Reviewerly/exHarmony huggingface dataset:
The Role of Governance, Leadership in Public Sector Organizations: A Case Study in the UAE

Leadership, governance, and management are among the most prominent components of an organization. These components contribute to adopting new trends and dealing effectively with the challenges by introducing, designing, and practically implementing the organizational strategies to improve productivity. This study also highlighted the role of leadership, governance, and management in accelerating organizational productivity. The researcher also proposed a potential mediation of organizational strategies in improving productivity. To empirically investigate the proposed relationship between study variables, the researcher gathered data from n = 230 individuals currently working in the UAE-based private telecom companies. Findings indicated that leadership, governance, and management are significantly playing their role to improve productivity. However, results also showed a mediating role of Organizational Strategies as indirectly affecting the relationship between leadership, governance, management, and improved productivity and strengthening these relationships at maximum level. Thus, the researcher concludes that governance, management, and leadership are the regulating agents of an organization. Here, the role of strategies is of greater significance as they help an organization accelerate its growth, serve the interests of organizational members, reduce the risks, increase productivity, and help an organization to achieve competitive advantages.
Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis

In today's digitalized era, the world wide web services are a vital aspect of each individual's daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users' personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks. Artificial intelligence (AI)-based techniques such as machine learning (ML) and deep learning (DL) have proven to be infallible in detecting phishing attacks. Nevertheless, sequential ML can be time intensive and not highly efficient in real-time detection. It can also be incapable of handling vast amounts of data. However, utilizing parallel computing techniques in ML can help build precise, robust, and effective models for detecting phishing attacks with less computation time. Therefore, in this proposed study, we utilized various multiprocessing and multithreading techniques in Python to train ML and DL models. The dataset used comprised 54 K records for training and 12 K for testing. Five experiments were carried out, the first one based on sequential execution followed by the next four based on parallel execution techniques (threading using Python parallel backend, threading using Python parallel backend and number of jobs, threading manually, and multiprocessing using Python parallel backend). Four models, namely, random forest (RF), naïve bayes (NB), convolutional neural network (CNN), and long short-term memory (LSTM) were deployed to carry out the experiments. Overall, the experiments yielded excellent results and speedup. Lastly, to consolidate, a comprehensive comparative analysis was performed.
Mathematical theory of Bayesian statistics for unknown information source

In statistical inference, uncertainty is unknown and all models are wrong. That is to say, a person who makes a statistical model and a prior distribution is simultaneously aware that both are fictional candidates. To study such cases, statistical measures have been constructed, such as cross validation, information criteria and marginal likelihood; however, their mathematical properties have not yet been completely clarified when statistical models are under- or over-parametrized. We introduce a place of mathematical theory of Bayesian statistics for unknown uncertainty, which clarifies general properties of cross validation, information criteria and marginal likelihood, even if an unknown data-generating process is unrealizable by a model or even if the posterior distribution cannot be approximated by any normal distribution. Hence it gives a helpful standpoint for a person who cannot believe in any specific model and prior. This paper consists of three parts. The first is a new result, whereas the second and third are well-known previous results with new experiments. We show there exists a more precise estimator of the generalization loss than leave-one-out cross validation, there exists a more accurate approximation of marginal likelihood than Bayesian information criterion, and the optimal hyperparameters for generalization loss and marginal likelihood are different. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
A systematic literature review on the effectiveness of deepfake detection techniques

With technological advances, the generation of deepfake material is now within reach of those operating consumer-grade hardware. As a result, much research has been undertaken on deepfake detection techniques. This work has analysed and measured the performance of various detection techniques using multiple metrics and discussed the effectiveness of these deepfake detection techniques. This has been undertaken by examining and analysing the current state of deepfake detection techniques. Unlike other existing surveys, this work produced a Systematic Literature Review (SLR) on research conducted from the beginning of 2021 to August 2022. This SLR includes tabulated data containing details of the techniques used and the accuracy of those techniques, performance metrics provided in each study, summaries of the datasets used, and challenges and future trends. This SLR has been undertaken with a focus on using a mixed methods approach. This SLR has determined that deep learning (DL) has surpassed machine learning (ML) as the preferred deepfake detection model. However, ML is still a primary focus method in medical imagery. It was also discovered that traditional artificial neural networks are no longer effective and require additional modules to produce ensembled and multi-attentional architectures.
Optimal parameters selection of back propagation algorithm in the feedforward neural network

