Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based License Plate Number Recognition for Smart Cities

: Smart city-aspiring urban areas should have a number of neces-sary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforce-ment, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates. The current research paper presents an effective Deep Learning (DL)-based VLPNR called DL-VLPNR model to identify and recognize the alphanumeric characters present in license plate. The proposed model involves two main stages namely, license plate detection and Tesseract-based character recognition. The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model. Then, the characters in the detected number plate are extracted using Tesseract Optical Character Recognition (OCR) model. The performance of DL-VLPNR model was tested in this paper using two benchmark databases, and the experimental outcome established the superior performance of the model compared to other methods.
Predicting place attachment through selfie tourism, memorable tourism experience and hedonic well-being

Purpose Selfie tourism is a fast-growing phenomenon. Given the convenience of photo-snapping and photo-sharing on social media, selfie tourism is seen as an emerging trend among travelers. This phenomenon gave consequence toward travelers’ behavior, especially on how it can affect their memorable tourism experience and place attachment. This paper aims to examine the relationship between selfie tourism, memorable tourism experience, hedonic well-being and travelers’ place attachment. Design/methodology/approach A self-administered survey method is used. This research was carried out during the COVID-19 pandemic, consequently, the data were collected via Google Form (online). The respondents are tourists who have visited various tourism destinations in West Sumatera, Indonesia. To test the hypotheses of this study, questionnaires are distributed to 450 respondents. The proposed model was tested using structural equation modeling. Findings The results of this study shows that selfie tourism has a positive and significant relationship toward both memorable tourism experience and hedonic well-being. In addition, memorable tourism experience and hedonic well-being also have a significant relationship with place attachment. Research limitations/implications The limitation of this study lies in the limited reference to the relationship between selfie tourism and memorable tourism experience and hedonic well-being, because this is the first study to examine the relationship of these variables. This study also has not tested the direct relationship between selfie tourism and place attachment and has not considered eudaemonic well-being as an antecedent of place attachment. Further research will discuss the direct effect of selfie tourism and eudaemonic experience on place attachment, as well as the moderating effect of memorable tourism and hedonic well-being. Practical implications This study shows the benefits obtained by tourism managers in preparing selfie facilities, as well as the ability to create meaningful experiences. It provides tourism practitioners with an understanding that tourist place attachment can be improved by both memorable tourism experience and hedonic well-being. Furthermore, both memorable tourism experience and hedonic well-being can be affected by selfie tourism. This understanding can enhance the tourism providers’ strategy to improve services that fit the characteristics of today’s tourists. Therefore, selfie tourism, memorable tourism experience and hedonic well-being can support tourism sustainability, especially in creating place attachments. Originality/value To the authors’ knowledge, this is one of the first studies that investigated the relationship between selfie tourism, memorable tourism experience and place attachment. From a market-specific context, this is also the first study that investigated the antecedents of place attachment on West Sumatera’s tourism.
Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models

Significant attention has recently been paid to deep learning as a method for improved catchment modeling. Compared with process‐based models, deep learning is often criticized for its lack of interpretability. One solution is to combine a process‐based hydrological model with a residual error model based on deep learning to give full scope to their respective advantages. In classical residual error models, Bayesian inference via Markov chain Monte Carlo (MCMC) is commonly used to provide an estimation of the uncertainty. However, deep neural networks tend to have excessively large numbers of parameters, making MCMC an unsuitable approach. Here, we introduce an alternative to Bayesian MCMC sampling called stochastic variational inference (SVI) which has recently been developed for Bayesian deep learning in Natural Language Processing. We implement SVI in a Long Short‐Term Memory (LSTM) network and construct residual error models in process‐based hydrological models. This approach is examined in the contrasting geographical and climatic characteristics of two catchments from China, the Tangnaihai catchment and the Shiquan catchment. Compared with the Bayesian linear regression model, the Bayesian LSTM provides better uncertainty estimates. Specifically, the proposed method improves the Continuous Ranked Probability Score (CRPS) by over 10% in both two catchments. In the Tangnaihai catchment, it provides more than 10% narrower uncertainty intervals in terms of Sharpness with slightly superior Reliability. In the Shiquan catchment, it provides comparable uncertainty intervals with better Reliability. Further, our study highlights the scalability of SVI to high‐dimensional parameter spaces in hydrological applications (e.g., distributed hydrological models, groundwater models).
Association between pregnancy intention and smoking or alcohol consumption in the preconception and pregnancy periods: A systematic review and meta-analysis.

