Blockchain Technology Based Credibility Verification in E-Journal Entities

In the industrial era 4.0, it has surpassed increasingly complex technological advances in the information system that required a very high infrastructure and facilities and prevented fraud. Counterfeiting is a procedure following an entity, statistics, or documents (observe forged documents) with plans to deceive. Falsification of extensive information certificates is exacerbated by the current economic situation in Indonesia, which is currently occurring due to the widespread epidemic, namely COVID-19. The spread of the COVID-19 outbreak has had a significant impact and changes in all sectors, especially certificate forgery in Indonesia. The imitation of busy certificates is the main focus of this journal, focusing on journal publications combined with Blockchain technology. The sophistication of blockchain technology as authentication is comparing two or more items or additional tests to ensure the accuracy, correctness, or correctness of the information. This method uses qualitative methods with data sources based on case studies in controlled supervision with the basic concept of cryptography as the basis of analysis. From the decisions made, it is hoped that this will minimize the level of forgery of certificates widely measured during the COVID-19 era. © 2022 IEEE.

How Trust in Authority and Trust in Networks Relates to Verification and Sharing Behaviours: COVID-19 Health Information Sharing on WhatsApp

Messaging platforms like WhatsApp are some of the largest contributors to the spread of Covid-19 health misinformation but they also play a critical role in disseminating credible information and reaching populations at scale. This study explores the relationships between verification behaviours and intention to share information to users that report high trust in their personal network and users that report high trust in authoritative sources. The study was conducted as a survey delivered through WhatsApp to users of the WHO HealthAlert chatbot service. An adapted theoretical model from news verification behaviours was used to determine the correlation between the constructs. Due to an excellent response, 5477 usable responses were obtained, so the adapted research model could be tested by means of a Structural Equation Model (SEM) using the partial least squares algorithm on SmartPLS4. The findings suggest significant correlations between the constructs and suggest that participants that have reported high levels of trust in authoritative sources are less likely to share information due to their increased behaviours to verify information. © 2023 IEEE.

Verification and validation of a framework for collaborative BIM implementation, measurement and management (CIMM)

PurposeThe purpose of this research is to develop through a two-stage verification and validation process a novel implementation framework for collaborative BIM, utilising experts from academia and industry as well as a real-world case study project.Design/methodology/approachThe aim of this research was to build upon previous research findings by the authors in order to develop an implementation framework that stems from ousting the inefficiencies of current collaborative BIM practices. This is achieved by a more objectified and quantified approach towards seeking heightened transparency and objectivism of what is required through the implementation of BIM. The mixed research methods technique of both qualitative and quantitative data collection was utilised, with the structure consisting of a two-stage approach utilising the Delphi model for verification and validation. This was developed to test the novelty and beneficial structure hypothesis involving 15 core BIM experts from academia, construction and design with c. 22 years average experience. Validation was undertaken on a complex, high value real world building structures project in central London, inclusive of 8 core project BIM experts. The research utilised a developed solution that mirrored and provided a more holistic representation guiding the practitioners as a project team step by step through the determination of underpinning elements, which support the goal of enhanced information requirements as well as executing the prioritisation measurement tools as part of the framework. Data ascertained at the workshop case study prioritised areas of importance that are core in supporting the delivering of these enhanced information requirements at a project delivery level, which were in order of prioritisation determined by the project team (1) constraints (39.17%), (2) stakeholder requirements (35.78%), (3) coordination (existing asset) (15.86%), (4) exchange requirements (5.38%) and (5) level of information need (3.81%). Furthermore, risk mitigations for the top three priorities were focussed on early stakeholder engagement, appropriation of survey data collection, focus on quality of outputs and applying toolsets and processes with meaning and emphasis on the defined high-level requirements.FindingsFindings show that the framework and the developed solution translate the process methodology of the framework schema into a useable and beneficial tool that provides both qualitative and quantitative inputs and outputs. Furthermore, a collective agreement on the objectives, risk mitigations and assignment of tasks in order to achieve outcomes is presented, with evidence on numerical weightings and goal achievement.Research limitations/implicationsDue to the impacts of COVID-19 on physical engagements both the verification (electronic survey questionnaire) and validation (case study project) were undertaken remotely, using available technologies and web interfaces.Practical implicationsThe case study workshop was limited to one building structures project in central London of a value of c. £70 m design and build cost that the project team (participants) were actively engaged with.Social implicationsThe social impacts of this research has resulted in the review of existing systems, methods and approaches from a wider perspective of theoretical and applied environments, which led to the development of a novel approach and framework guided by an interactive and useable solution.Originality/valueAs shown within the core findings, experts across academia and industry (design and construction) confirmed that the framework methodology and application were 100% novel, and added a benefit to the existing collaborative BIM approach. Value added is that through objectifying, weighting/prioritizing and creating a discussion supported by qualitative and quantitative reasoning the focus on what collaborative BIM is to achieve is increased, and thus the likelihood of successful implementation.

