Reviewerly/exHarmony huggingface dataset:
A Digital Human Emotion Modeling Application Using Metaverse Technology in the Post-COVID-19 Era

The metaverse has the potential to revolutionize various industries recently by providing a more engaging and personalized experience. It includes mainly augmented reality (AR) technology—a quickly growing area that is an interactive experience of a real-world scene added by a virtual world using artificial intelligence (AI). This paper proposes a new digital human emotion modeling study that utilizes augmented reality technology in the post-COVID-19 stage. We conduct a study on how human emotions are recognized and what augmented reality applications these emotional responses are related to. Then, we explore augmented reality systems that provide an immersive and interactive virtual environment where users can experience and express their emotions. By leveraging the advancements in artificial intelligence and augmented reality, the application models and responds to human emotions in real-time, creating a more personalized and human-like experience. In this paper, we divide augmented reality systems with human emotion modeling into two main categories—(1) human emotions modeled and used for evaluating the performances of augmented reality systems, and (2) human emotions modeled and used for triggering augmented reality content. This study is expected to have a great impact on various fields such as human-computer interaction, medical healthcare, and mental health. The paper concludes by discussing the potential benefits and limitations of this technology and its implications for the future.
A mutual certificate-based data privacy scheme for ubiquitous and sustainable computing system users

Ubiquitous sustainable computing systems are deployed for providing complex application services and processing for real-time applications. Data exchange and sharing are confidential for administering user security and privacy. The need for confidentiality is one of the significant obstacles to ubiquitous computing areas like human–machine interfaces and data security. It is crucial in ubiquitous computing to protect user and system security, privacy, and safety. Hence, this article introduces a Linearized Mutual Security Scheme (LMSS) to prevent data leakage and security breaches. The scheme provides mutual authentication based on security requirements depending upon the ubiquitous application. The user credentials are pre-shared with the service providers for determining the security level and certificate sharing. This sharing is updated at fixed intervals for deciding on authentication revocation and strength updates. In this scheme, federated learning is exploited for identifying user security demands and levels administered by different users. Based on the learning recommendations, the key authentication factors are updated in the certificate, preventing anonymous and tracking sessions in the sharing interval. Therefore, the proposed scheme achieves fair anomaly detection of 14.47%, reduces data leakage rate by 13.68%, and halts sessions by 10.26%. Besides, it improves the sharing ratio of 6.92% and application responses.
Immersive Marketing on Metaverse

Measuring immersive marketing experiences in the metaverse is a new area of study and it includes a wide range of algorithms to make better assessments and predictions. Any algorithm or metrics to measure the effectiveness of metaverse marketing will require a large amount of data, and then comes the challenge of data security and privacy. This study primarily aims at providing a framework for measuring the effectiveness of metaverse marketing considering the key drivers of virtual-commerce experiences and the metrics like engagement rates, conversion rates, customer acquisition costs, and the privacy and security-related changes in metaverse data analytics.
Two‐stream spatiotemporal networks for skeleton action recognition

Abstract Skeleton‐based neural networks have been considered a focus for human action recognition (HAR). It is noteworthy that the existing skeleton‐based methods are not capable of combining the spatial and temporal features reasonably to derive more effective high‐level representations, and it continues to be a challenging task of learning and representing the skeleton action discriminatively. In this study, a novel two‐stream spatiotemporal network (TSTN) is proposed, which is capable of processing the spatial and temporal features respectively and collectively to achieve a better representation and understanding of human action. The temporal branch stacks three gate recurrent unit (GRU) blocks in a new architecture to encode the temporal correlations from different aspects of human action, achieving high‐level temporal semantic feature expressions. The spatial branch encodes the spatial features with multi‐stacked graph convolutional network (GCN) blocks. Self‐attention mechanisms incorporated with the graph structure of the skeleton are explored to add weight influence and structural hints to further enhance the performance. The experimental results verify the effectiveness and superiority of the proposed model in skeleton action recognition; the model reaches state‐of‐the‐art on specific datasets.
Improving the success rate of quantum algorithm attacking RSA encryption system

