Reviewerly/exHarmony huggingface dataset:
Exploring Customer Awareness towards Their Cyber Security in the Kingdom of Saudi Arabia: A Study in the Era of Banking Digital Transformation

The annual rate of cybersecurity breaches has risen in the last few years, exposing millions of records in some cases. The average data breach cost in 2021 was a massive $4.24 million. This study examines customer awareness and satisfaction with cybersecurity in the context of the digital transformation of banking in Saudi Arabia. The study is empirical and based on the data collected from 355 banking customers in Saudi Arabia. Three significant aspects of cybersecurity, including cyberattacks, phishing, and hacking, have been analyzed through various dimensions. Customer satisfaction with bank cybersecurity assistance and their expectations of technical support and services on cybersecurity has also been studied. ANOVA and bivariate regression analysis are used to study the impact of cyberattack, phishing, hacking, cybersecurity assistance, and expectations on cybersecurity’s technical awareness on customer satisfaction. The results show that digital transformation has boosted the banking sector, and users benefit from online services. However, an increase in the awareness level of customers on cyberattack, phishing, and hacking activities will influence customers’ satisfaction with digital transactions. The results also revealed that customers need more satisfaction on security level aspects from the bank’s side, and banks should provide regular training programs to safeguard customers from cyberattacks. If banks prepare more secure cybersecurity management, their long-term sustainability goals could be easily achieved.
Identification and classification of transportation disaster tweets using improved bidirectional encoder representations from transformers

Social Media today has become the most relevant and affordable platform to express one's views in real-time. The #Endsars protest in Nigeria and the COVID-19 pandemic have proven how important and reliant both government agencies and individuals are on social media. This research uses tweets collected from Twitter API to identify and classify transportation disasters in Nigeria. Information such as the user, location, and time of the tweet makes identification and classification of transportation disasters available in real-time. Bidirectional Encoder Representations from Transformers (BERT) uses a transformer that includes two separate mechanisms, a decoder that produces a prediction for the task and an encoder that reads the text input. It learns contextual relations between words (sub-words) in a text. This research applied BERT with a combination of AdamW optimizers. AdamW is an improved version of stochastic gradient descent that computes an adaptive learning rate for each parameter. Our proposed model produces an accuracy of 82%. It was concluded that our approach outperformed the existing algorithm: BERT having an accuracy of 64%.
Transparency by Design for Blockchain-Based Supply Chains

Supply chains are experiencing significant advances in digital technologies, particularly those associated with industry 4.0. An example of such technology is blockchain. Blockchain is a disruptive technology characterised by anonymity and identity, consensus mechanism, decentralisation, overall performance and expectancy, reliability of systems and data, and information transparency. Blockchain offers supply chain opportunities to strengthen end-to-end visibility and traceability, leading to enhanced levels of transparency. Supply chains are increasingly exploring blockchain technology and transparency, with many focusing on system development. This paper explores transparency in blockchain-based supply chains to understand the principles underlining its design. A systematic review of literature is used, accompanied by data-driven analysis. The results present the principles within a framework for transparency by design in blockchain-based supply chains. Limitations and areas for future work are also presented.
Human-centered neural reasoning for subjective content processing: Hate speech, emotions, and humor

Some tasks in content processing, e.g., natural language processing (NLP), like hate or offensive speech and emotional or funny text detection, are subjective by nature. Each human may perceive some content individually. The existing reasoning methods commonly rely on agreed output values, the same for all recipients. We propose fundamentally different – personalized solutions applicable to any subjective NLP task. Our five new deep learning models take into account not only the textual content but also the opinions and beliefs of a given person. They differ in their approaches to learning Human Bias (HuBi) and fusion with content (text) representation. The experiments were carried out on 14 tasks related to offensive, emotional, and humorous texts. Our personalized HuBi methods radically outperformed the generalized ones for all NLP problems. Personalization also has a greater impact on reasoning quality than commonly explored pre-trained and fine-tuned language models. We discovered a high correlation between human bias calculated using our dedicated formula and that learned by the model. Multi-task solutions achieved better outcomes than single-task architectures. Human and word embeddings also provided additional insights.
Deepfakes Generation and Detection: A Short Survey

Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes refer to face multimedia content, which has been digitally altered or synthetically created using deep neural networks. The paper first outlines the readily available face editing apps and the vulnerability (or performance degradation) of face recognition systems under various face manipulations. Next, this survey presents an overview of the techniques and works that have been carried out in recent years for deepfake and face manipulations. Especially, four kinds of deepfake or face manipulations are reviewed, i.e., identity swap, face reenactment, attribute manipulation, and entire face synthesis. For each category, deepfake or face manipulation generation methods as well as those manipulation detection methods are detailed. Despite significant progress based on traditional and advanced computer vision, artificial intelligence, and physics, there is still a huge arms race surging up between attackers/offenders/adversaries (i.e., DeepFake generation methods) and defenders (i.e., DeepFake detection methods). Thus, open challenges and potential research directions are also discussed. This paper is expected to aid the readers in comprehending deepfake generation and detection mechanisms, together with open issues and future directions.
DML-PL: Deep metric learning based pseudo-labeling framework for class imbalanced semi-supervised learning

Traditional class imbalanced learning algorithms require training data to be labeled, whereas semi-supervised learning algorithms assume that the class distribution is balanced. However, class imbalance and insufficient labeled data problems often coexist in practical real-world applications. Currently, most existing class-imbalanced semi-supervised learning methods tackle these two problems separately, resulting in the trained model biased towards majority classes that have more data samples. In this study, we propose a deep metric learning based pseudo-labeling (DML-PL) framework that tackles both problems simultaneously for class-imbalanced semi-supervised learning. The proposed DML-PL framework comprises three modules: Deep Metric Learning, Pseudo-Labeling and Network Fine-tuning. An iterative self-training strategy is used to train the model multiple times. For each time of training, Deep Metric Learning trains a deep metric network to learn compact feature representations of labeled and unlabeled data. Pseudo-Labeling then generates reliable pseudo-labels for unlabeled data through labeled data clustering with nearest neighbors selection. Finally, Network Fine-tuning fine-tunes the deep metric network to generate better pseudo-labels in the subsequent training. The training ends when all the unlabeled data are pseudo-labeled. The proposed framework achieved state-of-the-art performance on the long-tailed CIFAR-10, CIFAR-100, and ImageNet127 benchmark datasets compared with baseline models.
Maximizing AUC to learn weighted naive Bayes for imbalanced data classification

Imbalanced data classification is a challenging problem frequently encountered in many real-world applications. Traditional classification algorithms are generally designed to maximize overall accuracy; therefore, their effectiveness tends to be impeded by imbalanced data. Similar to other traditional classifiers, naive Bayes (NB) sometimes fails at predicting minority instances owing to its sensitivity to class distribution. To cope with this challenge, we proposed RankOptAUC NB (RNB), a novel attribute weighting method for the NB. In the proposed method, learning a weighted NB classifier was formulated as a nonlinear optimization problem with the objective of maximizing the area under the ROC (AUC). The optimization formulation enabled the RNB method to select important variables by simply adding a regularization term to the objective function. We also provided theoretical evidence that, based on the AUC metric, the proposed method improved the performance of a weighted NB classifier. The results of numerical experiments conducted using 30 real-world datasets proved that the proposed scheme successfully determined the optimal attribute weights for imbalanced data classification.
Comparative Performance Analysis of PV Module Positions in a Solar PV Array Under Partial Shading Conditions

The irradiation intensity is the primary catalyst for energy production in photovoltaic (PV) systems. Nevertheless, it alleviates partial shading (PS) by inhibiting power production, primarily in habitable metropolitan areas, owing to surrounding objects or high soiling rates, particularly in dry and semi-arid climates. Hence, the PV module’s location, orientation, and tilt angle significantly affect its performance and shorten its durability. Thus, this study undertakes a benchmark analysis of the two distinct panel positions’ performance using PSIM and MATLAB software simulations and an actual practical test. A visual inspection of a solar power plant in Morocco’s Green Energy Park is the origin of the survey concept; it observes that the soil density consistently covers certain PV panel spots. In this context, the solar energy data of an intelligent home deployed in the earlier-mentioned research platform has been deployed to emulate the performance of future buildings challenged with various obstacles. Therefore, the roof-mounted PV system satisfied the home’s energy demands. In the first scenario, the PV module is in portrait mode. In the second case, they are in landscape orientation. The findings of this investigation demonstrate that standard PV panels produce more power when arranged in a landscape configuration than in a portrait configuration, exhibiting a discrepancy of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1010 Wh$ </tex-math></inline-formula> for a modest PV system. Finally, this study suggests appropriate orientation and design recommendations for standard and advanced solar modules, mainly those deployed in arid and heavily populated urban regions featuring severe shading constraints.
MetaMining: Mining in the Metaverse