The back propagation (BP) is one of the most widely used algorithms in the feedforward neural network (FNN), but selecting the non-optimal weights and thresholds may induce the slow convergence and local optimization. In this work, we propose an improved BP algorithm in which the loss function is constructed based on a small scale of data to seek the optimal weights and thresholds. The iteration process of the improved BP algorithm can be accelerated and the solutions will not be trapped into the local optimum. Solutions corresponding to the increasing scales of data are convergent which demonstrates the convergence of the parameters selection. Numerical simulations of elasticity and graphic reconstruction and repair confirm that the improved BP algorithm can achieve much faster convergence and higher efficiency than the conventional BP algorithm. Moreover, different initial values leading to the same optimal parameters indicates that the improved algorithm can achieve the global optimization during the learning process.
FusionRCNN: LiDAR-Camera Fusion for Two-Stage 3D Object Detection

Accurate and reliable perception systems are essential for autonomous driving and robotics. To achieve this, 3D object detection with multi-sensors is necessary. Existing 3D detectors have significantly improved accuracy by adopting a two-stage paradigm that relies solely on LiDAR point clouds for 3D proposal refinement. However, the sparsity of point clouds, particularly for faraway points, makes it difficult for the LiDAR-only refinement module to recognize and locate objects accurately. To address this issue, we propose a novel multi-modality two-stage approach called FusionRCNN. This approach effectively and efficiently fuses point clouds and camera images in the Regions of Interest (RoI). The FusionRCNN adaptively integrates both sparse geometry information from LiDAR and dense texture information from the camera in a unified attention mechanism. Specifically, FusionRCNN first utilizes RoIPooling to obtain an image set with a unified size and gets the point set by sampling raw points within proposals in the RoI extraction step. Then, it leverages an intra-modality self-attention to enhance the domain-specific features, followed by a well-designed cross-attention to fuse the information from two modalities. FusionRCNN is fundamentally plug-and-play and supports different one-stage methods with almost no architectural changes. Extensive experiments on KITTI and Waymo benchmarks demonstrate that our method significantly boosts the performances of popular detectors. Remarkably, FusionRCNN improves the strong SECOND baseline by 6.14% mAP on Waymo and outperforms competing two-stage approaches.
Xenophobia and Hate Speech towards Refugees on Social Media: Reinforcing Causes, Negative Effects, Defense and Response Mechanisms against That Speech

This study aims to recognize the possible role of social media in forming xenophobia and hate speech against Syrian refugees and to understand the reinforcing causes and negative effects of that speech on the refugees, as well as the refugees’ response mechanisms against that speech. A mixed approach was used in this study. In addition to a questionnaire tool that was used to collect data, focus group discussions were conducted to support the obtained data. The study used a statistically representative sample of Syrian refugees who live in Turkey and Jordan. The participants see the negative representation of their image via social media as leading to hatred towards them. The findings showed that there are some causes that reinforce xenophobic speech, such as otherization and demonization of refugees, by the negative representation and spreading of fake news on social media, which are published continuously. Moreover, gatekeepers have a role in promoting negative representation by approving xenophobic speech transmission without filtering or ethical control. The results also showed that the negative representation containing hate speech contributes to more negative effects on the refugees, such as psychological effects, as a sense of hatred towards refugees is created in the host country, and the effect of changing the feelings of the public from empathy to compassion fatigue concluding to hatred towards those refugees. Furthermore, the results showed that the Syrian refugees follow different defense mechanisms against xenophobia and hate speech against them published and circulated through social media platforms. The participants were divided into two main categories; the first one is those who prefer replying, not being silent, and defending the refugees, and the second category is those who prefer keeping silent. The first category of refugees stated that they do not accept keeping silent and they prefer to reply using logical, emotional, or defensive and offensive ways to bad comments against Syrian refugees in general. While the second category claimed that either they do not have time to reply or they sometimes agree that it was only a reaction to the behavior of some individual refugees. Some of them explained that they are afraid of being subjected to more bad comments and negative reactions against them. When they encounter aggressive comments and see that such views represent the majority, they prefer to keep silent to avoid being attacked. This is understood within the framework of the spiral of silence theory, where refugees hide their opinions, views, and preferences when they think that they fall within a minority group. They fear social isolation through social media.
Review on Blockchain Technology : Architecture, Characteristics, Benefits, Algorithms, Challenges and Applications

