Reviewerly/exHarmony huggingface dataset:
Deepsentimodels: A Novel Hybrid Deep Learning Model for an Effective Analysis of Ensembled Sentiments in E-Commerce and S-Commerce Platforms

Social networks and media have gradually grabbed significant time from people’s lives to share and communicate information. Moreover, these social platforms also act as an emotional catalyst for expressing their feeling and views on different products, movies, and even national policies. Thus, it helps to understand people’s opinions and predict their behaviors in social networks, where it helps the entrepreneur improve their products or services. It is considered the most efficient method to understand the customer’s view on their product better. Since entrepreneurs are increasing exponentially, an intelligent system is required to understand people’s views on their products. Also, people use informal language to express their sentiments; it has become a daunting task to extract the sentiments that reflect the people’s attitude and feedback about the product. Therefore, this article suggests a novel hybrid deep learning system, consisting of bi-convolutional networks (B-CNN) and spotted hyena optimized long short term memory (SHOLSTM), for improved understanding of people’s feelings and the construction of intelligent recommendation systems for entrepreneurs. Many experiments have been conducted utilizing various datasets compared to other current hybrid deep learning methods, including long short-term memory (LSTM), convolutional neural networks (CNN), BIGRU, and attention classifiers. Accuracy is achieved as 99.4%, 99% precision, 99.2% recall, 99.2% F1-score is achieved, which suits these algorithms best for implementing the intelligent recommendation systems based on the different ensembled sentiments.
SoK: Human-centered Phishing Susceptibility

Phishing is recognized as a serious threat to organizations and individuals. While there have been significant technical advances in blocking phishing attacks, end-users remain the last line of defence after phishing emails reach their email inboxes. Most of the existing literature on this subject has focused on the technical aspects related to phishing. The factors that cause humans to be susceptible to phishing attacks are still not well-understood. To fill this gap, we reviewed the available literature and systematically categorized the phishing susceptibility variables studied. We classify variables based on their temporal scope, which led us to propose a three-stage Phishing Susceptibility Model (PSM) for explaining how humans are vulnerable to phishing attacks. This model reveals several research gaps that need to be addressed to understand and improve protection against phishing susceptibility. Our review also systematizes existing studies by their sample size and generalizability and further suggests a practical impact assessment of the value of studying variables: Some more easily lead to improvements than others. We believe that this article can provide guidelines for future phishing susceptibility research to improve experiment design and the quality of findings.
OTEC

Encoding and transcoding videos into multiple codecs and representations is a significant challenge that requires seconds or even days on high-performance computers depending on many technical characteristics, such as video complexity or encoding parameters. Cloud computing offering on-demand computing resources optimized to meet the needs of customers and their budgets is a promising technology for accelerating dynamic transcoding workloads. In this work, we propose OTEC, a novel multi-objective optimization method based on the mixed-integer linear programming model to optimize the computing instance selection for transcoding processes. OTEC determines the type and number of cloud and fog resource instances for video encoding and transcoding tasks with optimized computation cost and time. We evaluated OTEC on AWS EC2 and Exoscale instances for various administrator priorities, the number of encoded video segments, and segment transcoding times. The results show that OTEC can achieve appropriate resource selections and satisfy the administrator's priorities in terms of time and cost minimization.
Analyzing Safe Haven, Hedging and Diversifier Characteristics of Heterogeneous Cryptocurrencies against G7 and BRICS Market Indexes

Cryptocurrency markets have experienced large growth in recent years, with an increase in the number and diversity of traded assets. Previous work has addressed the economic properties of Bitcoin with regards to its hedging or diversification properties. However, the surge of many alternatives, applications, and decentralized finance services on a variety of blockchain networks requires a re-examination of those properties, including indexes from outside the big economies and the inclusion of a variety of cryptocurrencies. In this paper, we report the results of studying the most representative cryptocurrency of each consensus mechanism by trading volume, forming a list of twenty-four cryptocurrencies from the 1st of January 2018 to the 30th of September 2022. Using the Baur and McDermott model, we examine hedge, safe haven, and diversifier properties of all assets for all G7 country’s major indexes as well as all BRICS major indexes breaking it down by two attributes: kind of blockchain technology and pre/during COVID health crisis. Results show that both attributes play an important role in the hedge, safe haven, and diversifier properties associated with the asset. Concretely: stablecoins appear to be the only ones to maintain hedge property in most analyzed markets pre- and during-COVID; Bitcoin investment properties shifted after the COVID crisis started; China and Russia stopped being correlated with the cryptocurrency after the COVID crisis hit.
TreeMerge: A Visual Comparative Analysis Method for Food Classification Tree in Pesticide Residue Maximum Limit Standards