AIMS To investigate the association between pregnancy intention and smoking or alcohol consumption in preconception and pregnancy periods. BACKGROUND Suboptimal lifestyle such as smoking and alcohol consumption can lead to devastating outcomes on the maternal and foetus. Pregnancy intention exerts a significant effect on promoting healthy lifestyle behaviours. However, no reliable evidences confirmed pregnancy intention was associated with smoking and alcohol consumption before and during pregnancy. DESIGN Systematic review and meta-analysis. METHODS We performed a comprehensive search from databases including PubMed, Cochrane, Web of Science, IEEE Xplore, MEDLINE, ProQuest and Scopus from the inception of these databases up to November, 2020. All eligible studies exploring the association between pregnancy intention and smoking or alcohol consumption were included. The fixed- or random effect pooled measure was used to estimate the odds ratio (OR) or risk ratio (RR) and 95% CI. In addition, the PRISMA checklist was used in this meta-analysis. RESULTS A total of 23 studies were included in this systematic review and meta-analysis. During pregnancy, the findings suggested that women with unplanned pregnancy were 68% more likely to consume cigarettes (OR = 1.68, 95% CI = 1.44-1.95) and 44% more likely to consume alcohol (OR = 1.44, 95% CI = 1.15-1.81) than those women with planned pregnancy. Meanwhile, during preconception, women with unplanned pregnancy were 30% more likely to consume cigarettes (OR = 1.30, 95% CI = 1.10-1.53) and 20% more likely to consume alcohol (OR = 1.20, 95% CI = 1.01-1.42) than those women with planned pregnancy. CONCLUSION The findings suggested that women with unplanned pregnancy were more likely to follow unhealthy behaviours such as smoking and alcohol consumption before and during pregnancy. Health professionals should consider the women's desire for pregnancy to decrease preconception and pregnancy smoking or alcohol consumption in future studies. RELEVANCE OF CLINICAL PRACTICE Pregnancy intention is the key determinant of smoking and alcohol consumption during preconception and pregnancy periods. Offering effective contraception in primary healthcare setting could prevent unplanned pregnancy. Meanwhile, popularising minimal alcohol consumption and comprehensive smoke-free legislation would be beneficial to improve reproductive outcomes.
Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain

The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. These local energy markets will require probabilistic price forecasting models to better describe the future price uncertainty. This article considers the application of probabilistic electricity price forecasting models to the wholesale market of Great Britain (GB) and compares them to better understand their capabilities and limits. One of the models that this paper considers is a recent novel X-model that predicts the full supply and demand curves from the bid-stack. The advantage of this model is that it better captures price spikes in the data. In this paper, we provide an adjustment to the model to handle data from GB. In addition to this, we then consider and compare two time-series approaches and a simple benchmark. We compare both point forecasts and probabilistic forecasts on real wholesale price data from GB and consider both point and probabilistic measures.
Crisis Management Strategy in Handling Financial Sector Scandals in the Digital Transformation Era

This paper provides empirical evidence of how scandals could affect financial institutions in terms of market stock price, yearly returns, and the length of time it took to regain the public’s trust and ultimately recover in the long run. Moreover, we carefully examine the importance of dealing with crisis management in the digital transformation era’s financial service sector. We specialize in crisis management, which aims to mitigate the destruction of companies’ public crises in existence. Finally, based upon investigating scandals in public and private financial sectors in the United States, we list 21 strategic crisis management plans at the end of the paper to handle financial services sector scandals in the digital transformation era.
Towards Personal Sustainability: Renewal as an Antidote to Stress

Research about stress management has traditionally focused on reducing possible stressors. However, more recent studies have highlighted the importance of renewal as an antidote to stress. The purpose of this paper is to provide a theoretical explanation of how renewal activities can invoke a psycho-physiological response that enhances personal sustainability. By drawing upon recent evidence from the fields of medicine, psychology, and management, we developed a conceptual understanding of how renewal activities are characterized by the movement of the individual from a negative to a positive psycho-physiological state. Such a transition happens over a tipping point, which people can enact in themselves and in others through emotional and social intelligence competencies. We illustrate this proposition with the specific case of ideal self-based coaching in the workplace that can enhance personal sustainability among both leaders and their team members.
Natural and Human-Transformed Vegetation and Landscape Reflected by Modern Pollen Data in the Boreonemoral Zone of Northeastern Europe

Modern pollen composition obtained from waterbody surface sediment represents surrounding vegetation and landscape features. A lack of detailed information on modern pollen from Latvia potentially limits the strength of various pollen-based reconstructions (vegetation composition, climate, landscape, human impact) for this territory. The aim of this study is to compare how modern pollen from natural and human-made waterbodies reflects the actual vegetation composition and landscape characteristics. Modern pollen analyses from surface sediment samples of 36 waterbodies from Latvia alongside oceanic-continental, lowland-upland, urban-rural and forested-agricultural gradients have been studied. In addition, we considered the dominant Quaternary sediment, soil type and land use around the studied waterbodies in buffer zones with widths of one and four km. The information on climate for the last 30 years from the closest meteorological station for each study site was obtained. Data were analyzed using Pearson correlation and principal component analysis. Results show that relative pollen values from surface sediment of waterbodies reflect dominant vegetation type and land use. Modern forest biomass had a positive correlation with pollen accumulation rate, indicating the potential use of pollen-based forest biomass reconstructions for the boreonemoral zone after additional research and calibration.
A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