Shor’s factorization algorithm (SFA) aims at finding the non-trivial factor of a given composite number, but the algorithm does not always work. In some cases, it has to call back to the beginning of the algorithm to make recalculation. After the analysis of the principle of SFA and the characteristics of RSA public-key cryptography with a series of data calculations, it can be concluded that the random value selected by the algorithm is closely related to whether the obtained period is effective. Therefore, a new optimized scheme is proposed to tackle with this defect from two perspectives: (a) When the a value is a perfect square, the algorithm can be completed with an odd cycle r. (b) When the period r obtained by the randomly selected a value is a multiple of 3, the algorithm can be completed by modifying the decomposition method to relax the requirements for the period without affecting the complexity of the algorithm. Due to the limitations of hardware in applying the quantum algorithms, the classical algorithm is applied to simulate quantum algorithms to test the success rate of decomposition of some composite numbers. The result indicates the effectiveness of the improved algorithm, which significantly reduces the probability of repeated operations to save the quantum circuit resources.
Deep Note: Can Acoustic Interference Damage the Availability of Hard Disk Storage in Underwater Data Centers?

The growing worldwide attention toward large-scale subsea data centers has garnered substantial interest from commercial entities which have built and deployed underwater prototypes since 2015. These data centers utilize hard disk drives (HDDs) as a cost-effective method of data storage. However, researchers have demonstrated that acoustic waves can affect the availability and integrity of HDDs and applications that rely on them. These studies are all conducted in air on commercial laptops, hence their applicability and implications in submerged environments remain unexplored. In this position paper, we investigate potential vulnerabilities of storage devices deployed in underwater data centers and subsea storage platforms against targeted acoustic attacks. Based on our initial investigation of a simplified scenario, a victim HDD deployed in an enclosed submerged container is especially vulnerable to those acoustic attacks, which at frequencies ranging from 300 Hz 1300 Hz can result in up to 100% throughput loss and application crashes. Based on these findings, we argue that further study is necessary to assess underwater storage system security and develop effective defenses against overlooked acoustic attacks.
A Novel Authentication Method That Combines Honeytokens and Google Authenticator

Despite the rapid development of technology, computer systems still rely heavily on passwords for security, which can be problematic. Although multi-factor authentication has been introduced, it is not completely effective against more advanced attacks. To address this, this study proposes a new two-factor authentication method that uses honeytokens. Honeytokens and Google Authenticator are combined to create a stronger authentication process. The proposed approach aims to provide additional layers of security and protection to computer systems, increasing their overall security beyond what is currently provided by single-password or standard two-factor authentication methods. The key difference is that the proposed system resembles a two-factor authentication but, in reality, works like a multi-factor authentication system. Multi-factor authentication (MFA) is a security technique that verifies a user’s identity by requiring multiple credentials from distinct categories. These typically include knowledge factors (something the user knows, such as a password or PIN), possession factors (something the user has, such as a mobile phone or security token), and inherence factors (something the user is, such as a biometric characteristic like a fingerprint). This multi-tiered approach significantly enhances protection against potential attacks. We examined and evaluated our system’s robustness against various types of attacks. From the user’s side, the system is as friendly as a two-factor authentication method with an authenticator and is more secure.
SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration

How to recover geometric transformations is one of the most challenging issues in image registration. To alleviate the effect of large geometric distortion in multimodal remote sensing image registration, a scale and rotate transform prediction net is proposed in this paper. First, to reduce the scale between the reference and sensed images, the image scale regression module is constructed via CNN feature extraction and FFT correlation, and the scale of sensed image can be recovered roughly. Second, the rotation estimate module is developed for predicting the rotation angles between the reference and the scale-recovered images. Finally, to obtain the accurate registration results, LoFTR is employed to match the geometric-recovered images. The proposed registration network was evaluated on GoogleEarth, HRMS, VIS-NIR and UAV datasets with contrast differences and geometric distortions. The experimental results show that the number of correct matches of our model reached 74.6%, and the RMSE of the registration results achieved 1.236, which is superior to the related methods.
A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images

Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Feature Fusion Module (FFM) is proposed, which can better fuse features by learning the importance of different scale features and improve the representation ability of multi-scale features by reusing feature maps to help models better detect multi-scale objects. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, we tested the CGMDet on VisDrone and UAVDT datasets. The proposed CGMDet improves mAP0.5 by 1.9% on the VisDrone dataset and 3.0% on the UAVDT dataset.
Area‐time efficient point multiplication architecture on twisted Edwards curve over general prime field GF(p)

