Reviewerly/exHarmony huggingface dataset:
Influence of emojis on user engagement in brand-related user generated content

Emojis are increasingly adopted in various platforms, such as text messages, social media, or blogs, as part of other digital communications. Recently, emojis have been used in brand-related user-generated content (UGC) as a strategic communication tool to promote positive outcomes. In spite of their increasing importance, little is known about the impact of incorporating emojis in brand-related UGC on consumer reactions. In particular, few research studies have investigated the joint effect of emojis with texts, which is the most important context where emojis function, and the contextual conditions that affect the influence of emojis on consumer reactions. To fill this research gap, we study brand-related UGC, focusing on the effect of emojis and the contextual conditions (e.g., texts). Using large-scale brand-related social media posts from Instagram, we find that, overall, the presence of emojis is positively associated with consumer engagement. Economically, the presence of emojis increases the average number of likes by 72% and the average number of comments by 70% in comparison to posts without any emojis. The interaction effects between emojis and texts reveal that emotional emojis have a positive and significant relationship with consumer engagement only when the texts in brand-related UGC are being skewed toward positive sentiments. However, informational emojis are negatively related with consumer engagement in the similar context. Additionally, the investigations regarding the contextual conditions show that using more emotional emojis has a positive influence on consumer engagement in commercial posts but a negative influence in general posts.
Automated speech based evaluation of mild cognitive impairment and Alzheimer’s disease detection using with deep belief network model

Early detection of Moderate Cognitive Impairment (MCI) and Alzheimer's disease (AD) is critical for increasing survival rates. Speech feature extraction is used in prior MCI and AD detection algorithms during neuropsychological assessments by medical specialists. The study's goal is to create an MCI and AD detection model using Automatic Speech Recognition (ASR) and a DL model. The suggested approach largely employs the Gaussian Mixture Model (GMM) for ASR in the patient's spontaneous speech. Furthermore, the Deep Belief Network (DBN) model is used to extract feature vectors from identified voice data. Finally, the SoftMax (SM) classifier is used to detect the presence of MCI and AD disorders in the used speech signals. A series of simulations were run to assess the superior performance of the GMM-DBN (Gaussian Mixture Model-Deep Belief Network model). Effectively describes the distribution of data observations as a weighted average of parameterized Gaussian distributions. The testing results indicated the GMM-DBN model's superior performance, with maximum accuracy of 90.28% and 86.76% on the binary and multiple class classifications, DN respectively. The GMM-DBN methodology has been successful in the classification of multiple classes, as evidenced by its F1-score reaching a maximum of 90.19% and its accuracy reaching 90.28%.
Semi-Supervised Federated Heterogeneous Transfer Learning

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine learning models with distributed data stored in different silos without exposing sensitive information. Different from most existing FL approaches requiring data from different parties share either the same feature space or sample ID space, federated transfer learning (FTL), which is a recently proposed FL concept, is designed for situations where data from different parties differ not only in samples but also in feature space. However, like most traditional FL approaches, FTL methods also suffer from issues caused by insufficiency of overlapping data. In this paper, we propose a novel FTL framework referred to as Semi-Supervised Federated Heterogeneous Transfer Learning (SFHTL) to leverage on the unlabeled non-overlapping samples to reduce model overfitting as a result of insufficient overlapping training samples in FL scenarios. Unlike existing FTL approaches, SFHTL makes use of non-overlapping samples from all parties to expand the training set for each party to improve local model performance. Through extensive experimental evaluation based on real-world datasets, we demonstrate significant advantages of SFHTL over state-of-the-art approaches.
Blockchain and Deep Learning Empowered Secure Data Sharing Framework for Softwarized UAVs