Food classification is an important part of food safety standards. In this paper, we propose a novel visual comparative analysis method for food classification trees (FCTs) in pesticide maximum residue limit (MRL) standards, called TreeMerge, to lay the foundation for a comprehensive comparison of pesticide MRL standards. First, a union tree is constructed by combining the two FCTs to be compared. Then, sunburst with an embedded chordal graph (SECG) and overlapping circular treemap (OCT), which are two new visualization solutions designed in this paper, are used to show the similarities and differences in a union tree. SECG can express the hierarchical structure and the similarity between corresponding nodes in the union tree at the same time. OCT uses an improved nested Venn diagram (overlapping circle) to express the attribute values in each layer of the union tree and uses a circle-filling layout algorithm based on the testing circle to improve the readability and space utilization of the view. Finally, a visual analysis system for comparing FCT, named FCTvis, is designed and implemented to support the exploration of the structural difference pattern of food classification in the two MRL standards and the quantity or scale of residue limits in various foods. The effectiveness of TreeMerge was verified by case studies on pesticide MRL standards in the Chinese Mainland and Chinese Hong Kong.
Combined Pseudo-Random Sequence Generator for Cybersecurity

Random and pseudo-random number and bit sequence generators with a uniform distribution law are the most widespread and in demand in the market of pseudo-random generators. Depending on the specific field of application, the requirements for their implementation and the quality of the generator's output sequence change. In this article, we have optimized the structures of the classical additive Fibonacci generator and the modified additive Fibonacci generator when they work together. The ranges of initial settings of structural elements (seed) of these generators have been determined, which guarantee acceptable statistical characteristics of the output pseudo-random sequence, significantly expanding the scope of their possible application, including cybersecurity. When studying the statistical characteristics of the modified additive Fibonacci generator, it was found that they significantly depend on the signal from the output of the logic circuit entering the structure. It is proved that acceptable statistical characteristics of the modified additive Fibonacci generator, and the combined generator realized on its basis, are provided at odd values of the module of the recurrent equation describing the work of such generator. The output signal of the combined generator has acceptable characteristics for a wide range of values of the initial settings for the modified additive Fibonacci generator and the classic additive Fibonacci generator. Regarding the use of information security, it is worth noting the fact that for modern encryption and security programs, generators of random numbers and bit sequences and approaches to their construction are crucial and critical.
Compressed Federated Learning Based on Adaptive Local Differential Privacy

Federated learning (FL) was once considered secure for keeping clients' raw data locally without relaying on a central server. However, the transmitted model weights or gradients still reveal private information, which can be exploited to launch various inference attacks. Moreover, FL based on deep neural networks is prone to the curse of dimensionality. In this paper, we propose a compressed and privacy-preserving FL scheme in DNN architecture by using Compressive sensing and Adaptive local differential privacy (called as CAFL). Specifically, we first compress the local models by using Compressive Sensing (CS), then adaptively perturb the remaining weights according to their different centers of variation ranges in different layers and their own offsets from corresponding range centers by using Local Differential Privacy (LDP), finally reconstruct the global model almost perfectly by using the reconstruction algorithm of CS. Formal security analysis shows that our scheme achieves ϵ-LDP security and introduces zero bias to estimating average weights. Extensive experiments using MINIST and Fashion-MINIST datasets demonstrate that our scheme with minimum compression ratio 0.05 can reduce the number of parameters by 95%, and with a lower privacy budget ϵ = 1 can improve the accuracy by 80% on MINIST and 12.7% on Fashion-MINIST compared with state-of-the-art schemes.
HD2A-Net: A novel dual gated attention network using comprehensive hybrid dilated convolutions for medical image segmentation

The convolutional neural networks (CNNs) have been widely proposed in the medical image analysis tasks, especially in the image segmentations. In recent years, the encoder-decoder structures, such as the U-Net, were rendered. However, the multi-scale information transmission and effective modeling for long-range feature dependencies in these structures were not sufficiently considered. To improve the performance of the existing methods, we propose a novel hybrid dual dilated attention network (HD2A-Net) to conduct the lesion region segmentations. In the proposed network, we innovatively present the comprehensive hybrid dilated convolution (CHDC) module, which facilitates the transmission of the multi-scale information. Based on the CHDC module and the attention mechanisms, we design a novel dual dilated gated attention (DDGA) block to enhance the saliency of related regions from the multi-scale aspect. Besides, a dilated dense (DD) block is designed to expand the receptive fields. The ablation studies were performed to verify our proposed blocks. Besides, the interpretability of the HD2A-Net was analyzed through the visualization of the attention weight maps from the key blocks. Compared to the state-of-the-art methods including CA-Net, DeepLabV3+, and Attention U-Net, the HD2A-Net outperforms significantly, with the metrics of Dice, Average Symmetric Surface Distance (ASSD), and mean Intersection-over-Union (mIoU) reaching 93.16%, 93.63%, and 94.72%, 0.36 pix, 0.69 pix, and 0.52 pix, and 88.03%, 88.67%, and 90.33% on three publicly available medical image datasets: MAEDE-MAFTOUNI (COVID-19 CT), ISIC-2018 (Melanoma Dermoscopy), and Kvasir-SEG (Gastrointestinal Disease Polyp), respectively.
The Unreasonable Effectiveness of the Chaotic Tent Map in Engineering Applications