Today, blockchain technology is playing a vital role in the professional world as one of the most important discoveries and creative developments. Blockchain technology is continuously advancing and revolutionizing our society. An overview of blockchain technology is presented in this paper. The blockchain is a distributed ledger technology that allows different transactions and operations to be recorded in a chain of blocks without requiring a third party. A growing number of businesses and industrial communities are exploring blockchain technology as a technological option. In the last decade, blockchain technology has completely changed the paradigm of computer applications. Blockchain was created with the vision of creating an immutable, confidential, open-source, decentralized P2P network located on a decentralized network to facilitate data sharing. An overview of blockchain applications in different areas is presented. We provide a timely summary of blockchain research for individuals and organizations interested in this field. This paper presents an overview of blockchain technology, listing all the key characteristics, benefits, and features which make blockchain technology both superior and unique. We also discuss popular consensus protocols and taxonomy for blockchain systems.
Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features

Decoding human visual neural representations is a challenging task with great scientific significance in revealing vision-processing mechanisms and developing brain-like intelligent machines. Most existing methods are difficult to generalize to novel categories that have no corresponding neural data for training. The two main reasons are 1) the under-exploitation of the multimodal semantic knowledge underlying the neural data and 2) the small number of paired (stimuli-responses) training data. To overcome these limitations, this paper presents a generic neural decoding method called BraVL that uses multimodal learning of brain-visual-linguistic features. We focus on modeling the relationships between brain, visual and linguistic features via multimodal deep generative models. Specifically, we leverage the mixture-of-product-of-experts formulation to infer a latent code that enables a coherent joint generation of all three modalities. To learn a more consistent joint representation and improve the data efficiency in the case of limited brain activity data, we exploit both intra- and inter-modality mutual information maximization regularization terms. In particular, our BraVL model can be trained under various semi-supervised scenarios to incorporate the visual and textual features obtained from the extra categories. Finally, we construct three trimodal matching datasets, and the extensive experiments lead to some interesting conclusions and cognitive insights: 1) decoding novel visual categories from human brain activity is practically possible with good accuracy; 2) decoding models using the combination of visual and linguistic features perform much better than those using either of them alone; 3) visual perception may be accompanied by linguistic influences to represent the semantics of visual stimuli.
Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics

Covid-19 pandemic created a global shift in the way how consumers purchase. Restrictions to movements of individuals and commodities created a big challenge on day today life. Due to isolation, social media usage has increased substantially, and these platforms created significant impact carrying news and sentiments instantaneously. These sentiments impacted the purchase behavior of consumers and online retailers witnessed variations in their sales. Retailers used various customer behavior prediction models such as Recommendation systems to influence consumers and increasing their sales. Due to Covid-19 pandemic, these models may not perform the same way due to changes in consumer behavior. By integrating consumer sentiments from online social media platform as another feature in the prediction machine learning models such as recommendation systems, retailers can understand consumer behavior better and create Recommendations appropriately. This provides the consumers with appropriate choice of products in essential and non-essential categories based on pandemic condition restrictions. This also helps retailers to plan their operations and inventory appropriately.
Adversarial patch attacks against aerial imagery object detectors

