Reviewerly/exHarmony huggingface dataset:
The Prediction of Consumer Behavior from Social Media Activities

Consumer behavior variants are evolving by utilizing advanced packing models. These models can make consumer behavior detection considerably problematic. New techniques that are superior to customary models to be utilized to efficiently observe consumer behaviors. Machine learning models are no longer efficient in identifying complex consumer behavior variants. Deep learning models can be a capable solution for detecting all consumer behavior variants. In this paper, we are proposing a new deep learning model to classify consumer behavior variants using an ensemble architecture. The new model incorporates two pretrained learning algorithms in an optimized fashion. This model has four main phases, namely, data gathering, deep neural modeling, model training, and deep learning model evaluation. The ensemble model is tested on Facemg BIG-D15 and TwitD databases. The experiment results depict that the ensemble model can efficiently classify consumer behavior with high precision that outperforms recent models in the literature. The ensemble model achieved 98.78% accuracy on the Facemg database, which is higher than most machine learning consumer behavior detection models by more than 8%.
Enhancement of Underwater Images by CNN-Based Color Balance and Dehazing

Convolutional neural networks (CNNs) are employed to achieve the color balance and dehazing of degraded underwater images. In the module of color balance, an underwater generative adversarial network (UGAN) is constructed. The mapping relationship between underwater images with color deviation and clean underwater images is learned. In the module of clarity improvement, an all-in-one dehazing model is proposed in which a comprehensive index is introduced and estimated by deep CNN. The third module to enhance underwater images adopts an adaptive contrast improvement method by fusing global and local histogram information. Combined with several underwater image datasets, the proposed enhancement method based on the three modules is evaluated, both by subjective visual effects and quantitative evaluation metrics. To demonstrate the advantages of the proposed method, several commonly used underwater image enhancement algorithms are compared. The comparison results indicate that the proposed method gains better enhancement effects for underwater images in different scenes than the other enhancement algorithms, since it can significantly diminish the color deviation, blur, and low contrast in degraded underwater images.
Quantum Teleportation and Dense Coding in Multiple Bosonic Reservoirs

The effect of a reservoir on quantum communication depends on its spectral density. The efficiency of quantum teleportation and dense coding is explored when each one of the channel qubits is coupled simultaneously to multiple bosonic reservoirs. It is shown that the non-Markovianity triggered by increasing the reservoir number can induce revivals of quantum advantages of the two protocols after their disappearance. However, the backflow of information to the system that signifies non-Markovianity does not always induce immediate revivals of the quantum advantages. There may be a delayed effect for some initial states, and only as the backflow of information accumulates to a certain extent can the revivals of quantum advantages be triggered.
Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback

Evolving fuzzy neural networks are models capable of solving complex problems in a wide variety of contexts. In general, the quality of the data evaluated by a model has a direct impact on the quality of the results. Some procedures can generate uncertainty during data collection, which can be identified by experts to choose more suitable forms of model training. This paper proposes the integration of expert input on labeling uncertainty into evolving fuzzy neural classifiers (EFNC) in an approach called EFNC-U. Uncertainty is considered in class label input provided by experts, who may not be entirely confident in their labeling or who may have limited experience with the application scenario for which the data is processed. Further, we aimed to create highly interpretable fuzzy classification rules to gain a better understanding of the process and thus to enable the user to elicit new knowledge from the model. To prove our technique, we performed binary pattern classification tests within two application scenarios, cyber invasion and fraud detection in auctions. By explicitly considering class label uncertainty in the update process of the EFNC-U, improved accuracy trend lines were achieved compared to fully (and blindly) updating the classifiers with uncertain data. Integration of (simulated) labeling uncertainty smaller than 20% led to similar accuracy trends as using the original streams (unaffected by uncertainty). This demonstrates the robustness of our approach up to this uncertainty level. Finally, interpretable rules were elicited for a particular application (auction fraud identification) with reduced (and thus readable) antecedent lengths and with certainty values in the consequent class labels. Additionally, an average expected uncertainty of the rules were elicited based on the uncertainty levels in those samples which formed the corresponding rules.
Learning Local Attention With Guidance Map for Pose Robust Facial Expression Recognition

Facial expression recognition (FER) is an extremely challenging task under unconstrained conditions. Especially, variant head poses degrade the performance dramatically due to the large variations in appearance of facial expressions. To address this problem, we propose a local attention network (LAN), which adaptively captures the important facial regions according to pose variations. The LAN emphasizes on more attentive regions while suppressing the regions not differentiated between classes. To find out attentive regions, we propose a simple yet efficient coarse-level attention guidance map annotation method in an unsupervised manner. The guidance map includes attention values for regions based on whether features are deformed by facial poses. Further, the attentive regional features obtained by our LAN and original global features are combined for pose-invariant FER. We validate our method on a controlled multiview dataset, KDEF, three popular in- the-wild datasets, RAF-DB, FERPlus, and AffectNet, and their subsets that contain images under pose variation conditions. Extensive experiments show that our LAN largely improves the performance of FER under pose variations. Our method also performs favorably against the previous methods.
Detection of Violent Behavior Using Neural Networks and Pose Estimation

