Reviewerly/exHarmony huggingface dataset:
Designing immersive experiences in virtual reality for design for additive manufacturing training

Adopting additive manufacturing (AM) in product design is limited by the shortage of skilled designers with design for AM (DfAM) expertise. This lack of AM talent is a major hurdle for organizations looking for innovative solutions to solve emerging engineering problems. Developing institutional talent in DfAM and AM process knowledge is of paramount importance to support organizations looking to leverage AM. Therefore, future designers need meaningful experiences to acquire DfAM intuition and AM process competency to enable informed artifact generation during the design process. This necessitates supplying active experiences that highlight the advantages and limitations of AM technologies and promote DfAM intuition. However, limited access to in-person experiences with physical AM systems inhibits designers from gaining such intuition. Digital modalities of AM education, such as computer-aided (CAx) and virtual reality (VR) tools, can alternatively provide access to experiential learning to cultivate this AM expertise. Where CAx experiences are typically associated with non-immersive modalities, VR experiences can offer enhanced immersion to replicate in-person experiences. There is a gap in the literature, however, that investigates immersive and non-immersive modalities on their effectiveness in training and artifact generation for AM. Additionally, there is a scarcity of information on design guides for creating immersive experiences that can support these research endeavors. It is essential to establish guidance on designing immersive experiences that offer meaningful and active engagement with AM technologies. Such guidance will support the development of experiences that cultivate DfAM intuition, AM process competency, and problem-solving skills. This research is thus motivated to explore the design of immersive VR experiences for problem-solving and artifact generation with AM. For this purpose, this work presents a generalized framework to inform the design of such experiences along with an example created using the proposed framework.
Monocular camera-based online sensor-to-segment calibration for upper body pose estimation

Robot-assisted rehabilitation training usually requires body pose estimation for either evaluation or robot control. Wearable sensors such as inertial measurement units (IMUs) have been extensively studied as affordable solutions. However, a prerequisite is to calibrate the relative transform between the sensor frame and segment anatomical frame, which is usually complex and time-consuming. In this paper, a visual-inertial fusion approach is proposed where a monocular camera is employed to overcome the drawbacks of IMUs and simplify the calibration process. The locations of IMUs are detected from vision using ArUco markers attached to them and the sensor-to-segment calibration is achieved using additional skeletal keypoint locations detected using the MediaPipe framework during arbitrary movements. Experiments were conducted to evaluate the accuracy of the proposed calibration method and the root mean square error of the shoulder and elbow joint angles are 5.13–7.86° with correlation coefficients above 0.96 in comparison to ground truth joint angles from the optical motion capture system. The proposed method achieves similar accuracy to traditional predefined poses-based calibration methods but simplifies the procedure, making it suitable for patients who cannot perform poses accurately in rehabilitation.
An Approach for Risk Traceability Using Blockchain Technology for Tracking, Tracing, and Authenticating Food Products

Regulatory authorities, consumers, and producers alike are alarmed by the issue of food safety, which is a matter of international concern. The conventional approaches utilized in food quality management demonstrate deficiencies in their capacity to sufficiently address issues related to traceability, transparency, and accountability. The emergence of blockchain technology (BCT) has provided a feasible approach to tackle the challenge of regulating food safety. This research paper presents a methodology for implementing blockchain technology to establish risk traceability in the context of monitoring, tracing, and authenticating agricultural products. The proposed system underwent a comprehensive evaluation, which placed significant emphasis on simulation parameters and assessment standards. The aim of the study was to demonstrate the effectiveness of the system through the assessment of various quantitative metrics, including throughput, latency, and resource utilization. The Hyperledger Fabric and Hyperledger Caliper were employed in the formulation and assessment of algorithms intended for agricultural supply chain management. The configuration comprising two entities and two peers achieved the highest write throughput (205.87 transactions per second; TPS), thereby demonstrating the network’s effective transaction processing capability. In a two-organization, two-peer system, the mean latency for read operations exhibited variability spanning from 0.037 to 0.061 s, contingent upon the transaction rates and accounting for the duration needed for network processing and validation. The results were visually depicted, offering a distinct demonstration of the system’s efficacy under various conditions. This study presents a quantitative analysis that illustrates the efficacy of the blockchain system in enhancing the traceability of agricultural products across the entire supply chain. The results of this research suggest that the implementation of blockchain technology could potentially enhance both the security and efficacy of food supply management.
Evaluating generation of chaotic time series by convolutional generative adversarial networks