Softwarized Unmanned Aerial Vehicles (UAVs) use network programmability concept of Software-Defined Network (SDN) to separate the hardware control layer from the data layer via OpenFlow protocols. The softwarized UAV enable ubiquitous connection, as well as a flexible, cost-effective, and improved method for upgrading all network services without shutting down the entire system. However, the connectivity of UAVs with OpenFlow switches and their heavy reliance on unsecured communication protocols makes the entire network vulnerable. This is a critical concern, particularly in combat surveillance, where eavesdropping, adding, changing, or deleting messages during communications between deployed UAVs and SDN controller is a possible threat. To mitigate the aforementioned issues, this paper presents a novel secure data sharing framework for softwarized UAV environments that incorporates blockchain and Deep Learning (DL). First we present a blockchain-based technique to reg-ister, verify and thereafter validate the communication entities in softwarized UAV environment using smart contract-based Proof-of-Authentication (PoA) consensus mechanism. Additionally, a new deep neural network architecture-based flow analyzer is designed to detect illegitimate transactions. The latter combines a Stacked Contractive Sparse AutoEncoder with Attention-based Long Short-term Memory Neural Network (SCSAE-ALSTM) to improve intrusion detection process. The effectiveness of our framework over several standard baseline methodologies is demonstrated by security analysis and experimental findings.
Innovative Cryptocurrency Trade Websites’ Marketing Strategy Refinement, via Digital Behavior

Nowadays, the cryptocurrency market is thriving, through the rise in cryptocurrency trading, opening the way for cryptocurrency trading websites&#x2019; optimization. Optimization of customer satisfaction is a vital part of cryptocurrency trade organizations&#x2019; digital marketing problems. It is vital to keep digital advertisement costs low while driving more traffic to a website. This study aims to define a digital marketing strategy for cryptocurrency trading websites by utilizing digital behavior metrics. Web analytics data were gathered from 10 world-leading cryptocurrency trade websites over 80 days. Statistical analysis of cryptocurrency trade web analytics, Fuzzy Cognitive Mapping modeling, and Agent-Based Model development have been deployed. Enhancement of cryptocurrency trade digital engagement levels can boost organizations&#x2019; SEO and SEM strategy campaigns. Outputs of the study provide a handful of insights regarding cryptocurrency trading websites&#x2019; digital promotion strategy optimization and the parameters of digital behavior mostly connected with websites&#x2019; digital marketing costs and traffic. Cryptocurrency trade organizations should utilize both organic and paid campaigns, observe regularly their website KPIs connected with visitors&#x2019; behavior and enhance their website users&#x2019; experience, by increasing their engagement.
Robust Nonlinear Control for the Fully Actuated Hexa-Rotor: Theory and Experiments

This letter addresses a fully-actuated Hexa-rotor’s control under exogenous position and attitude disturbances. For that aim, we propose a nonlinear and robust control based on a backstepping technique for position dynamics and a geometric control for attitude dynamics. We demonstrate that the closed-loop system is globally exponentially stable. To validate the reached stability, we present simulation results using MATLAB/Simulink and software in the loop simulations using the well-known PX4 firmware in a realistic scenario on a virtual environment in Gazebo. Finally, to ultimately demonstrate the effectiveness of our algorithm, we implement it on an own-developed fully-actuated Hexa-rotor platform. Our contributions can be summarized as 1) the design of a robust controller for the fully-actuated Hexa-rotor not presented in the literature, 2) the publication of the controller’s code for the PX4 autopilot software, and 3) drone’s construction and presentation of real flight experiments.
A Tractable Probabilistic Approach to Analyze Sybil Attacks in Sharding-Based Blockchain Protocols

Blockchain like Bitcoin and Ethereum suffer from scalability issues. Sharding is one of the most promising and leading solutions to scale blockchain. The basic idea behind sharding is to divide the blockchain network into multiple committees, where each processing a separate set of transactions, rather than the entire network processes all transactions. In this paper, we propose a probabilistic approach to analyze the security of sharding-based blockchain protocols. Based on this approach, we investigate the threat of Sybil attacks in these protocols. The key contribution of our paper is a tractable probabilistic approach to accurately compute the failure probability that at least one committee fails and ultimately compute the probability of a successful attack. To show the effectiveness of our approach, we conduct a numerical and comparative analysis of the proposed approach with existing approaches.
Cyber-Attack on P2P Energy Transaction Between Connected Electric Vehicles: A False Data Injection Detection Based Machine Learning Model