U-Net based Semantic Segmentation for Touchless Fingerprint Technology: A Reflective Review

Touch-based fingerprints are widely used in today's world;even with all the success, the touch-based nature of these is a threat, especially in this COVID-19 period. A solution to the same is the introduction of Touchless Fingerprint Technology. The workflow of a touchless system varies vastly from its touch-based counterpart in terms of acquisition, pre-processing, image enhancement, and fingerprint verification. One significant difference is the methods used to segment desired fingerprint regions. This literature focuses on pixel-level classification or semantic segmentation using U-Net, a key yet challenging task. A plethora of semantic segmentation methods have been applied in this field. In this literature, a spectrum of efforts in the field of semantic segmentation using U-Net is investigated along with the components that are integral while training and testing a model, like optimizers, loss functions, and metrics used for evaluation and enumeration of results obtained. © 2022 IEEE.

The Verification & Validation of the Mars 2020 Perseverance Rover Surface Mission Operations System

Leading up to its landing on Mars on February 18, 2021, the Mars 2020 Perseverance rover Surface Mission Operations System (MOS) underwent a verification and validation (V&V) campaign consisting of a series of Super Thread Tests and Operational Readiness Tests. This V&V campaign emphasized incremental testing, mission scenario and capability coverage, and the Test-As-You-Fly approach wherever possible. Although the elements that comprise the Surface System underwent their own internal V&V campaigns, the system-level tests were vital in uncovering findings observable only through the integrated and flight-like nature of these Surface Mission Operations System V & V tests. The COVID-19 pandemic posed additional challenges, including limited facility access, constrained in-person activities, changes to operations paradigms, and evolving safety protocols in the midst of the testing campaign and preparation for surface operations. This paper describes the Verification and Validation campaign of the Mars 2020 Perseverance Rover Surface Mission Operations System that led to the readiness for rover operations on Mars. © 2023 IEEE.

Antecedents of freight transportation for sustainable supply chain in the post-COVID era: an emerging market study

PurposeIn past years, the global supply chain has witnessed devastating effects of coronavirus (COVID-19) disease. However, the COVID-19 pandemic has renewed the interest of the Sustainable Supply Chain (SSC) stakeholders on sustainability. The stakeholders are now rethinking their business processes and strategy to make them sustainable. In this context, the relevant literature is required to support emerging markets to formulate sustainability-focussed strategies. The purpose of this study is to provide a comprehensive analysis of potential antecedents that leads towards sustainable development of freight transportation in emerging markets.Design/methodology/approachInitially, the antecedents of the Sustainable Freight Transport (SFT) system are derived from the literature survey followed by verification from the experts. Then, the potential antecedents are categorized under four (social, organizational, operational and environmental) broad categories. Afterwards, a Neutrosophic Analytic Network Process (N-ANP) method is employed to obtain the priority weights of the identified potential antecedents.FindingsThe paper identified and ranked 17 antecedents of the SFT system. According to the study's findings, the top three antecedents of SFT are "the presence of a multimodal transportation system,” "circularity in SFT” and "traffic congestion management”. The results from the study advocate the promotion of existing multi-modal transport facilities which is promising to achieve sustainability. The results suggested the adoption of the digital twin to manage the transport operations.Originality/valueThis study sheds light on how to achieve sustainability in the freight transportation system post-COVID era highlighting the potential antecedents. The study's findings will assist practitioners in developing SFT strategies in the face of such pandemics in future.