Regarding safety and security, felonies and crimes with physical violence remain a significant problem worldwide. Some solutions for pedestrian safety are guards, police car patrolling, sensors, and security cameras. Nonetheless, these methods react only when the crime takes place. In the worst cases, the damage may be irreversible when it has already occurred. Therefore, numerous methods based on Artificial Intelligence have been proposed to solve this problem. Many approaches to detect violent behavior and action recognition rely on 3D convolutional neural networks (3D-CNNs), spatio-temporal models, long short-term memory networks, pose estimation, among other implementations. However, these approaches work in a limited fashion and have not been adapted to uncontrolled environments. Thus, a significant contribution from this work is the development of an innovative solution model capable of detecting violent behavior. This approach focuses on pedestrian detection, tracking, pose estimation, and neural networks to predict pedestrian behavior in video frames. Our proposal uses a time window frame to extract joint angles, given by the pose estimation algorithm, as features for classifying behavior. Another significant contribution of this work is the creation of a new database, Kranok-NV, with a total of 3,683 normal and violent videos. This database was used to train and test the solution model. For the evaluation, we designed a protocol using 10-fold cross-validation. We obtained an accuracy slightly above 98% on the Kranok-NV database with the implemented solution model. Although the proposed solution model detects violent and normal behavior, it can be easily extended to classify other types of behavior.
Towards Robust Person Re-Identification by Defending Against Universal Attackers

Recent studies show that deep person re-identification (re-ID) models are vulnerable to adversarial examples, so it is critical to improving the robustness of re-ID models against attacks. To achieve this goal, we explore the strengths and weaknesses of existing re-ID models, i.e., designing learning-based attacks and training robust models by defending against the learned attacks. The contributions of this paper are three-fold: First, we build a holistic attack-defense framework to study the relationship between the attack and defense for person re-ID. Second, we introduce a combinatorial adversarial attack that is adaptive to unseen domains and unseen model types. It consists of distortions in pixel and color space (i.e., mimicking camera shifts). Third, we propose a novel virtual-guided meta-learning algorithm for our attack-defense system. We leverage a virtual dataset to conduct experiments under our meta-learning framework, which can explore the cross-domain constraints for enhancing the generalization of the attack and the robustness of the re-ID model. Comprehensive experiments on three large-scale re-ID benchmarks demonstrate that: 1) Our combinatorial attack is effective and highly universal in cross-model and cross-dataset scenarios; 2) Our meta-learning algorithm can be readily applied to different attack and defense approaches, which can reach consistent improvement; 3) The defense model trained on the learning-to-learn framework is robust to recent SOTA attacks that are not even used during training.
Comparative Analysis of Various Load Balancing Techniques in Cloud Environment

Today, cloud computing has emerged as new technology and a business model. Cloud computing is spreading everywhere on the planet because of its simplicity and easy-to-use model. Growing cloud computing services offer great opportunities for sponsors to induce the most effective and the best prices, which poses new challenges in choosing the simplest service for an outsized group. Cloud computing uses a spread of computer resources to facilitate large-scale operations. Therefore, selecting the correct node to try to do the duty can improve the performance of an outsized cloud computing site. It takes time for consumers to assemble the required information and analyze all service providers to create a call.. Load balancing is the technique used to distribute the loads among various systems to optimize performance. Load balancing ensures that nodes are neither overloaded nor underloaded. Cloud Analyst could be a tool that helps developers to simulate large-scale Cloud applications to understand the functionality of such programs under various deployment settings. The cloud analyst simulator is used to run the experiments.
Full-field phase shifting and stress quantification using a polarization camera

• A novel operation method to achieve 10-step phase shifting is proposed by means of a polarization camera. • A Full-field stress separation strategy has been proposed. • The comparison and examples indicate that the proposed method is simple to apply and accurate in stress determination. Phase shifting technique is widely used in determination of photoelastic parameters. Efficient full-field stress quantification remains a challenge due to cumbersome rotation of optical elements in the polarizer and the complex stress separation strategy. By deriving the mathematical expressions with the Stokes parameters with the use of a polarization camera, a simple operation method achieving phase shifting for accurately determining photoelastic parameters has been proposed. A stress separation strategy for full-field stress calculation has also been developed. Experiments are presented on a circular plate subjected to diametric compression and a special-shaped sample subjected to compressive loads. Experimental results indicate that the proposed methods enable a simple and accurate full-field stress determination for photoelastic samples.
Deep CNN based Image Compression with Redundancy Minimization via Attention Guidance

