Reviewerly/exHarmony huggingface dataset:
Bio-Inspired Deformable Propeller Concept for Smooth Human-UAV Interaction and Efficient Thrust Generation

This letter presents a novel deformable propeller concept, 3D-printed in flexible thermoplastic polyurethane, which stores impact energy in the form of elastic energy for smooth collisions, reducing risk in human-UAV interactions. However, such a design experiences elastic deformations when exposed to aerodynamic and centrifugal loads. The most relevant occur in the 3-5 krpm range. At higher speeds, the centrifugal force is dominant and the propeller becomes quite stiff. This work provides an investigation of the propeller deformation angles (which ultimately alter the aerodynamic profile and limit thrust generation) based on Fluid-Structure Interaction (FSI) simulations. These results are leveraged to introduce two complementary solutions: deformation reduction oriented internal fiber distributions, inspired by the ultra-efficient dragonfly wings (I); anticipatory designs which pre-modify pitch and roll angles based on simulation results to ensure optimal performance at the target rotational velocity (II). This letter offers a multi-configuration analysis, yielding an easy to manufacture propeller with a specific design methodology which results in increased efficiency and reduced impact recovery time. This work presents collision tests with various objects, as well as a proof of concept for its flight capabilities in a conventional quadrotor, including physical interaction with humans. These findings are valuable for the development of collision control strategies for UAVs.
A masked-face detection algorithm based on M-EIOU loss and improved ConvNeXt

Feature extraction networks play a crucial role in classification algorithms. However, most convolutional neural networks use the form of residual block stacking to extract downstream features of images, and this simple form is not sufficient to obtain better classification performance. Therefore, this paper proposes a YOLOX detection framework based on an improved ConvNeXt which incorporates many advantages of Swin-Transformer with efficient classification performance. To address the problems of unstable IOU convergence and inaccurate regression coordinates of the algorithm, this paper adopts Manhattan distance as the new penalty term, and the new loss function combines the advantages of Euclidean distance and Manhattan distance, which has no gradient explosion in the early training period and can drop smoothly to a stable point in the late training period. The proposed algorithm is robust to negative effects such as dust, light, shadows, and occlusions, and is well-suited for masked-face detection in industrial and public scenarios. The proposed model is tested on the VOC dataset and the AIZOO open-source masked-face dataset, obtaining 5.58% AP improvement on the VOC dataset and reaching 96.665% AP on the masked-face dataset, which is better than other algorithms.
Sophisticated deep learning with on-chip optical diffractive tensor processing

Ever-growing deep-learning technologies are making revolutionary changes for modern life. However, conventional computing architectures are designed to process sequential and digital programs but are burdened with performing massive parallel and adaptive deep-learning applications. Photonic integrated circuits provide an efficient approach to mitigate bandwidth limitations and the power-wall brought on by its electronic counterparts, showing great potential in ultrafast and energy-free high-performance computation. Here, we propose an optical computing architecture enabled by on-chip diffraction to implement convolutional acceleration, termed “optical convolution unit” (OCU). We demonstrate that any real-valued convolution kernels can be exploited by the OCU with a prominent computational throughput boosting via the concept of structral reparameterization. With the OCU as the fundamental unit, we build an optical convolutional neural network (oCNN) to implement two popular deep learning tasks: classification and regression. For classification, Fashion Modified National Institute of Standards and Technology (Fashion-MNIST) and Canadian Institute for Advanced Research (CIFAR-4) data sets are tested with accuracies of 91.63% and 86.25%, respectively. For regression, we build an optical denoising convolutional neural network to handle Gaussian noise in gray-scale images with noise level σ =10, 15, and 20, resulting in clean images with an average peak signal-to-noise ratio (PSNR) of 31.70, 29.39, and 27.72 dB, respectively. The proposed OCU presents remarkable performance of low energy consumption and high information density due to its fully passive nature and compact footprint, providing a parallel while lightweight solution for future compute-in-memory architecture to handle high dimensional tensors in deep learning.
Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.
A storage-efficient SNN–CNN hybrid network with RRAM-implemented weights for traffic signs recognition