Candidate SNP Markers Significantly Altering the Affinity of TATA-Binding Protein for the Promoters of Human Hub Genes for Atherogenesis, Atherosclerosis and Atheroprotection.

Atherosclerosis is a systemic disease in which focal lesions in arteries promote the build-up of lipoproteins and cholesterol they are transporting. The development of atheroma (atherogenesis) narrows blood vessels, reduces the blood supply and leads to cardiovascular diseases. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death, which has been especially boosted since the COVID-19 pandemic. There is a variety of contributors to atherosclerosis, including lifestyle factors and genetic predisposition. Antioxidant diets and recreational exercises act as atheroprotectors and can retard atherogenesis. The search for molecular markers of atherogenesis and atheroprotection for predictive, preventive and personalized medicine appears to be the most promising direction for the study of atherosclerosis. In this work, we have analyzed 1068 human genes associated with atherogenesis, atherosclerosis and atheroprotection. The hub genes regulating these processes have been found to be the most ancient. In silico analysis of all 5112 SNPs in their promoters has revealed 330 candidate SNP markers, which statistically significantly change the affinity of the TATA-binding protein (TBP) for these promoters. These molecular markers have made us confident that natural selection acts against underexpression of the hub genes for atherogenesis, atherosclerosis and atheroprotection. At the same time, upregulation of the one for atheroprotection promotes human health.

Face mask effects on speaker verification performance in the presence of noise.

Due to its spread via physical contact and the regulations on wearing face masks, COVID-19 has resulted in tough challenges for speaker recognition. Masks may aid in preventing COVID-19 transmission, although the implications of the mask on system performance in a clean environment and with varying levels of background noise are unclear. The face mask has an impact on speech output. The task of comprehending speech while wearing a face mask is made more difficult by the mask's frequency response and radiation qualities, which is vary depending on the material and design of the mask. In this study, we recorded speech while wearing a face mask to see how different masks affected a state-of-the-art text-independent speaker verification system using an i-vector speaker identification system. This research investigates the influence of facial coverings on speaker verification. To address this, we investigated the effect of fabric masks on speaker identification in a cafeteria setting. These results present preliminary speaker recognition rates as well as mask verification trials. The result shows that masks had little to no effect in low background noise, with an EER of 2.4-2.5% in 20 dB SNR for both masks compared to no mask at the same level. In noisy conditions, accuracy was 12.7-13.0% lowers than without a mask with a 5 dB SNR, indicating that while different masks perform similarly in low background noise levels, they become more noticeable in high noise levels.

Deep neural network for monitoring the growth of COVID-19 epidemic using meteorological covariates

Growth of an epidemic is influenced by the natural variation in climatic conditions and enforcement variation in government stringency policies. Though these variations do not prompt an instant change in the growth of an epidemic, effects of climatic conditions and stringency policies become apparent over time. Time-lagged relationships and functional dynamic connectivity among meteorological covariates and stringency levels generate many lagged features deemed to be important for prediction of reproduction rate, a measure for growth of an epidemic. This empirical study examines the importance scores of lagged features and implements distributed lag inspired feature selection with back testing for model selection and forecasting. A verification forecasting scheme is developed for continuous monitoring of the growth of an epidemic. We have demonstrated the monitoring process by computing a week ahead expected target of the reproduction rate and then by computing a one day ahead verification forecast to evaluate the progress towards the expected target. This evaluation procedure will aid the analysts with a decision making tool for any early adjustment of control options to suppress the transmission.

Potenciální produkt a mezera výstupu v období ekonomických krizí

The article investigates potential output and output gap modelling and estimation in the Czech Republic in the period 1996-2021, including the global recession from 2008 and the recent crisis caused by government measures against the COVID-19 pandemic. The unobserved components (UC) methodology is applied, coefficients are estimated by the maximum likelihood method, unobserved variables are estimated using the Kalman filter. The standard UC model is modified in an original way to nonlinearly describe the hysteresis effect by allowing the output gap to have an asymmetrical influence on potential output. The econometric model verification proved significance of the hysteresis effect and showed a substantial inertia of negative consequences of both crises. Predictions of an impact of the War in Ukraine on the gap were also calculated and the uncertainty associated with these predictions was quantified.

Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments

The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.Significance StatementDuring the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.

The YOPP Final Summit: Assessing Past and Forecasting Future Polar Prediction Research

The successes of YOPP from the presentations and keynote presentations included * a better understanding of the impact of key polar measurements (radiosondes and space-based instruments such as microwave radiometers), and recent advancements in the current NWP observing system, achieved through coordinated OSEs in both polar regions (e.g., Sandu et al. 2021);* enhanced understanding of the linkages between Arctic and midlatitude weather (e.g., Day et al. 2019);* advancements in the atmosphere–ocean–sea ice and atmosphere–land–cryosphere coupling in NWP, and in assessing and recognizing the added value of coupling in Earth system models (e.g., Bauer et al. 2016);* deployment of tailored polar observation campaigns to address yet-unresolved polar processes (e.g., Renfrew et al. 2019);* progress in verification and forecasting techniques for sea ice, including a novel headline score (e.g., Goessling and Jung 2018);* advances in process understanding and process-based evaluation with the establishment of the YOPPsiteMIP framework and tools (Svensson 2020);* better understanding of emerging societal and stakeholder needs in the Arctic and Antarctic (e.g., Dawson et al. 2017);and * innovative transdisciplinary methodologies for coproducing salient information services for various user groups (Jeuring and Lamers 2021). The YOPP Final Summit identified a number of areas worthy of prioritized research in the area of environmental prediction and services for the polar regions: * coupled atmosphere, sea ice, and ocean models with an emphasis on advanced parameterizations and enhanced resolution at which critical phenomena start to be resolved (e.g., ocean eddies);* improved definition and representation of stable boundary layer processes, including mixed-phase clouds and aerosols;incorporation of wave–ice–ocean interactions;* radiance assimilation over sea ice, land ice, and ice sheets;understanding of linkages between polar regions and lower latitudes from a prediction perspective;* exploring the limits of predictability of the atmosphere–cryosphere–ocean system;* an examination of the observational representativeness over land, sea ice, and ocean;better representation of the hydrological cycle;and * transdisciplinary work with the social science community around the use of forecasting services and operational decision-making to name but a few. The presentations and discussions at the YOPP Final Summit identified the major legacy elements of YOPP: the YOPPsiteMIP approach to enable easy comparison of collocated multivariate model and observational outputs with the aim of enhancing process understanding, the development of an international and multi-institutional community across many disciplines investigating aspects of polar prediction and services, the YOPP Data Portal3 (https://yopp.met.no/), and the education and training delivered to early-career polar researchers. Next steps Logistical issues, the COVID-19 pandemic, but also new scientific questions (e.g., the value of targeted observations in the Southern Hemisphere), as well as technical issues emerging toward the end of the YOPP Consolidation Phase, resulted in the decision to continue the following three YOPP activities to the end of 2023: (i) YOPP Southern Hemisphere (YOPP-SH);(ii) Model Intercomparison and Improvement Project (MIIP);of which YOPPSiteMIP is a critical element;and (iii) the Societal, Economics and Research Applications (PPP-SERA) Task Team.

Fighting disinformation through source management: a comparison between journalistic models in the context of COVID-19