From decimal expansion of real numbers to complex behaviour in physical, biological and human-made systems, deterministic chaos is ubiquitous. One of the simplest examples of a nonlinear dynamical system that exhibits chaos is the well known 1-dimensional piecewise linear Tent map. The Tent map (and their skewed cousins) are instances of a larger family of maps namely Generalized Luröth Series (GLS) which are studied for their rich number theoretic and ergodic properties. In this work, we discuss the unreasonable effectiveness of the Tent map and their generalizations (GLS maps) in a number of applications in electronics, communication and computer engineering. To list a few of these applications: (a) GLS-coding: a lossless data compression algorithm for i.i.d sources is Shannon optimal and is in fact a generalization of the popular Arithmetic Coding algorithm used in the image compression standard JPEG2000; (b) GLS maps are used as neurons in the recently proposed Neurochaos Learning architecture which delivers state-of-the-art performance in classification tasks; (c) GLS maps are ideal candidates for chaos-based computing since they can simulate XOR, NAND and other gates and for dense storage of information for efficient search and retrieval; (d) Noise-resistant versions of GLS maps are useful for signal multiplexing in the presence of noise and error detection; (e) GLS maps are shown to be useful in a number of cryptographic protocols - for joint compression and encryption and also for generating pseudo-random numbers. The unique properties and rich features of the Tent Map (GLS maps) that enable these wide variety of engineering applications will be investigated. A list of open problems are indicated as well.
FedClamp: An Algorithm for Identification of Anomalous Client in Federated Learning

With the ever-increasing internet of things (IoT) and the rise of edge computing, federated learning (FL) is considered a promising solution for privacy and latency-aware applications. However, the data is highly distributed among several clients, making it challenging to monitor node anomalies caused by malfunctioning devices or any other unforeseen reasons. In this paper, we propose FedClamp, an anomaly detection algorithm based on the hidden Markov model (HMM) in the FL environment. FedClamp identifies the anomalous node and isolates them before aggregation to improve the performance of the global model. FedClamp was tested in a short-term energy forecasting problem using artificial neural networks when the FL environment had five clients. The algorithm uses mean absolute percentage error (MAPE) generated from local models and clusters them in normal and faulted nodes using HMM. The anomalous nodes identified using this algorithm are isolated before aggregation and achieve global model convergence with few communication rounds.
Federated data analytics: A study on linear models

As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge compute resources are exploited to process more of the data locally. This regime of analytics is coined as federated data analytics (FDA). In spite of the recent success stories of FDA, most literature focuses exclusively on deep neural networks. In this work, we take a step back to develop an FDA treatment for one of the most fundamental statistical models: linear regression. Our treatment is built upon hierarchical modeling that allows borrowing strength across multiple groups. To this end, we propose two federated hierarchical model structures that provide a shared representation across devices to facilitate information sharing. Notably, our proposed frameworks are capable of providing uncertainty quantification, variable selection, hypothesis testing and fast adaptation to new unseen data. We validate our methods on a range of real-life applications including condition monitoring for aircraft engines. The results show that our FDA treatment for linear models can serve as a competing benchmark model for future development of federated algorithms.
QR code beautification by instance segmentation (IS-QR)

Due to the popularity of smartphones, cameras can be seen everywhere. QR codes are widely used daily, and their application is becoming more and more diverse, such as for warehouse management, electronic tickets, mobile payment, etc. As COVID-19 rapidly spread worldwide, people were forced to change their payment habits. Contactless systems, such as electronic tickets, became increasingly used to display information and avoid traditional queues. However, the standard QR code comprises black and white squares in monochrome images, which is not visually appealing. Yet, the easiest way to present a theme in a QR code is an image, which is more eye-catching and easier to understand than text. In this study, we devise an IS-QR method to integrate full-color images with QR codes by instance segmentation, using BlendMask to extract image feature regions and take Human Visual System into account. Discrete wavelet transform and contrast sensitivity were used to lessen the impact of reduced readability of QR codes during printing. Representative image visual quality measures, including PSNR, MSE, SSIM, FSIM, and GMSD, were used to measure the experimental results in order to validate the effectiveness of QR code beautification. The subjective quality evaluation is also performed. Finally, the measurement results indicate that the beautified QR codes generated by the method IS-QR designed in this study perform better than other related studies in terms of visualization and beautification.
Quantum bionic advantage on near-term cloud ecosystem