To understand the ability and limitations of convolutional neural networks to generate time series that mimic complex temporal signals, we trained a generative adversarial network consisting of convolutional networks to generate chaotic time series and used nonlinear time series analysis to evaluate the generated time series. A numerical measure of determinism and the Lyapunov exponent showed that the generated time series well reproduce the chaotic properties of the original time series. However, error distribution analyses showed that large errors appeared at a low but non-negligible rate. Such errors would not be expected if the distribution were assumed to be exponential.
Understanding Condensed Matter Physics With Refined Strong and Electroweak Mass Formula

In our recent publications, based on strong and electroweak interactions, we have developed a completely new formula for estimating nuclear binding energy. With reference to currently believed Semi Empirical Mass Formula (SEMF), we call our formula as ‘Strong and Electroweak Mass Formula’ (SEWMF). Our formula constitutes 4 simple terms and only one energy coefficient. First term is a volume term, second term seems to be a representation of free nucleons associated with electroweak interaction, third term is a radial term and fourth one is an asymmetry term about the mean stable mass number. Considering this kind of approach, condensed matter and nuclear structure can be understood in terms of strong and weak interactions and cold nuclear fusion like complicated concepts can be understood in a theoretical approach positively. In this paper, considering nuclear volume as ‘core inner’ and ‘core outer’ and by considering a correction factor of [2-(N/Z)] and second expression [Z^2+N^2+(Z^2/N)^2] an attempt is made for estimating the number of free nucleons pertaining to electroweak term. Considering our first expression (Z^2+A^2)/2 and the proposed second expression for free nucleons, we noticed that, for medium and heavy atomic nuclides, binding energy estimated with second expression is matching with reference binding energy at A=2Z. Binding energy estimated with first expression is matching with reference binding energy at A=3.2Z. As a compromise, for A=2Z to 3.2Z, we propose the third expression for estimating number of free nucleons as [Z^2+N^2+(Z^2/N)^2]-[(A-2Z)/2]^2. Accuracy point of view, in this procedure, selected stable mass number of Z also plays a crucial role. Based on the experimental binding energy values of 2521 nuclides, root mean square deviation is 5.96 MeV and it needs further study.
Preferring print: The planned behavior and preferences of first-generation college students in the academic library

Academic libraries have been adapting and changing their collections with technology. Often this technology has accompanied a transition from physical collections, such as print books, to electronic collections and electronic books. Understanding how this shift away from print formats might affect certain campus populations is essential as electronic collections continue to grow and expand in various academic institutions. This mixed methods case study aimed to understand how first-generation college students at a public research university use print books versus electronic books. Data was collected in two phases, with the first phase consisting of a Likert scale survey distributed to 4419 potential participants. The second phase was a qualitative semi-structured interview with 19 self-identified participants from the survey. The survey did not indicate a strong preference for print books over electronic books. However, the qualitative interviews did indicate that first-generation students preferred using print to facilitate their reading styles. The study showed that students prefer to use print books over electronic formats daily at their academic institutions for various factors, including the ability to focus and review information. However, the primary reason first-generation college students prefer print books is that it helps them retain information for classes better than electronic books.
Research of trusted real‐time electrical data transmission mechanism based on Parallel Proof of Work algorithm

Abstract Secure and reliable electricity supply is a prerequisite for the development of smart cities, and the trustworthy and efficient transmission of electrical data is the foundation for the safe and stable operation of the power grid. This paper introduces a real‐time data transmission blockchain technique based on parallel proof of work algorithm. The new block generation process of proposed blockchain is divided into five subroutines: hash pointer computation, real‐time data pudding, signature value iteration, interruption, block header assembly. The real‐time data pudding and signature value iteration are parallel processed, which brings the effect of decreasing energy loss of blockchain system, and enhances the speed of new block generation and the bandwidth of data storage on blockchain. Computer simulation shows the proposed strategy can be effectively applied in real‐time electrical data transmission application, raising the data transmission reliability with no harm to real‐time data transfer function. This strategy provides a solution to guarantee data transmission safety in the digital conversion of power grid.
A comprehensive review of blockchain integration in remote patient monitoring for E‐health

Abstract The integration of the Internet of Things (IoT) with blockchain technology has enabled a significant digital transformation in the areas of E‐health, supply chain, financial services, smart grid, and automated contracts. Many E‐health organizations take advantage of the game‐changing power of blockchain and IoT to improve patient outcomes and optimize internal operational activities. In particular, it proposes a decentralized and evolutive way to model and acknowledge trust and data validity in a peer‐to‐peer network. Blockchain promises transparent and secure systems to provide new business solutions, especially when combined with smart contracts. In this paper, we provide a comprehensive survey of the literature involving blockchain technology applied to E‐health. First, we present a brief background on blockchain and its fundamentals. Second, we review the opportunities and challenges of blockchain in the context of E‐health. We then discuss popular consensus algorithms and smart contracts in blockchain in conjunction with E‐health. Finally, blockchain platforms are evaluated for their suitability in the realm of IoT‐based E‐health, including electronic health records, electronic management records, and personal health records, from the perspective of remote patient monitoring.
Online Learning Sample Filtering for Object Tracking