Traffic Signs Recognition (TSR) is a key technology to implement Automatic Driving System (ADS) and Advanced Driver Assistant System (ADAS). Numerous efforts have been endeavored to improve the TSR accuracy and speed, and Convolution Neural Networks (CNN) are usually employed. However, most of the existing works require large network scale and thus suffer from the drawback of high computation and power consumption. To alleviate this issue, in this paper, we propose an SNN–CNN hybrid network with RRAM-implemented weights to improve the storage and computing efficiency of TSR. Specifically, we utilize SNN to quickly determine the superclass that a traffic sign belongs to. Then utilize CNNs to determine the subclass accurately, thus the complex TSR task is decomposed into multiple simple tasks. Finally, the network weights are binarized to alleviate the implementation of the hybrid network on RRAM-based accelerators, which have higher power efficiency than CMOS logic based ones. Experimental results indicate that, when compared with state of the art CNN method, the SNN–CNN hybrid network can achieve similar accuracy with less weight scale (−69.21%) and lower power consumption (−81.55%). Binarizing the hybrid network can further reduce the weights storage requirement by another 96.875% with only 1.518% accuracy loss. When the network weights are implemented with state of the art RRAM array (resistance variation is 5%), the proposed network can achieve a mean accuracy of 96.47% with 95% ∼ 97.5% confidence interval.
An Agile Model-Based Software Engineering Approach Illustrated through the Development of a Health Technology System

Model-Based Software Engineering (MBSE) is an architecture-based software development approach. Agile, on the other hand, is a light system development approach that originated in software development. To bring together the benefits of both approaches, this article proposes an integrated Agile MBSE approach that adopts a specific instance of the Agile approach (i.e., Scrum) in combination with a specific instance of an MBSE approach (i.e., Model-Based System Architecture Process—“MBSAP”) to create an Agile MBSE approach called the integrated Scrum Model-Based System Architecture Process (sMBSAP). The proposed approach was validated through a pilot study that developed a health technology system over one year, successfully producing the desired software product. This work focuses on determining whether the proposed sMBSAP approach can deliver the desired Product Increments with the support of an MBSE process. The interaction of the Product Development Team with the MBSE tool, the generation of the system model, and the delivery of the Product Increments were observed. The preliminary results showed that the proposed approach contributed to achieving the desired system development outcomes and, at the same time, generated complete system architecture artifacts that would not have been developed if Agile had been used alone. Therefore, the main contribution of this research lies in introducing a practical and operational method for merging Agile and MBSE. In parallel, the results suggest that sMBSAP is a middle ground that is more aligned with federal and state regulations, as it addresses the technical debt concerns. Future work will analyze the results of a quasi-experiment on this approach focused on measuring system development performance through common metrics.
Metaverse for education – Virtual or real?

Metaverse is considered an extension of the Internet that is expected to revolutionize our social and economic lives by offering seamless interactions in the virtual worlds. Some believe that the Metaverse is one of those technologies that are destined to fail while others envisage Metaverse as part and parcel of future life, same as the Internet in today’s world. While the jury is still out on whether the Metaverse would eventually be materialized to its full potential, billions have been invested into to the development of the Metaverse and its related technologies by the technology giants such as Meta, Microsoft, and Apple. The Metaverse promises to bring a step change in every walk of life and Education sector is no exception. A true realization of the Metaverse will make the geographical boundaries disappear making the education accessible to the least privileged communities. At the same time, the virtual worlds with immersive experiences of usually less accessible resources would provide at-scale resource sharing throughout the world. The Metaverse would enable seamless access of Stanford labs, for instance, to someone in Africa to do collaborative experiments in the virtual worlds. This Perspective presents the status of the Metaverse in Education. It describes the future of Education sector with the integration of the Metaverse. In the end it presents the author’s perspective on the practicalities and challenges of the Metaverse in Education. The author believes that despite all the hype created around Metaverse, it will take a pandemic-like disruption for the academic world to truly embrace and integrate the Metaverse in Education sector.
Cross-Modality Person Re-Identification via Local Paired Graph Attention Network

Cross-modality person re-identification (ReID) aims at searching a pedestrian image of RGB modality from infrared (IR) pedestrian images and vice versa. Recently, some approaches have constructed a graph to learn the relevance of pedestrian images of distinct modalities to narrow the gap between IR modality and RGB modality, but they omit the correlation between IR image and RGB image pairs. In this paper, we propose a novel graph model called Local Paired Graph Attention Network (LPGAT). It uses the paired local features of pedestrian images from different modalities to build the nodes of the graph. For accurate propagation of information among the nodes of the graph, we propose a contextual attention coefficient that leverages distance information to regulate the process of updating the nodes of the graph. Furthermore, we put forward Cross-Center Contrastive Learning (C3L) to constrain how far local features are from their heterogeneous centers, which is beneficial for learning the completed distance metric. We conduct experiments on the RegDB and SYSU-MM01 datasets to validate the feasibility of the proposed approach.
User Experience Evaluation in Shared Interactive Virtual Reality