Almost all compression algorithms try to minimize the one or other type of visual redundancy present in the image. Compression becomes challenging while considering the preservation of contextual information and other information. Without considering the contextual information, some of the unwanted features are also learned by the learning-based methods, which leads to the wastage of computational resources. Motivated by this fact, we propose an attention mechanism guided multi-size kernel convolution network-based image compression-decompression algorithm, which focuses on important (local and global) features that are needed for better reconstruction. Among various feature maps obtained after convolution at any stage, channel attention focuses on "what" is meaningful, and spatial attention focuses on "where" the important features are present in the entire feature map. Secondly, we propose to use a perceptual loss function for the task of image compression, which is a combination of contextual, style, and ℓ-2 loss functions. The proposed network and training it with perceptual loss function helped achieve significant improvements when tested with various datasets like CLIC 2019, Tecnick, Kodak, FDDB, ECSSD, and HKU-IS datasets. When assessed on CLIC 2019 challenging dataset, the MS-SSIM and PSNR of the proposed algorithm outperformed JPEG, JPEG2000, and BPG by approximately up to 49.6%, 34.61%, 20.69%, and 10.79%, 1.32%, 3.36% respectively, at low-bit rates (around 0.1 bpp). We further investigated the effectiveness of the proposed algorithm on the cartoon images and found them to be superior to other algorithms. Lastly, as the cartoon images are significantly less available for experimentation using deep learning algorithms, we propose a cartoon image dataset, namely CARTAGE.
Wristband Fall Detection System Using Deep Learning

Fall is one of the significant problems threatening older people. Ambient Assisted Living (AAL) is equipment and process for supporting older people&#x2019;s independent and safe living. AAL includes elderly fall detection. The life of older people will be safe if rescue comes to help in the safety period after fall. Then fall detection is needed for life safety. The development of microcontrollers is very fast with tiny in size, high calculation performance, and low power consumption. In addition, the software for machine learning is extensive interest and developed. This study developed a fall detection system by applying an accelerometer as a sensor and a deep learning algorithm as a fall pattern recognition. We used an ESP32 microcontroller to determine the pattern of the user&#x2019;s activity. If ESP32 detects the fall, it will send a fall alert to the provider via Wi-Fi with the LINE application. The fall detection model of this study has an accuracy of 96.55% with the testing data.
Oriented-YOLOv5: A Real-time Oriented Detector Based on YOLOv5

Current state-of-the-art oriented detectors have time-consuming feature extraction backbones, oriented proposals generation methods or additional special branches. These tricks increase the computational cost of oriented detectors and prevent them from some practical applications. YOLOv5 is one of the best real-time (inference time&#x2A7D;33.3ms, or FPS&#x2A7E;30) detectors in the field of general target detection that can be applied in various real tasks. However, YOLOv5 does not output the angular prediction that is crucial to reflect attitudes and shapes of the targets. We propose Oriented-YOLOv5 that can output angular prediction of rotated target and achieve real-time detection in aerial images. Specifically, we integrate Circular Smooth Label (CSL) into YOLOv5 (v5.0), so it inherits both the fast and lightweight features of YOLOv5 and the high performance of CSL for angle detection. Experimental results indicate that Oriented-YOLOv5s achieves an accuracy of 68.24% mAP and an efficiency of 11.8ms with aerial images of 1024&#x00D7;1024 input size. It can be further improved (68.86% mAP and 10.8ms) using Conv instead of Focus. We also integrate CSL into YOLOv5m and YOLOv5l, and they achieve improved accuracies (70.03% mAP and 71.2% mAP, respectively) and retain real-time inference speed. Overall, Oriented-YOLOv5 is well suited for detection tasks that have vertical view and require angular prediction with highly real-time and limited memory, so it can be used for applications deployed to satellites, UAVs, or robotic arms. Source code: https://github.com/Leesanjin/Oriented-YOLOv5
A Cross-Chain Protocol based on Quantum Teleportation for Underlying Architecture of Metaverse

In this era of technological change, metaverse has attracted extensive attention from all over the world as next-generation internet. Blockchain technology plays an indispensable role in realizing the independent digital identity and economic system which are the significant attributes of the metaverse. To achieve the cross-universe mechanism and reduce network resource consumption, this paper proposes a cross-chain protocol for the underlying architecture of metaverse that utilizes an identity information chain to guarantee the separate digital identity of users and decrease the data interaction between varied consortium blockchains. However, along with the rapid development of quantum computing, the classical cryptosystems which commonly count on mathematical complexity will not be reliable and secure. The current post-quantum cryptography technologies that increase the time complexity of algorithm will lead to quite low efficiency and high resource overhead while applying to blockchain structures. But quantum cryptography is capable of lifting both security and efficiency of blockchain. As a result, the scheme put forward in the article adopts quantum teleportation to implement secure communication. In addition, to assure the atomicity of cross-chain transactions that cross-chain asset exchanges succeed or fail simultaneously, a quantum time lock that improves the previous studies and combines quantum cryptography technology is proposed.
A Resource Efficient Software-Hardware Co-Design of Lattice-Based Homomorphic Encryption Scheme on the FPGA