Object tracking tasks often face problems such as illumination and appearance changes due to various scenarios. To effectively solve such problems, single object tracking algorithms in recent years have improved the stability of the tracker by introducing online learning, reducing tracking drift, and tracking objects stably even with large deformations. However, on the one hand, employing online learning increases computation costs, causing the tracker to run slower. On the other hand, because it learns from the tracking results, the tracking template will only be correctly updated if the object is accurately tracked. Any incorrect update will result in tracking failure. In this paper, we propose a filtering algorithm for online learning sample processing in object tracking to remedy these issues. Our method can select valid training samples and discard unnecessary training samples in object tracking. Numerous experiments demonstrate that our algorithm improves the tracking accuracy, reduces the computation complexity, and improves the running speed and run‐time. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Entropy‐aware energy‐efficient virtual machine placement in cloud environments using type information

Summary One of the practical preferences of cloud service providers is to use specialized physical hosts. In other words, the goal is to place homogeneous virtual machines (VMs) on the physical host according to performance criteria such as energy consumption, resource wastage, and utilization. virtual machine placement (VMP) falls into NP‐hard knapsack problems. To overcome the time complexity, the use of heuristic and metaheuristic methods has attracted the attention of researchers. In this paper, we use an entropy‐based method for VMP for the first time. The proposed method tries to place the VMs on physical machines by considering the type of VMs to minimize entropy. Entropy is a measurable property that is more associated with disorder, randomness, or uncertainty. We use one of the most common entropy criteria called the Gini coefficient. In summary, among the different placement combinations of VMs, those that can minimize the Gini coefficient are preferred. We then solve the multi‐objective problem with the non‐dominated sorting genetic algorithm (NSGA‐III). We also combine this method with differential evolution methods to improve the quality of solutions. Recent research in other engineering fields has shown that combining metaheuristic methods with differential evolution methods increases the rate of convergence toward the optimal solution. The simulation results on the CloudSim simulator, along with statistical analysis, show that the entropy‐based method has a significant improvement over the state‐of‐the‐art methods in terms of significant performance criteria such as utilization, resource wastage, and energy consumption.
Brain-Inspired Binaural Sound Source Localization Method Based on Liquid State Machine

Binaural Sound Source Localization (BSSL) is a remarkable topic in robot design and human hearing aid. A great number of algorithms flourished due to a leap in machine learning. However, prior approaches lack the ability to make a trade-off between parameter size and accuracy, which is a primary obstacle to their further implementation on resource-constrained devices. Spiking Neural Network (SNN)-based models have also emerged due to their inherent computing superiority over sparse event processing. Liquid State Machine (LSM) is a classic Spiking Recurrent Neural Network (SRNN) which has the natural potential of processing spatiotemporal information. LSM has been proved advantageous on numerous tasks once proposed. Yet, to our best knowledge, it is the first proposed BSSL model based on LSM, and we name it BSSL-LSM. BSSL-LSM is lightweight with only 1.04M parameters, which is a considerable reduction compared to CNN (10.1M) and D-BPNN (2.23M) while maintaining comparable or even superior accuracy. Compared to SNN-IID, there is a 10% accuracy improvement for $$10^\circ $$ interval localization. To achieve better performance, we introduce Bayesian Optimization (BO) for hyperparameters searching and a novel soft label technique for better differentiating adjacent angles, which can be easily mirrored on related works. Project page: https://github.com/BSSL-LSM .
Link Prediction Based on the Sub-graphs Learning with Fused Features

As one of the important research methods in the area of the knowledge graph completion, link prediction aims to capture the structural information or the attribute information of nodes in the network to predict the link probability between nodes, In particular, the graph neural networks based on the sub-graphs provide a popular approach for the learning representation to the link prediction tasks. However, they cannot solve the resource consumption in large graphs, nor do they combine global structural features since they often simply stitch attribute features and embedding to predict. Therefore, this paper proposes a novel link prediction model based on the Sub-graphs Learning with the Fused Features, named SLFF in short. In particular, the proposed model utilizes random walks to extract the sub-graphs to reduce the overhead in the process. Moreover, it utilizes the Node2Vec to process the entire graph and obtain the global structure characteristics of the node. Afterward, the SLFF model utilizes the existing embedding to reconstruct the embedding according to the neighborhood defined by the graph structure and node attribute space. Finally, the SLFF model can combine the attribute characteristics of the node with the structural characteristics of the node together. The extensive experiments on datasets demonstrates that the proposed SLFF has better performance than that of the state-of-the-art approaches.
Task Scheduling with Improved Particle Swarm Optimization in Cloud Data Center