Mines are one of the important energy sources in the world. Due to mining areas are often affected by adverse weather and environmental conditions (sand, dust, extreme cold, heavy snow, etc.), the efficiency and security of mining are low. As a new, efficient, prospective mode, Metaverse has been studied and successfully applied in various industries. However, there is still no research about its usage in mines. In this article, we apply Metaverse to mining and propose MetaMining. We present its definition and analyze the functions it could perform. Furthermore, we propose its development phases and architecture. Specifically, MetaMining is a mode, which aims to achieve high-efficiency, high-security mining in the physical world through the interaction between the physical world and the virtual world. Its development phases involve three steps: 1) digital twins; 2) digital natives; and 3) surreality. Its architecture consists of three components: 1) human world; 2) virtual mining system; and 3) physical mining system. In addition, we analyze key technologies and possible challenges in the construction of MetaMining.
Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation

Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important to develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, to enhance the level of usability and promote their deployment. With this purpose an automatic tri-axial accelerometer-based system for postural recognition has been developed, useful in detecting potential inappropriate behavioral habits for the elderly. Systems in the literature and on the market for this type of analysis mostly use personal computers with high computing resources, which are not easily portable and have high power consumption. To overcome these limitations, a real-time posture recognition Machine Learning algorithm was developed and optimized that could perform highly on platforms with low computational capacity and power consumption. The software was integrated and tested on two low-cost embedded platform (Raspberry Pi 4 and Odroid N2+). The experimentation stage was performed on various Machine Learning pre-trained classifiers using data of seven elderly users. The preliminary results showed an activity classification accuracy of about 98% for the four analyzed postures (Standing, Sitting, Bending, and Lying down), with similar accuracy and a computational load as the state-of-the-art classifiers running on personal computers.
Interaction between giant atoms in a one-dimensional structured environment

Giant atoms -- quantum emitters that couple to light at multiple discrete points -- are emerging as a new paradigm in quantum optics thanks to their many promising properties, such as decoherence-free interaction. While most previous work has considered giant atoms coupled to open continuous waveguides or a single giant atom coupled to a structured bath, here we study the interaction between two giant atoms mediated by a structured waveguide, e.g., a photonic crystal waveguide. This environment is characterized by a finite energy band and a band gap, which affect atomic dynamics beyond the Markovian regime. Here we show that, inside the band, decoherence-free interaction is possible for different atom-cavity detunings, but is degraded from the continuous-waveguide case by time delay and other non-Markovian effects. Outside the band, where atoms interact through the overlap of bound states, we find that giant atoms can interact more strongly and over longer distances than small atoms for some parameters -- for instance, when restricting the maximum coupling strength achievable per coupling point. The results presented here may find applications in quantum simulation and quantum gate implementation.
A blockchain-based framework for trusted quality data sharing towards zero-defect manufacturing

There is a current wave of a new generation of digital solutions based on intelligent systems, hybrid digital twins and AI-driven optimization tools to assure quality in smart factories. Such digital solutions heavily depend on quality-related information within the supply chain business ecosystem to drive zero-waste value chains. To empower zero-waste value chain strategies with meaningful, reliable, and trustful data, there must be a solution for end-to-end industrial data traceability, trust, and security across multiple process chains or even inter-organizational supply chains. In this paper, we first present Product, Process, and Data quality services to drive zero-waste value chain strategies. Following this, we present the Trusted Framework (TF), which is a key enabler for the secure and effective sharing of quality-related information within the supply chain business ecosystem, and thus for quality optimization actions towards zero-defect manufacturing. The TF specification includes the data model and format of the Process/Product/Data (PPD) Quality Hallmark, the OpenAPI exposed to factory system and a comprehensive Identity Management layer, for secure horizontal- and vertical quality data integration. The PPD hallmark and the TF already address some of the industrial needs to have a trusted approach to share quality data between the different stakeholders of the production chain to empower zero-waste value chain strategies.
Optimal site selection for floating photovoltaic systems based on Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): a case study

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A new design of parity preserving reversible Vedic multiplier targeting emerging quantum circuits