Virtual reality (VR) has served the entertainment industry all the way to world-leading museums in delivering engaging experiences through multisensory virtual environments (VEs). Today, the rise of the Metaverse fuels a growing interest in leveraging this technology, bringing along an emerging need to better understand the way different dimensions of VEs, namely social and interactive, impact overall user experience (UX). This between-subject exploratory field study investigates differences in the perceived and lived experience of 28 participants engaging, either individually or in dyads, in a VR experience comprising different levels of interactivity, i.e., passive or active. A mixed methods approach combining conventional UX measures, i.e., psychometric surveys and user interviews, as well as psychophysiological measures, i.e., wearable bio- and motion sensors, allowed for a comprehensive assessment of users' immersive and affective experiences. Results pertaining to the social dimension of the experience reveal that shared VR elicits significantly more positive affect, whereas presence, immersion, flow, and state anxiety are unaffected by the copresence of a real-world partner. Results pertaining to the interactive dimension of the experience suggest that the interactivity afforded by the VE moderates the effect of copresence on users' adaptive immersion and arousal. These results support that VR can be shared with a real-world partner not only without hindering the immersive experience, but also by enhancing positive affect. Hence, in addition to offering methodological directions for future VR field research, this study provides interesting practical insights into guiding VR developers toward optimal multiuser virtual environments.
A novel human learning optimization algorithm with Bayesian inference learning

Humans perform Bayesian inference in a wide variety of tasks, which can help people make selection decisions effectively and therefore enhances learning efficiency and accuracy. Inspired by this fact, this paper presents a novel human learning optimization algorithm with Bayesian inference learning (HLOBIL), in which a Bayesian inference learning operator (BILO) is developed to utilize the inference strategy for enhancing learning efficiency. The in-depth analysis shows that the proposed BILO can efficiently improve the exploitation ability of the algorithm as it can achieve the optimal values and retrieve the optimal information with the accumulated search information. Besides, the exploration ability of HLOBIL is also strengthened by the inborn characteristics of Bayesian inference. The experimental results demonstrate that the developed HLOBIL is superior to previous HLO variants and other state-of-art algorithms with its improved exploitation and exploration abilities.
Conjoint Analysis of Blockchain Adoption Challenges in Government

This study identifies the critical challenges for blockchain adoption in government, and more specifically in the delivery of public services in the state governments of India. A literature review and focus-group with stakeholders was conducted to identify critical cfhallenges. Each challenge was ranked based on the opinions of stakeholders using conjoint analysis. Regarding government adoption of Blockchain, this study points to ecological shifts as a more significant challenge than technology. This study provides theoretical implications for researchers and valuable insights for practitioners.
Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition

The dichotomy between the challenging nature of obtaining annotations for activities, and the more straightforward nature of data collection from wearables, has resulted in significant interest in the development of techniques that utilize large quantities of unlabeled data for learning representations. Contrastive Predictive Coding (CPC) is one such method, learning effective representations by leveraging properties of time-series data to setup a contrastive future timestep prediction task. In this work, we propose enhancements to CPC, by systematically investigating the encoder architecture, the aggregator network, and the future timestep prediction, resulting in a fully con-volutional architecture. Across sensor positions and activities, our method shows substantial improvements on four of six target datasets, demonstrating its ability to empower a wide range of application scenarios. Further, in the presence of very limited labeled data, our technique significantly outperforms both supervised and self-supervised baselines, positively impacting situations where collecting only a few seconds of labeled data may be possible. This is promising, as CPC does not require specialized data transformations or reconstructions for learning effective representations.
Provably secure public key encryption with keyword search for data outsourcing in cloud environments

In recent days, the application of cloud computing has been gaining significant popularity among people. A considerable amount of data are being stored in the cloud server. However, data owners outsource their encrypted data to the cloud for various security reasons. Unfortunately, encrypted data cannot be searched, like plaintext data. So how to search encrypted data is an interesting problem in this era. Many public key encryption with keyword search (PEKS) schemes have been designed in the literature. However, most of them cannot prevent keyword-guessing attacks. In this paper, we develop a provably secure PEKS scheme in the random oracle model. This scheme may be used for secure email access from an email server containing a list of encrypted keywords. The proposed scheme can resist keyword-guessing attacks, and offer ciphertext and trapdoor indistinguishability properties. We use the data owner’s private key during encryption to prevent keyword-guessing attacks. Using the data owner’s public key in the verification phase ensures the resilience of keyword-guessing attacks. Finally, the proposed scheme has been tested on a real testbed, and the results show that it can be used in the cloud computing scenario to search for keywords on encrypted data.
A Blockchain-based IoT Security Solution Using Multichain