Although Deep Neural Networks (DNNs)-based object detectors are widely used in various fields, especially on aerial imagery object detections, it has been observed that a small elaborately designed patch attached to the images can mislead the DNNs-based detectors into producing erroneous output. However, the target detectors being attacked are quite simple, and the attack efficiency is relatively low in previous works, making it not practicable in real scenarios. To address these limitations, a new adversarial patch attack algorithm is proposed in this paper. Firstly, we designed a novel loss function using the intermediate outputs of the models rather than the model's final outputs interpreted by the detection head to optimize adversarial patches. The experiments conducted on the DOTA, RSOD, and NWPU VHR-10 datasets demonstrate that our method can significantly degrade the performance of the detectors. Secondly, we conducted intensive experiments to investigate the impact of different outputs of the detection model on generating adversarial patches, demonstrating the class score is not as effective as the objectness score. Thirdly, we comprehensively analyzed the attack transferability across different aerial imagery datasets, verifying that the patches generated on one dataset are also effective in attacking another. Moreover, we proposed ensemble training to boost the attack's transferability across models. Our work alarms the application of DNNs-based object detectors in aerial imagery.
Haze removal for single image: A comprehensive review

Image dehazing is always a hot topic in the field of computer vision since haze has significant impact on the imaging quality of camera. Therefore, many image dehazing methods have been proposed for the past decades. To help researchers who are new to this field quickly figure out the development history as well as the current status of image dehazing, this review analyzes some representative dehazing methods, evaluates their advantages and disadvantages, and most importantly, points out the best dehazing method from different viewpoints. A large quantity of experiments show that AECR-Net can be generally considered to be the best dehazing algorithm and Tarel method can be regarded as the best real-time dehazing method. Besides, the mainstream benchmark, metrics, challenges and opportunities for image dehazing are also discussed in this review.
Blockchain Supply Chain Management and Supply Chain Sustainability

The majority of the environmental and climatic issues emerge due to the massive production and consumption of products that ultimately compromise human and wild lives and the future interests of generations. Sustainable supply chain management emerges as a strategic way to address these misfortunes, the implementation of which is a bit challenging but adaptable. Therefore, this chapter addresses the implementation of blockchain supply chains to attain supply chain sustainability. The chapter starts with the introduction, emergence, and growth of blockchain technology and also covers the definitions of blockchain technology. Besides this, it also provides an in-depth understanding of blockchain-driven supply chains and how these (BCSCM) can be used to attain sustainable supply chains. It also examines the basic characteristics and types of blockchain-driven supply chains and the role of blockchain supply chains in present scenarios and the context of COVID-19. This chapter also discusses how the integration of blockchain supply chains can be used to achieve sustainable supply chain performance. It also examines the enablers and barriers in the implementation of blockchain supply chains in achieving sustainable supply chains. The types and advantages of blockchain supply chains are also part of this chapter. Moreover, the role of blockchain supply chains in forecasting future production and reducing waste in production and distribution is also discussed. Finally, future aspects like blockchain supply chain opportunities, security, traceability, and visibility of blockchain supply chains were also evaluated. This study implies that SCM managers can achieve supply chain sustainability through the incorporation of blockchain-driven supply chains in their business operations. Further, the managers can make strategic decisions while keeping the barriers and drives discussed in this chapter. Lastly, the conclusion and future research perspectives were provided.
A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.
Learning cross-domain semantic-visual relationships for transductive zero-shot learning

Zero-Shot Learning (ZSL) learns models for recognizing new classes. One of the main challenges in ZSL is the domain discrepancy caused by the category inconsistency between training and testing data. Domain adaptation is the most intuitive way to address this challenge. However, existing domain adaptation techniques cannot be directly applied into ZSL due to the disjoint label space between source and target domains. This work proposes the Transferrable Semantic-Visual Relation (TSVR) approach towards transductive ZSL. TSVR redefines image recognition as predicting the similarity/dissimilarity labels for semantic-visual fusions consisting of class attributes and visual features. After the above transformation, the source and target domains can have the same label space, which hence enables to quantify domain discrepancy. For the redefined problem, the number of similar semantic-visual pairs is significantly smaller than that of dissimilar ones. To this end, we further propose to use Domain-Specific Batch Normalization to align the domain discrepancy.