This paper proposes an improved particle swarm optimization algorithm with simulated annealing (IPSO-SA) for the task scheduling problem of cloud data center. The algorithm uses Tent chaotic mapping to make the initial population more evenly distributed. Second, a non-convex function is constructed to adaptively and decreasingly change the inertia weights to adjust the optimization-seeking ability of the particles in different iteration periods. Finally, the Metropolis criterion in SA is used to generate perturbed particles, combined with an modified equation for updating particles to avoid premature particle convergence. Comparative experimental results show that the IPSO-SA algorithm improves 13.8% in convergence accuracy over the standard PSO algorithm. The respective improvements over the other two modified PSO are 15.2% and 9.1%.
RPF3D: Range-Pillar Feature Deep Fusion 3D Detector for Autonomous Driving

In this paper, we present RPF3D, an innovative single-stage framework that explores the complementary nature of point clouds and range images for 3D object detection. Our method addresses the sampling region imbalance issue inherent in fixed-dilation-rate convolutional layers, allowing for a more accurate representation of the input data. To enhance the model's adaptability, we introduce several attention layers that accommodate a wide range of dilation rates necessary for processing range image scenes. To tackle the challenges of feature fusion and alignment, we propose the AttentiveFusion module and the Range Image Guided Deep Fusion (RIGDF) backbone architecture in the Range-Pillar Feature Fusion section, which effectively addresses the one-pillar-to-multiple-pixels feature alignment problem caused by the point cloud encoding strategy. These innovative components work together to provide a more robust and accurate fusion of features for improved 3D object detection. We validate the effectiveness of our RPF3D framework through extensive experiments on the KITTI and Waymo Open Datasets. The results demonstrate the superior performance of our approach compared to existing methods, particularly in the Car class detection where a significant enhancement is achieved on both datasets. This showcases the practical applicability and potential impact of our proposed framework in real-world scenarios and emphasizes its relevance in the domain of 3D object detection.
Privacy-Preserving Travel Time Prediction for Internet of Vehicles: A Crowdsensing and Federated Learning Approach

Travel time prediction (TTP) is an important module task to support various applications for Internet of Vehicles (IoVs). Although TTP has been widely investigated in the existing literature, most of them assume that the traffic data for estimating the travel time are comprehensive and public for free. However, accurate TTP needs real-time vehicular data so that the prediction can be adaptive to traffic changes. Moreover, since real-time data contain vehicles' privacy, TTP requires protection during the data processing. In this paper, we propose a novel Privacy-Preserving TTP mechanism for IoVs, $$\mathbb{P}\mathbb{T}$$ Prediction, based on crowdsensing and federated learning. In crowdsensing, a data curator continually collects traffic data from vehicles for TTP. To protect the vehicles' privacy, we make use of the federated learning so that vehicles can help the data curator train the prediction model without revealing their information. We also design a spatial prefix encoding method to protect vehicles' location information, along with a ciphertext-policy attribute-based encryption (CP-ABE) mechanism to protect the prediction model of the curator. We evaluate $$\mathbb{P}\mathbb{T}$$ Prediction in terms of MAE, MSE, RMSE on two real-world traffic datasets. The experimental results illustrate that the proposed $$\mathbb{P}\mathbb{T}$$ Prediction shows higher prediction accuracy and stronger privacy protection comparing to the existing methods.
MTLAN: Multi-Task Learning and Auxiliary Network for Enhanced Sentence Embedding

The objective of cross-lingual sentence embedding learning is to map sentences into a shared representation space, where semantically similar sentence representations are closer together, while distinct sentence representations exhibit clear differentiation. This paper proposes a novel sentence embedding model called MTLAN, which incorporates multi-task learning and auxiliary networks. The model utilizes the LaBSE model for extracting sentence features and undergoes joint training on tasks related to sentence semantic representation and distance measurement. Furthermore, an auxiliary network is employed to enhance the contextual expression of words within sentences. To address the issue of limited resources for low-resource languages, we construct a pseudo-corpus dataset using a multilingual dictionary for unsupervised learning. We conduct experiments on multiple publicly available datasets, including STS and SICK, to evaluate both monolingual sentence similarity and cross-lingual semantic similarity. The empirical results demonstrate the significant superiority of our proposed model over state-of-the-art methods.