When cybersecurity is neglected, any network system loses its efficiency, reliability, and resilience. With the huge integration of the Information, Communication and Technology capabilities, the Connected Electric Vehicle (CEV) as a transportation form in cities is becoming more and more efficient and able to reply to citizen and environmental expectations which improve the quality of citizens&#x2019; life. However, this CEV technological improvement increases the CEV vulnerabilities to cyber-attacks resulting to serious risks for citizens. Thus, they can intensify their negative impact on societies and cause unexpected physical damage and economic losses. This paper targets the cybersecurity issues for CEVs in parking lots where a peer-to-peer(P2P) energy transaction system based on blockchain, and smart contract scheme is launched. A False Data Injection Attack (FDIA) on the electricity price and power signal is proposed and a Machine Learning/SVM classification protocol is used to detect and extract the right values. Simulation results are conducted to prove the effectiveness of this proposed model.
Color Image Encryption Using 2D Sine-Cosine Coupling Map

In this paper, a new two-dimensional sine-cosine coupling chaos map(2D-SCCM) is proposed. The performance of the proposed two-dimensional chaotic system is analysed by using trajectory distribution maps, Lyapunov exponents, sample entropy and sequence sensitivity, etc. The results show that the 2D-SCCM has better randomness and ergodicity, as well as a wider hyperchaotic range than the existing partial two-dimensional chaos maps. To verify its application in practice, a 2D-SCCM-based color image encryption algorithm is proposed. First, the plain image is used to combine with hash function to generate the key. Then, we construct S-Boxes using the random sequence generated by 2D-SCCM combined with Arnold map. Finally, a color image encryption algorithm is proposed by using the constructed S-Boxes and the chaotic map combined with hash function. Experimental simulations and security tests show that the proposed encryption algorithm has high encryption efficiency and strong security, and can effectively protect images from various attacks.
Memory-Modulated Transformer Network for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) aims at matching face images across different domains. It is challenging due to the severe domain discrepancies and overfitting caused by small training datasets. Some researchers apply a &#x201C;recognition via generation&#x201D; strategy and propose to solve the problem by translating images from a given domain into the visual domain. However, in many HFR tasks such as near-infrablack HFR, there is no paiblack data, which makes it an unsupervised generation. Pose variations, background differences, and many other factors present challenges. Moreover, the generated results lack diversity since many previous works regard this image translation as a &#x201C;one-to-one&#x201D; generation task. Considering the information deficiency in the input images, we propose to formulate this image translation process as a &#x201C;one-to-many&#x201D; generation problem. Specifically, we introduce reference images to guide the generation process. We propose a memory module to explore the prototypical style patterns of the reference domain. After self-supervised updating, the memory items are attentively aggregated to represent the style information. Moreover, to subtly fuse the contents of input images with the style of reference images, we propose a novel style transformer module. Specifically, we crop the encoded input and reference feature maps into patches, and use the style transformer to establish long-range dependencies between the input and reference patches. Thus, the style of every input patch is transferblack based on those of the most relevant reference patches. Extensive experiments on multiple datasets for various HFR tasks, including NIR-VIS, thermal-VIS, sketch-photo, and gray-RGB, are conducted. The robustness and effectiveness of the proposed MMTN are demonstrated both quantitatively and qualitatively.
A Systematic Literature Review of Quantum Computing for Routing Problems

Quantum Computing is drawing a significant attention from the current scientific community. The potential advantages offered by this revolutionary paradigm has led to an upsurge of scientific production in different fields such as economics, industry, or logistics. The main purpose of this paper is to collect, organize and systematically examine the literature published so far on the application of Quantum Computing to routing problems. To do this, we embrace the well-established procedure named as Systematic Literature Review. Specifically, we provide a unified, self-contained, and end-to-end review of 18 years of research (from 2004 to 2021) in the intersection of Quantum Computing and routing problems through the analysis of 53 different papers. Several interesting conclusions have been drawn from this analysis, which has been formulated to give a comprehensive summary of the current state of the art by providing answers related to the most recurrent type of study (practical or theoretical), preferred solving approaches (dedicated or hybrid), detected open challenges or most used Quantum Computing device, among others.
Improved Reptile Search Optimization Algorithm Using Chaotic Map and Simulated Annealing for Feature Selection in Medical Field