The race to conquer the quantum information domain has accelerated the pace of research and innovation toward developing radical new approaches for quantum-based technologies. Quantum computing, communication, sensing combinatorial optimization, and quantum machine learning are among the most competitive and dynamic research and development areas currently on the verge of unlocking practical new quantum capabilities that will have a profound effect on almost every aspect of our lives. This paper presents the advantages of superconducting qubits and a summary of the various archetypes that show promise for the long-term goal of constructing larger-scale fault-tolerant quantum computers. The paper presents the implementation of the relevant NISQ era algorithms for near-term integrated hybrid cloud-based quantum computing systems, including the advances and implications of these algorithms which make them particularly suitable for quantum machine learning, and combinatorial optimization. In addition, this paper explores the practical realities of both quantum sensing for ultrasensitive measurements and quantum communications to provide unparalleled security, data rates, and efficiency.
A flexible lateral chromatic aberration correction method for a color-encoded fringe projection system

A flexible correction method is proposed for improving the performance of a color-encoded fringe projection system which suffers from lateral chromatic aberration (LCA) effects. The method uses a white plate with discrete markers and orthogonal fringe patterns projected on its surface to establish the relationships between depth information and pixel deviations among three color channels, and then corrects color-encoded fringe patterns affected by LCA with the acquired relationship for three-dimensional (3D) reconstruction. Compared with the methods that use a translation stage to accurately locate the plate to several parallel positions, the proposed method is flexible because the plate can be placed at arbitrary positions and directions in the measuring volume without an extra translation stage. Two experiments, including 3D shape measurement of a white plate and 3D shape measurement of a regular spherical object are carried out to verify the effectiveness and accuracy of the proposed method.
Optimization-assisted personalized event recommendation for event-based social networks

The usage of Online Social Networks (OSN) is promptly increasing in recent days. Particularly, Social Media networks permit individuals to share, communicate, observe and comment on diverse kinds of multimedia content. These phenomena produce a massive quantity of data that shows Big Data features, primarily owing to their large volume higher change rate, and inherent heterogeneity. In this viewpoint, Recommender Systems are established for helping users to discover “what they need within this ocean of information”. Here, this paper intends to design a novel personalized event recommendation approach, which deploys the “multi-criteria decision making (MCDM) approach” for ranking the events. In the adopted model, the preference schemes are built to compute categorical, geographical, temporal and social influences. Moreover, a personalized weight is approximated for every criterion (i.e., all influences). However, the major work deals with the estimation of personalized weight, and for this, new automated weight estimation is introduced via Weight oriented Grey Wolf Optimization (W-GWO) algorithm. Thereby, the dominance intensity measures is computed by exploiting the personalized criterion's weight of every criterion and the alternatives are given ranks depending on approximated dominance intensity measures for recommending the top-ranked events. Eventually, the supremacy of the adopted method is validated over other existing approaches in terms of various measures.
Nonlocal B-spline representation of tensor decomposition for hyperspectral image inpainting

Hyperspectral image (HSI) completion is a fundamental problem in image processing and remote sensing. Typical methods, either perform suboptimally due to lack of appropriate priors or suffer from excessive complexity since the combination of too many constraints. Recently, nonlocal low-rank tensor approximation-based (NLTA) HSI completion methods are widespreadly investigated taking full advantage of global spectral correlation and non-local self-similarity. Common NLTA methods are mainly based on tensor train (TT) and tensor ring (TR) decompositions, but their representation of tensors is inadequate and inflexible. In this paper, a fully-connected tensor network (FCTN) decomposition is introduced for HSI completion exploring both the global spectral correlation and the spatial structure. FCTN approximates an N-order tensor as a sequence of smaller sized N-order tensors, which has outstanding capability to explore the essential invariance for transposition and also to improve intrinsic correlations among factors. Furthermore, motivated by the transformation based methods on HSI reconstruction and after deeply comparing its superiority over other techniques, we employ an iterative B-spline representation to construct a transformed tensor for better revelation of prior information. The proposed model can be efficiently solved using proximal alternating minimization (PAM) approach. Extensive experiments results demonstrate that our method achieves state-of-the-art performance both in quantitative metrics and visual effect evaluations.