Lattice-based homomorphic encryption schemes provide strong resistance against quantum and classical computer-based adversary security attacks. In this article, we present a software-hardware co-design of two partially homomorphic encryption (PHE) schemes employing an ARM-System on Chip (ARM-SoC) and an field programmable gate array (FPGA). This provides necessary acceleration to PHE methods in the ecosystem mentioned above. The first PHE scheme is designed for generic homomorphic encryption, while the second scheme is aimed at resource optimized lightweight IoT-driven applications. For seamless assimilation, a robust and reliable low latency data transfer protocol is developed between the FPGA-based accelerator IP and ARM-SoC host system. The proposed PHE schemes are realized using Verilog hardware description language on multiple FPGA platforms. The proposed lightweight scheme is <inline-formula><tex-math notation="LaTeX">$52.71\times$</tex-math></inline-formula> more resource-efficient than the pipelined BGV RLWE-based method. It exhibits <inline-formula><tex-math notation="LaTeX">$1.43\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$1.29\times$</tex-math></inline-formula> better throughput than non-pipelined and pipelined realizations of the BGV RLWE-based scheme. The proposed hardware accelerators realized on FPGA platforms having lesser clock speed and consuming lower resources showcase significant speedup compared to their software implementations making our proposed method an efficient alternative to enhance security in edge-enabled IoT devices.
The legality of cryptocurrency from an Islamic perspective: a research note

Purpose Islamic scholars are not unanimous on the legality of cryptocurrency. Muslims are confused about the permissibility of cryptocurrency. Thus, this study aims to evaluate the cryptocurrency from Shariah’s perspective. Design/methodology/approach This study is qualitative and uses a deductive research approach. It drives conceptual and logical deduction from Qur'an and Hadith. Findings The cryptocurrency is an intangible currency without any precious metal or commodity backing. It does not possess any intrinsic value, and also its prices are highly volatile. On these grounds, cryptocurrency does not qualify as money in Islam. Social implications Cryptocurrency is not compatible with Islamic principles. It is not legal tender and is not backed by any government. Its prices are highly volatile, which opens the door for speculation. Muslims should avoid investing in it. Originality/value This study fulfills an identified need to evaluate cryptocurrency as money from Shariah’s perspective.
Zero click attacks – a new cyber threat for the e-banking sector

Purpose The purpose of the study is to confirm the fact that in informations security, the human factor was considered as a key carrier of the majority of attacks that an information system faces. Banking and other financial services are always top among the most attractive targets for cyber attackers. Blind phishing or spear phishing is still one of the major contributors to all malicious activities in the e-banking sector. All the counter mechanisms, therefore, revolve around the concept of how security-aware the customers are. To fool these mechanisms, attacks are becoming smarter and are searching for methods where the human involvement is diminishing to zero. Zero click attacks are one big leap that attackers are taking that removes the requirement of human involvement in initiating attacks and are moving toward an era of unassisted attacks. Even though the standard procedure and protocols are built into the banking system, they fail to detect this attack resulting in significant losses. Design/methodology/approach This paper follows a conceptual review of the upcoming concept in security and its implication in e-banking sector. The methodology adopted in this paper uses review papers, articles and white papers to conclude a theoretical model. A detailed analysis of unassisted attacks is considered from 2010 onwards till 2022. Findings This research deliberates on the methodologies of zero click attacks and gives a detailed analysis of attack vectors and their exploits. This research also identifies the likely attacks on e-banking that these vulnerabilities can trigger. Originality/value The key contribution is toward the early detection of zero click attacks, suggesting countermeasure, reducing the likelihood of these attacks and the financial impact.
Adam SPGD algorithm in freeform surface in-process interferometry

The adaptive interferometer has been recently proposed to realize the metrology of unknown freeform surfaces with several restructured algorithms for feedback control. The adaptive moment estimation (Adam) stochastic parallel gradient descent (SPGD) algorithm is employed in this paper for fringes release. The proposed algorithm makes considerable progress in relieving conflict of the convergence rate, speed, and parameters intervention. Simulations and experiments show its 37% time saving and 99% convergence rate, with arbitrarily configured parameter increment, compared with the SPGD algorithm. It would have great potential in in-process tests in freeform surface fabrication or large-volume testing.