Introducción: La "pandemia" de la desinformación ha marcado la cobertura periodística del COVID-19, por lo que resulta de interés ver cómo diferentes modelos periodísticos han empleado la gestión de fuentes como criterio de calidad para hacerle frente. Metodología: El presente trabajo analiza las fuentes periodísticas - desde los indicadores de número, identificación y tipologíaempleadas en 420 artículos sobre la cobertura del COVID-19 correspondientes a cuatro modelos profesionales periodísticos: prensa tradicional, medios nativos digitales, prensa popular y plataformas de verificación. Resultados: El análisis refleja una adecuada gestión por parte de la prensa española de las fuentes durante la cobertura de la pandemia, utilizando de forma mayoritaria un número de fuentes superior al estándar;identificándolas correctamente en la práctica totalidad de casos;y empleando una adecuada variedad. Así, pese al claro predominio de las fuentes oficiales, se da también una presencia notable de fuentes expertas de los ámbitos científico y sanitario. Discusión y conclusiones: La calidad en la gestión de fuentes ha contribuido a hacer frente a la desinformación por parte de la prensa española, si bien se detectan importantes diferencias entre modelos profesionales. Las plataformas de verificación son las que presentan unos estándares de calidad más elevados a la hora de gestionar sus fuentes informativas, lo que contribuye a reforzar criterios claves en la lucha contra la desinformación como son verificación, relevancia, credibilidad y transparencia.Alternate :Introduction: The pandemic of misinformation has shaped the journalistic coverage of COVID-19, so it is interesting to see how different journalistic models have used source management as a quality criterion to address it. Methodology: This paper analyses the journalistic sources - from the indicators of number, identification and typology - used in 420 articles on the coverage of COVID-19 corresponding to four journalistic professional models: traditional press, digital native media, popular press and verification platforms. Results: The analysis reflects an adequate management of sources by the Spanish press during the coverage of the pandemic, mostly using a higher than standard number of sources;identifying them correctly in almost all cases;and using an adequate variety. Therefore, despite the clear predominance of official sources, there is also a notable presence of expert sources from the scientific and health fields. Discussion and conclusions: The quality of source management has contributed to fighting disinformation in the Spanish press, although there are important differences between professional models. Verification platforms are those with the highest quality standards when managing their information sources, which helps to reinforce key criteria in the fight against disinformation, such as verification, relevance, credibility and transparency.

Disinformation and Verification in a Digital Society: An Analysis of Strategies and Policies Applied in the European Regional TV Broadcasters of the CIRCOM Network

The recent COVID-19 health crisis has shone a spotlight on disinformation as the circulation of false information became more and more prominent. What the World Health Organization (WHO) has defined as an ‘infodemic' poses a great risk for democracies and for society in general. In this context, public television channels, with their regional scope, actively participate in the fight against misinformation. This research aims to identify and classify the different verification initiatives and technological tools, as well as the different strategies and codes used in fact-checking tasks by European broadcasters belonging to the CIRCOM network. The methodology undertakes an exploratory approach and employs a questionnaire that is applied to a sample of the members of the network. Managers and professionals with executive profiles were asked about the management, operation and strategies used in the verification process. In light of the results obtained, it can be concluded that the current verification processes are based on human efforts, rather than technological tools, amounting to a total dependence on content curation by the writing teams in the newsroom. Thus, it is evidenced that in most cases, there is neither a specific department for verification, nor sufficient resources, despite the fact that all those surveyed regard disinformation as a priority issue, a threat to democratic integrity and a responsibility of public service media.

A Study on Digital Signature in Blockchain Technology

In the recent trends, block chain technology plays a vital role due to its secured methodology. Since many parts of the world severely affected by COVID pandemic situation, people used to perform more of digital transactions day by day. To provide more security for all online transactions, Block chain Technology is one of the solutions that many countries have started to utilize. Every block in a block chain should be validated and verified by the secured hash algorithms and digital signatures. Since Hacking is difficult in Block Chain Technology, due to its secure hash process, many people started using it for various applications. This article discusses about the working of Digital Signatures especially about Elliptic Curve Digital Signature Algorithm - ECDSA. Digital signatures are signatures done digitally for the purpose of security. ECDSA works on the mechanism of Elliptic Curve and Cryptography algorithms of Elliptic curve. The proposed research work has selected the private key as a random point, which is of type integer ranging between 1 and n-1 in Elliptic curve to sign documents. Then Public key is calculated by multiplying a point from elliptic curve with the Private key. Using this Public key, our digital signature is verified. This is much secured because no one can crack/hack private key. This process is used to verify digital transactions in the block chain technology. Our study helps researcher in block chain technologies to proceed ahead. © 2023 IEEE.

The Next Chapter in Global Compliance Auditing

The recent COVID-19 pandemic spurred evolutionary changes to how risk management programs are executed across the globe. As companies responded to the effects the virus had on their operations, they also had to continue their compliance verifications despite the inability of audit teams to visit sites for a first-hand evaluation of program status. Audit teams were driven to innovate to accomplish their mission. This metamorphosis has forever changed the process by which compliance auditing is conducted. These methodologies continue to evolve as the pandemic endures. This article will discuss the author's views of advancements in compliance auditing as a direct result of the pandemic. It will feature tools that have been developed, including planning tips, audit strategies, auditor utilization, and various concepts that audit teams can employ. The author will explain how she believes these aspects will affect the future of post-pandemic compliance auditing, particularly focusing on auditing for Process Safety Management (PSM) system requirements. © 2022 AIChE Spring Meeting and 18th Global Congress on Process Safety, GCPS 2022. All rights reserved.