Blockchain technology is increasingly being utilized in Internet of Things (IoT) systems to provide secure data communications and hence prevent data tampering. Private blockchain offers improved speed and efficiency for small networks controlled by a single authoritative node while maintaining the secure, transparent, and immutable characteristics of blockchain technology. Multichain, an open-source private blockchain platform, offers high flexibility in building robust IoT security solutions through customizable configurations and permissions. Key features of Multichain include rapid deployment, unlimited asset management, and streamlined data streams. In this paper, we undertake a comprehensive review of existing work on blockchain IoT solutions using Multichain. To further validate our findings, we conduct a blockchain-based simulation experiment using Multichain and aim to provide insights into the potential applicability of the Multichain platform for securing IoT systems.
Fraud detection in financial statements using data mining and GAN models

Financial statements are analytical reports published periodically by financial institutions explaining their performance from different perspectives. As these reports are the fundamental source for decision-making by many stakeholders, creditors, investors, and even auditors, some institutions may manipulate them to mislead people and commit fraud. Fraud detection in financial statements aims to discover anomalies caused by these distortions and discriminate fraud-prone reports from non-fraudulent ones. Although binary classification is one of the most popular data mining approaches in this area, it requires a standard labeled dataset, which is often unavailable in the real world due to the rarity of fraudulent samples. This paper proposes a novel approach based on the generative adversarial networks (GAN) and ensemble models that is able to not only resolve the lack of non-fraudulent samples but also handle the high-dimensionality of feature space. A new dataset is also constructed by collecting the annual financial statements of ten Iranian banks and then extracting three types of features suggested in this study. Experimental results on this dataset demonstrate that the proposed method performs well in generating synthetic fraud-prone samples. Moreover, it attains comparative performance with supervised models and better performance than unsupervised ones in accurately distinguishing fraud-prone samples.
Emotion-regulatory chatbots for enhancing consumer servicing: An interpersonal emotion management approach

To reduce service staff's burden in customer servicing and improve the performance of automatic service recovery, we propose an emotion-regulatory chatbot for service recovery applications drawing on interpersonal emotion management (IEM) theory. In addition, we develop a model to examine the underlying mechanisms through which perceived IEM strategies influence consumers' emotions and behavioral intentions. Our experimental results verify the effectiveness of IEM strategies. We find that appraisals and consumers' post-recovery emotions sequentially mediate the relationship between perceived emotion regulatory strategies and positive word-of-mouth (PWOM).
Strong quantum nonlocality without entanglement in an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> -partite system with even <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>

In multipartite systems, great progress has been made recently on the study of strong quantum nonlocality without entanglement. However, the existence of orthogonal product sets with strong quantum nonlocality in even party systems remains unknown. Here the even number is greater than four. In this paper, we successfully construct strongly nonlocal orthogonal product sets in $n$-partite systems for all even $n$, which answers the open questions given by Halder et al. [\href{https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.040403} {Phys. Rev. Lett \textbf{122}, 040403 (2019)}] and Yuan et al. [\href{https://journals.aps.org/pra/abstract/10.1103/PhysRevA.102.042228} {Phys. Rev. A \textbf{102}, 042228 (2020)}] for any possible even party systems. Thus, we find general construction of strongly nonlocal orthogonal product sets in space $\otimes_{i=1}^{n}\mathcal{C}^{d_{i}}$ ($n,d_{i}\geq 3$) and show that there do exist incomplete orthogonal product bases that can be strongly nonlocal in any possible $n$-partite systems for all even $n$. Our newly constructed orthogonal product sets are asymmetric. We analyze the differences and connections between these sets and the known orthogonal product sets in odd party systems. In addition, we present a local state discrimination protocol for our sets by using additional entangled resource. When at least two subsystems have dimensions greater than three, the protocol consumes less entanglement than teleportation-based protocol. Strongly nonlocal set implies that the information cannot be completely accessed as long as it does not happen that all parties are together. As an application, we connect our sets with local information hiding in multipartite system.