Abstract Elliptic curve point multiplication is the main primitive required in almost all security schemes using elliptic curve cryptography (ECC). It is the leading computationally intensive operation that sets the overall performance of the associated cryptosystem. This work presents a highly novel area–time efficient elliptic curve point multiplier over a general prime field . It is based on an efficient radix‐2 3 parallel multiplier, which performs a ‐bit multiplication in clock cycles. On the system level, the twisted Edwards curves with unified point addition using projective coordinates are adopted, where an efficient scheduling technique is presented to schedule several operations on deployed modular arithmetic units. Due to the introduced optimization at different stages of the design, latency, hardware resource requirement, and total clock cycle count are reduced significantly. Synthesis, and implementation of the proposed design over different Xilinx FPGA platforms are completed using the Xilinx ISE Design Suite tool for key sizes of 192, 224, and 256 bits. The 256‐bit Xilinx Virtex‐7 FPGA implementation reveals that it completes a single point multiplication operation in 0.8 ms and occupies 6.7K FPGA slices in a clock cycle count of 132.2K. It produces significantly better area–time product and throughput per slice than the contemporary designs. The proposed design also has the potential to counter simple power analysis and timing attacks. Thus, it is an elegant solution to develop ECC‐based cryptosystems for applications, where both speed and hardware resource consumption are important.
Deep Camera–Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions

AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs' safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and false positives due to atmospheric scattering caused by fog particles. The existing deep object detection techniques often exhibit a high degree of accuracy. Their drawback is being sluggish in object detection in fog. Object detection methods with a fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persists. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image onto the camera image. Using the attention mechanism, we emphasized and improved the important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our results show that the proposed method significantly enhances the detection of small and distant objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed, with an accuracy of 0.849 at 69 fps.
A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization

Vendors implement the MAC address randomization technique to prevent IEEE 802.11 client station devices from being tracked. Although it conceals device identity, it cannot hide its occurring data transmission. This paper presents a novel covert channel that leverages the MAC address randomization technique to create a covert channel to hide data transmission inside IEEE 802.11 networks. The secret data are a disposable random MAC address generated by the IEEE 802.11 station as part of the probe request frame while scanning the network. The paper presents the concept of the covert channel, its implementation, and performance metrics. The study covers diverse scenarios, including the adaptation of the modified Selective Repeat ARQ protocol to alleviate the impact of the number of client stations and their offered loads on the covert channel. The results show that with the appropriate parameter selections, we can adapt the covert channel to produce excellent throughput, efficiency, delay, and jitter according to the environment in which it is installed.
Metaverse and its creative potential for visual arts

The National Centre for Culture is conducting a foresight research project on the impact of emerging technologies on the arts. The article presents the first findings brought by the analysis of opportunities created by the development of virtual spaces accessible to artists and the public – which are expected to create the Metaverse and promote a new form of ownership, NFT. The text is dedicated to the potential of new tools and media – the positive side of the interface between modern technologies and art. The article offers definitions of the Metaverse, NFT and digital art. Apparently, these elements of social (and technological) reality not only overlap, but also complement each other. The text attempts to answer the question of what potential the next phase of digital transformation brings for visual artists.
Dietary Behavior Based Food Recommender System Using Deep Learning and Clustering Techniques

Deep learning algorithms have been highly successful in various domains, including the development of collaborative filtering recommender systems. However, one of the challenges associated with deep learning-based collaborative filtering methods is that they require the involvement of all users to construct the latent representation of the input data, which is then utilized to predict the missing ratings of each user. This can be problematic as some users may have different preferences or interests, which may affect the accuracy of the prediction generation process. The research proposed a food recommender system, which tries to find users with similar dietary behavior and involve them in the recommendations generation process by combining clustering technique with denoising autoencoder to generate a rate prediction model. It is applied to “Food.com Recipes and Interactions” dataset. RMSE score was used to evaluate the performance of the proposed model which is 0.1927. It outperformed the other models that used autoencoder and denoising autoencoder without clustering where the RMSE values are 0. 4358 and 0.4354 consequently.
Signature of quantumness in pure decoherence control