Testing and Verifying the Security of COVID-19 CT Images Deep Learning System with Adversarial Attack

The Coronavirus disease 2019 (COVID-19) is a pandemic that occurred in December 2019 and spread globally. Most of the current research is on how to apply deep learning to detect COVID-19, but little research has been done on the security of COVID-19 deep learning systems. Therefore, we test and verify the security of COVID-19 CT images deep learning system with adversarial attack. Firstly, we build a deep learning system for recognizing COVID-19 CT images. Secondly, adding imperceptible disturbance to CT images will lead to neural network classification errors. Finally, we discuss the application of formal methods and formal verification to deep learning systems. We hope to draw more attention from researchers to the application of formal methods and formal verification to artificial intelligence. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An IoT Based Smart Vault Security and Monitoring System with Zero UI

Recent advances have introduced IoT as one of the key technologies globally. As safety remains a critical issue for those who spend much time outside. Automated security systems are very useful where safety is an important issue. With a prospect of a Zero User Interface (UI) model this work represents a novel IoT based smart vault security system. The system is built and designed based on IoT combining with Arduino-Uno and Bluetooth module. This system involves LDR sensor, IR sensor and Sonar sensor for monitoring. The vault provides security on three levels. Password protected entry to connect with the smartphone using Bluetooth module, IR sensor array to use 'secret gesture pattern' to unlock the door, tracking number of transactions from the vault using Sonar sensor and LDR was used as a switch. To avoid the replication of physical unlocking of objects IR sensor array was used to introduce 'secret gesture pattern' unlocking system through touchless interfaces for the avoidance of transmissive diseases like COVID-19. This novel system has substantial possibility as a security vault system for industrial and residential use in a contactless manner. © 2023 IEEE.

Intelligent Remote Online Proctoring in Learning Management Systems

An essential component of the learning phase is the administration of assessments using online testing platforms. It is the responsibility of the platforms used for online education to guarantee that every student successfully completes the evaluation procedure without cheating. Because of the widespread COVID-19 epidemic, all educational institutions are required to alter the process by which they administer online examinations and to take measures to reduce the amount of cheating that takes place during online examinations. In this paper, we create a proctoring system that is integrated with a learning management system. It has the following features: user verification, browser lockdown, face counter, and automatic cheating counter. The goal of this proposed system is to solve the problem that was presented. On the client side, there is no need for any extra software or programs to support any of these functions. System experiment is done under actual exam situations in order to gauge the dependability of the proctoring system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Improving Masked Face Recognition Using Dense Residual Unit Aided with Quadruplet Loss

All over the world, people are wearing face masks and practising social distancing to protect themselves against the Coronavirus disease (COVID-19). The need for contactless biometric systems has increased to avoid the common point of contact. Among contactless biometric systems, facial recognition systems are the most economical and effective ones. Conventional face recognition systems rely heavily upon the facial features of the eyes, nose, and mouth. But due to people wearing face masks, the important facial features of the nose and mouth get hidden under the mask, resulting in degraded performance by the facial recognition systems on masked faces. In this paper, we propose a Dense Residual Unit (DRU) aided with Quadruplet loss on top of existing facial recognition systems. This solution tries to unveil the masked faces by producing embeddings for masked faces, which are similar to embeddings of unmasked faces of the same identity but different from embeddings of different identities. We have evaluated our method using two pre-trained facial recognition models' backbones, i.e. Resnet-101 and MobileFaceNet, and upon two datasets, among them, one is a real-world dataset, i.e. MFR2, and one is a simulated masked face dataset i.e. masked version of LFW. We have achieved improvement in the performance of masked face recognition in terms of False Match Rate, False Non-Match Rate, Fisher Discriminant ratio, and Equal Error Rate. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.