Abstract Reversible logic is used increasingly to design digital circuits with lower power consumption. The parity preserving (PP) property contributes to detect permanent and transient faults in reversible circuits by comparing the input and output parity. Multiplication is also considered one of the primary operations in both digital and analog circuits due to its wide applications in digital signal processing and computer arithmetic operations. Accordingly, Vedic mathematics, as a set of techniques sutras, has become popular and is extensively used to solve mathematical problems more efficiently and faster. This work proposes three PP reversible blocks, N 1 , N 2 , and N 3 , which are used to develop a novel effective 2‐bit PP reversible Vedic multiplier and 4‐bit ripples carry adders (RCAs). Moreover, 2‐bit Vedic multiplier and RCA are used to develop the 4‐bit PP reversible Vedic multiplier. The proposed designs outperform the most relevant state‐of‐the‐art structures in terms of garbage output (GO), constant input (CI), gate count (GC), and quantum cost (QC). Average savings of 22.37%, 35.44%, 35.44%, and 34.76%, and 17.76%, 26.60%, 24.52%, and 27.27% respectively, are observed for two‐bit and four‐bit PP reversible Vedic multipliers in terms of QC, GO, CI and GC as compared to previous works.
aeroBERT-NER: Named-Entity Recognition for Aerospace Requirements Engineering using BERT

Organizations predominantly use natural language (NL) for requirements elicitation, development, and management since NL is easy to understand and use by stakeholders with varying levels of experience, unlike specialized model-based languages, which require training to use. However, NL requirements entail ambiguities associated with language, a tedious and error- prone manual examination process, difficulties verifying requirement completeness, and failure to recognize and use technical terms effectively. Modern complex systems warrant an integrated and holistic approach to their development to capture their numerous interrelationships. This need has spurred a trend toward model-centric approaches to engineering as compared to traditional document-based methods. Transitioning to Model-Based Systems Engineering (MBSE) requires standardized, machine-readable requirements, but the conversion of NL requirements into models is hindered by the ambiguities and inconsistencies inherent in NL. The field of Natural Language Processing (NLP) has shown promise in facilitating the conversion of NL requirements into a format that enables their standardization and use in a model-based environment. Named-Entities Recognition (NER), in particular, can be leveraged to help address the ambiguities in NL requirements. To be of value, however, a language model needs to be fine-tuned to recognize aerospace-specific terms. To that end, we developed an annotated aerospace corpus and fine-tuned the BERT language model on the corpus to create aeroBERT-NER: a new model for identifying named entities (NEs) in aerospace requirements. A comparison between aeroBERT-NER and BERTBASE-NER showed the improved performance of aeroBERT-NER in identifying NEs within a set of aerospace requirements. The NEs identified by the model were used to create a glossary to facilitate consistent use of technical terms and language in aerospace requirements.
Refined edge detection with cascaded and high-resolution convolutional network

Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with traditional methods like Sobel or Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results with erroneous edges. In this paper, we attempted to overcome these challenges for refined edge detection using a cascaded and high-resolution network named (CHRNet). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, sub-blocks are connected at every stage with the output of the previous layer. Also, after each layer, we use batch normalization layer with an active affine parameter as an erosion operation for the homogeneous region in the image. The proposed method is evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.The code is available at: https://github.com/elharroussomar/chrnet/
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition

Contrastive learning is an emerging and important self-supervised learning paradigm that has been successfully applied to sensor-based human activity recognition (HAR) because it can achieve competitive performance relative to supervised learning.Contrastive learning methods generally involve instance discrimination, which means that the instances are regarded as negatives of each other, and thus their representations are pulled away from each other during the training process.However, instance discrimination could cause overclustering, meaning that the representations of instances from the same class could be overly separated.To alleviate this overclustering phenomenon, we propose a new contrastive learning framework to select negatives by clustering in HAR, which is named ClusterCLHAR.First, ClusterCLHAR clusters the instance representations, and for each instance, only those from different clusters are regarded as negatives.Second, a new contrastive loss function is proposed to mask the same-cluster instances from the negative pairs.We evaluate ClusterCLHAR on three popular benchmark datasets, USC-HAD, MotionSense, and UCI-HAR, using the mean F1-score as an evaluation metric for downstream tasks.The experimental results show that ClusterCLHAR outperforms all the state-ofthe-art methods applied to HAR in self-supervised learning and semi-supervised learning.