We study a decoherence reduction scheme that involves an intermediate measurement on the qubit in an equal superposition basis, in the general framework of all qubit-environment interactions that lead to qubit pure decoherence. We show under what circumstances the scheme always leads to a gain of coherence on average, regardless of the time at which the measurement is performed, demonstrating its wide range of applicability. Furthermore, we find that observing an average loss of coherence is a highly quantum effect, resulting from noncommutation of different terms in the Hamiltonian. We show the diversity of behavior of coherence as effected by the application of the scheme, which is skewed towards gain rather than loss, on a variant of the spin-boson model that does not fulfill the commutation condition.
Object Detection Using Region-Conventional Neural Network (RCNN) and OpenCV

Object detection is used in almost every real-world application such as autonomous traversal, visual system, face detection, and even more. This paper aims at applying object detection technique to assist visually impaired people. It helps visually impaired people to know about the objects around them to enable them to walk free. A prototype has been implemented on a Raspberry PI3 using OpenCV libraries, and satisfactory performance is achieved. In this paper, a detailed review has been carried out on object detection using region-conventional neural network (RCNN)-based learning systems for a real-world application. This paper explores the various process of detecting objects using various object detections methods and walks through detection including a deep neural network for SSD implemented using Caffee model.
MIRACLE: multi-satellite Island image registration using anisotropic coherence locality enhanced nonlinear diffusion and Mahalanobis distance guided marginalization

ABSTRACTFeature in an image plays a crucial role for geometric image registration. The geo-registration problem becomes difficult for scanty feature islands’ images captured by high and medium spatial resolution remote sensing satellites over deep ocean water. The article presents an automatic multi-satellite image registration methodology for scanty feature island scenes, termed as MIRACLE. In data pre-processing stage, the multi-spectral and reference image are enhanced using anisotropic coherence for better localized feature demarking of island regions. The input multi-spectral images are transformed using Principal Component Analysis (PCA) to maximize the variance information for improved feature matching with reference image. Enhanced features are detected and described using nonlinear diffusion filtering, and the matched control points are pruned using Mahalanobis distance guided marginalization optimization technique. The estimated affine parameters are applied to generate multi-satellite co-registered data products. MIRACLE is evaluated with multi-temporal Indian Resourcesat multi-spectral images and NASA-USGS Landsat−8 OLI panchromatic images that span from 5.0-metre spatial resolution to 15.0-metre spatial resolution and cover the Lakshadweep islands in deep ocean. The visual quality assessment indicates that different island regions are aligned at sub-pixel level registration accuracy. The matching accuracy of MIRACLE is quantified for multi-resolution images and is found to have 2.6% improvement in Correct Matching Ratio (CMR) as compared to the state-of-the-art feature based image registration techniques. The average Root Mean Square Error (RMSE) of island regions after precise geometric correction is found to be 0.45 pixel.KEYWORDS: Islandsimage registrationanisotropic coherencenonlinear diffusionMahalanobis distanceMarginalization Optimization AcknowledgementsThe authors are grateful to Dr. Nandini Ray Chaudhury, Space Applications Centre (SAC), ISRO, Ahmedabad, India, for providing the remote sensing datasets for island image registration and the feedback on output co-registered data products. The authors also thank the Director, SAC, ISRO, Ahmedabad, India, for his encouragement and support.Disclosure statementNo potential conflict of interest was reported by the author(s).
IoT Devices Data Management in Supply Chains by Applying BC and ML

Current monitoring systems supply chains reveal deficiencies such as homogeneous traceability of the process, active participation of the actors involved in each stage, timely communication of results, compliance with regulatory aspects or international standards, and attention to consumer-oriented information requirements. Aiming to have a reliable mechanism that guarantees quality, safety and sustainability of food through the whole chain, this paper proposes a framework based on Blockchain (BC) attributes, IoT devices and Machine Learning (ML) techniques to detect unusual patterns in some attributes measured during transportation. The value proposition of this implementation is the alternation between Proof of Work and Proof of Learning that allows a better use of computational resources to support organizational needs relating ML tasks.