The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles’ encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets.
Sentiment Analysis of COVID-19 Tweets Using Adaptive Neuro-Fuzzy Inference System Models

In today’s digital era, Twitter’s data has been the focus point among researchers as it provides specific data and in a wide variety of fields. Furthermore, Twitter’s daily usage has surged throughout the coronavirus disease (Covid-19) period, presenting a unique opportunity to analyze the content and sentiment of covid-19 tweets. In this paper, a new approach is proposed for the automatic sentiment classification of Covid-19 tweets using the Adaptive Neuro-Fuzzy Inference System (ANFIS) models. The entire process includes data collection, pre-processing, word embedding, sentiment analysis, and classification. Many experiments were accomplished to prove the validity and efficiency of the approach using datasets Covid-19 tweets and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes. Our experimental results indicate that fuzzy deep learning achieves the best accuracy (i.e. 0.916) with word embeddings.
A Mobile Robot Path Planning Algorithm Based on Improved A* Algorithm and Dynamic Window Approach

The traditional A* algorithm has several problems in practical applications, such as many path turning points, redundant nodes, and long running time.it is sometimes impossible to plan the theoretical optimal route.To solve the above problem, this paper presents an optimized A* algorithm, the adaptive adjustment step algorithm and the three-time Bezier curve are used to solve the problems of many turning points, large turning angles, and long running time in the search path.Moreover, aiming at the path planning problem of mobile robots facing dynamic obstacle interference in complex environments, an algorithm that integrates the improved A* algorithm with the dynamic window method is proposed, which not only solves the shortcomings of the A* algorithm in which the dynamic obstacles cannot be avoided, but also prevents the mobile robot from falling into local optimization.The results show that the fusion algorithm of the improved A* algorithm and the dynamic window method with the traditional A* algorithm reduces the number of turns by 50% and the path length by 3.62% compared with the original algorithm.In the same environment, compared with the traditional algorithm, the hybrid algorithm in this paper reduces the average time consumption by 10.27%, the number of path inflection points by 57.14%, and the accuracy is higher than 33.33%, which is more effective in complex dynamic environments.
Lux

Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, visually exploring data in dataframes remains tedious, requiring substantial programming effort for visualization and mental effort to determine what analysis to perform next. We propose Lux, an always-on framework for accelerating visual insight discovery in dataframe workflows. When users print a dataframe in their notebooks, Lux recommends visualizations to provide a quick overview of the patterns and trends and suggests promising analysis directions. Lux features a high-level language for generating visualizations on demand to encourage rapid visual experimentation with data. We demonstrate that through the use of a careful design and three system optimizations, Lux adds no more than two seconds of overhead on top of pandas for over 98% of datasets in the UCI repository. We evaluate Lux in terms of usability via interviews with early adopters, finding that Lux helps fulfill the needs of data scientists for visualization support within their dataframe workflows. Lux has already been embraced by data science practitioners, with over 3.1k stars on Github.
CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis

In 2020, according to the publications of both the Global Cancer Observatory (GCO) and the World Health Organization (WHO), breast cancer (BC) represents one of the highest prevalent cancers in women worldwide. Almost 47% of the world’s 100,000 people are diagnosed with breast cancer, among females. Moreover, BC prevails among 38.8% of Egyptian women having cancer. Current deep learning developments have shown the common usage of deep convolutional neural networks (CNNs) for analyzing medical images. Unlike the randomly initialized ones, pre-trained natural image database (ImageNet)-based CNN models may become successfully fine-tuned to obtain improved findings. To conduct the automatic detection of BC by the CBIS-DDSM dataset, a CNN model, namely CoroNet, is proposed. It relies on the Xception architecture, which has been pre-trained on the ImageNet dataset and has been fully trained on whole-image BC according to mammograms. The convolutional design method is used in this paper, since it performs better than the other methods. On the prepared dataset, CoroNet was trained and tested. Experiments show that in a four-class classification, it may attain an overall accuracy of 94.92% (benign mass vs. malignant mass) and (benign calcification vs. malignant calcification). CoroNet has a classification accuracy of 88.67% for the two-class cases (calcifications and masses). The paper concluded that there are promising outcomes that could be improved because more training data are available.