Tracking Algorithm with the Global Context Module

Fully-convolutional Siamese network vehicle tracking algorithm (SiamFC) has become a research hotspot in the field of vehicle tracking in recent years. However, this kind of tracker algorithm lacks deep feature extraction ability and overall perception of the target vehicle, is easy to lose in the case of complex background, scale changing and changing illumination. This paper proposes to use the Inception network as the backbone network, and according to the characteristics of tracking model, optimized it from two aspects: cutting the feature map and adjusting the total network step size, making inception network can extract the high-level semantic information of the vehicle while maintaining the sensitivity to the change of vehicle position. And introducing the Non-local network (NLNet) into the branch networks to enhance the overall perception of the tracking model for the target vehicle. Experiments show that the tracking speed and robustness of the proposed algorithm are significantly improved in the case of scale changing, illumination variation and complex background. Better tracking results can be obtained in dataset OTB2013 and OTB2015. Compared with the classical trackers such as SiamFC, the tracking accuracy is improved by 4.2% in OTB2015, and the tracking success rate is improved by 2.6% in OTB2015, and the tracking speed is up to 90FPS.
The Relationship between Particle Size and Element Distribution in Stream Sediments from the Dongyuan W-Mo Deposit, Eastern China

Particle size exerts significant control on the concentration of elements in stream sediments and is therefore critical in stream sediment-based geochemical exploration, which has proved important in China’s National Geochemical Mapping Project. There are various geographical landscapes in China with different distribution characteristics of stream sediments. Therefore, we studied the relationship between particle size and element distribution in stream sediments, which is always a crucial but challenging issue in geochemical surveys. The distributions of minerals and elements in eight size fractions of stream sediments (2–0.84, 0.84–0.42, 0.42–0.25, 0.25–0.177, 0.177–0.125, 0.125–0.096, 0.096–0.074, and &lt;0.074 mm) from the Dongyuan W-Mo deposit in eastern China were studied. The results show that the 2–0.25 mm particle size fraction of stream sediments is composed mainly of rock debris and various minerals from broken bedrock upstream, while the &lt;0.25 mm fraction is composed mainly of clay, individual minerals, and organic matter. The pilot survey results prove that using 2–0.25 mm as the sampling particle size fraction is better than using &lt;0.25 mm, especially in geochemical prospecting and geological body delineation. Sampling the 2–0.25 mm particle size fraction of stream sediments can help to delineate proven ore bodies, ore-related anomalies, and geological bodies more effectively and more credibly. The suggested sampling particle size fraction for a stream sediment geochemical survey in a humid to semi-humid low mountain landscape in eastern China is therefore 2–0.25 mm, rather than the particle size fraction of &lt;0.25 mm that was used for sampling in this area before. This paper depicts a successful example for determining the optimal sampling particle size fraction for stream sediment-based geochemical exploration.