: Skin diseases effectively influence all parts of life. Early and accurate detection of skin cancer is necessary to avoid significant loss. The manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color levels. Therefore, an automated system to identify these skin diseases is required. Few studies on skin disease classification using different techniques have been found. However, previous techniques failed to identify multi-class skin disease images due to their similar appearance. In the proposed study, a computer-aided framework for automatic skin disease detection is presented. In the proposed research, we collected and normalized the datasets from two databases (ISIC archive, Mendeley) based on six Basal Cell Carcinoma (BCC), Actinic Keratosis (AK), Seborrheic Keratosis (SK), Nevus (N), Squamous Cell Carcinoma (SCC), and Melanoma (M) common skin diseases. Besides, segmentation is performed using deep Convolutional Neural Networks (CNN). Furthermore, three types of features are extracted from segmented skin lesions: ABCD rule, GLCM, and in-depth features. AlexNet transfer learning is used for deep feature extraction, while a support vector machine (SVM) is used for classification. Experimental results show that SVM outperformed other studies in terms of accuracy, as AK disease achieved 100% accuracy, BCC 92.7%, M 95.1%, N 97.8%, SK 93.1%, SCC 91.4% with a global accuracy of 95.4%.
Knowledge-Guided Disentangled Representation Learning for Recommender Systems

In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel Knowledge-guided Disentangled Representations approach (KDR) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.
Ultra-Wideband Reconfigurable X-Band and Ku-Band Metasurface Beam-Steerable Reflector for Satellite Communications

A continuously reconfigurable metasurface reflector based on unit cell mushroom geometry that was integrated with a varactor diode is presented in this paper. The unit cell of the metasurface was designed and optimized to operate in the X-band and Ku-band, improving satellite communication’s quality of service. The losses mechanisms of continuous control over the unit cell phase reflection in beam steering resolution are considered and the analysis results are presented. The unit cell design parameters were analyzed with an emphasis on losses and dynamic reflection phase range. The unit cell magnitude and phase reflection are shown in the wide frequency bandwidth and showed a good agreement between all the measurements and the simulations. This metasurface enabled a high dynamic range in the unit cell resonant frequency range from 7.8 to 15 GHz. In addition, the reflection phase and absorption calibration are demonstrated for multiple operating frequencies, namely, 11 GHz, 12 GHz, and 13.5 GHz. Furthermore, design trade-offs and manufacturing limitations were considered. Finally, a beam-steering simulation using the designed metasurface is shown and discussed.
Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

The Shared Mobility Problems (SMP) with the rideshare concept based on sharing a vehicle are fast becoming a trend in many urban cities around the world. Examples of these problems are like ridesharing, carpooling, taxisharing, buspooling, vanpooling, and multi-modal ridesharing. This is the new way to access transportation services by those who are propelling the sharing economy, where access rather than ownership is the new norm. This paper provides a systematic review of SMP using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method. A total of 110 papers from the last decade are selected from 12 abstracts and citation databases to be reviewed and classified. This is done based on the problem types, variants, characteristics, and solution approaches. The current trends and analysis of the survey findings are also summarised. From this systematic review, it is observed that both the time window and multi-objective problems are popular among the researchers, while the minimisation of the total cost is the main concern in the literature of the SMP. Both static and dynamic cases of the SMP are the most researched where heuristic and metaheuristic approaches are widely adopted by the researchers in the literature. Finally, challenges and suggestions for future work are discussed and highlighted.
Model-Based Condition-Monitoring and Jamming-Tolerant Control of an Electro-Mechanical Flight Actuator with Differential Ball Screws

The work deals with the development of deterministic model-based condition-monitoring algorithms for an electromechanical flight control actuator with fault-tolerant architecture, in which two permanent magnets synchronous motors are coupled with differential ball screws in speed-summing paradigm, so that the system can operate even after a motor fault, an inverter fault or a mechanical jamming. To demonstrate the potential applicability of the system for safety-critical aerospace applications, the failure transients related to major fault modes have to be characterised and analysed. By focusing the attention to jamming faults, a detailed nonlinear model of the actuator is developed from physical first principles and experimentally validated in both time and frequency domains for normal condition and with different types of jamming. The validated model is then used to design the condition-monitoring algorithms and to characterize the system failure transient, by simulating mechanical blocks in different locations of the transmission. The operability after the fault, obtained via fault-tolerant control strategy and position regulator reconfiguration, is also verified, by highlighting and discussing possible enhancements and criticalities.
Re-considering the nature of work in complex adaptive organisations – fluid work as a driver of learning through work

Purpose This paper aims to draw on data from a study of professionals’ experiences of work and learning framed by a complex adaptive systems approach to examine the nexus of work and learning in complex adaptive organisations. Design/methodology/approach The study used an adapted phenomenographic approach and the complex adaptive systems conceptual framework (CAOCF) to analyse data from semi-structured interviews with fourteen professionals from a variety of organisations and industry sectors within Sydney, Australia. Findings The findings highlight that work in complex adaptive organisations is best described as fluid work. Further, the findings suggest that fluid work influences professionals towards flexible learning approaches that take place in the flow of work. Originality/value This paper empirically demonstrates the nexus of work and learning as experienced by professionals in their day-to-day work, as well as the ways in which fluid work influences flexible and adaptable learning through participation in work.