Verification Method Research Articles

NAVIGATING MEDIA LITERACY CHALLENGES IN HIGHER EDUCATION EFL COURSES: INSIGHTS FROM STUDENT BEHAVIORS AND PEDAGOGICAL STRATEGIES

This study aims to assess the media literacy proficiency of university students to enhance educational programs and skill development. Using a mixed methods approach, it examines students’ social media usage, interactions with misinformation, and news verification strategies. Data collection involves Google Forms surveys, the Online Standard Deviation Calculator, and interviews with faculty members. The findings highlight significant social media use, with platforms like Google, YouTube, Instagram, and TikTok being ubiquitously used. Students’ engagement with misinformation and their verification methods are explored, revealing a reliance on search engines and the need for legal frameworks. The challenges faced by students in navigating social media underscore the pivotal role of media literacy education. Material and pedagogical hurdles limit its integration effectiveness. The study advocates for a holistic approach, emphasizing the need for didactic materials and faculty development to foster effective media literacy integration. Despite challenges, this study highlights collective awareness and commitment to enhancing media literacy in academia. Insights from online behaviours, media consumption, and misinformation encounters inform pedagogical preferences, offering considerations for refining teaching methodologies. Addressing identified challenges is crucial for cultivating a media-literate academic community, marking this study as a catalyst for future research and interventions. Key words: critical media literacy, effective learning, fake news, clickbait, social networks.

The characteristics and functionalities of citizen-led disaster response through social media: A case study of the #HenanFloodsRelief on Sina Weibo

Using the topic of #HenanFloodRelief on Sina Weibo during the 2021 Henan floods as a case study, this research adopts a combination of social network analysis, content analysis, and text analysis to explore the characteristics and roles of public participation in disaster response through social media. The findings show that under the #HenanFloodRelief topic, the public was mainly involved in emergency response by posting help and rescue information, updating disaster information, and providing contact details for resource coordination. They also expressed emotions to reflect post-disaster social psychology, providing an emotional basis for social mobilization. Public and organizational participation demonstrated different characteristics, exhibiting the advantages of spontaneity and firsthand information. In contrast, organizational participation reflected professionalism and played a more significant role in providing emotional comfort and mobilization. The research demonstrates the vital role of social media in enabling public participation in disaster response while pointing out the potential risks of the public utilizing information resources for online relief efforts, highlighting the need for further research into effective information verification methods. This study provides a Chinese case to examine public participation in disaster response and offers insights into this field of research.

Public Danger of the Act: Verification Impossible?

Public danger, being largely objective in nature, is the most important feature of a crime, underlying the decision to criminalize some act. However, there is no knowledge on how to verify whether it exists in each specific case of criminal legal prohibition, since the theory of verification of public danger in Russia has yet to be formed. The paper proposes approaches to solving this problem. In a number of cases, it should be recognized that public danger is a presumption (in particular, for those criminal acts that involve causing harm to life or health, using coercion, some forms of deception, etc.). The presumption is based here on such a method of verification as protocol statements, and, in fact, does not require other methods. In all other cases, when verifying a public danger, the author suggests relying on the method of expert assessments, proof by contradiction, and looking for new ways to verify the true nature of the public danger. In this case, verification of public danger is mandatory and should ideally be carried out at the stage of drafting the bill.

Fraud Prediction and Verification of Smart Credit Card using Machine Learning Techniques

This study unveils a powerful method for smart credit card fraud detection and verification. This system integrates data preprocessing, feature engineering, and real-time prediction using a hybrid model that incorporates supervised machine learning algorithms, an encoder, and LSTM networks. A supervised LSTM network sorts transactions, while an unsupervised Autoencoder finds outliers. Assessment criteria strike a balance between recall and accuracy. Alerts are sent by the system upon detection of fraud, and it runs in real-time. Compliance, scalability, and constant monitoring are key points. To close the gap between ease and safety in contemporary monetary transactions, this project offers a state-of-the-art method for strengthening the security of smart credit cards.

LC-MS/MS analysis of 11-nor-9-carboxy-hexahydrocannabinol (HHC-COOH) and 11-hydroxy-hexahydrocannabinol (HHC-OH) for verification of hexahydrocannabinol (HHC) intake

Natural and semi-synthetic cannabinoid analogs are getting increasing media attention for their recreative use as an alternative to traditional cannabis, in Sweden as well as internationally. To investigate an increasing number of urine samples incoming to our clinical laboratory that were screening positive, using a CEDIA THC-COOH immunoassay from ThermoFisher Scientific, but then testing negative using GC-MS based verification analysis, we developed an LC-MS/MS-method for verification of hexahydrocannabinol (HHC) and Δ8-tetrahydrocannabinol. Assessment of HHC intake was based on identification of the following four metabolites: 11-nor-9(R)-carboxy-hexahydrocannabinol (R-HHC-COOH), 11-nor-9(S)-carboxy-hexahydrocannabinol (S-HHC-COOH), 11-hydroxy-9(R)-hexahydrocannabinol (R-HHC-OH) and 11-hydroxy-9(S)-hexahydrocannabinol (S-HHC-OH). Out of 46 urine samples analysed in this study, 44 showed presence of HHC-metabolites, which indicate HHC as the main explanation for an increased number of negative verifications for THC-COOH. In these samples, the HHC-OH metabolites occurred at a higher concentration than R-HHC-COOH while S-HHC-COOH was only detected in few samples at low concentrations. R-HHC-COOH and S-HHC-COOH can easily be added to a pre-existing verification method for THC-COOH, and still show acceptable results, while HHC-OH requires an enzyme capable of hydrolysing the ether glucuronide bond.

Towards Large Certified Radius in Randomized Smoothing Using Quasiconcave Optimization

Randomized smoothing is currently the state-of-the-art method that provides certified robustness for deep neural networks. However, due to its excessively conservative nature, this method of incomplete verification often cannot achieve an adequate certified radius on real-world datasets. One way to obtain a larger certified radius is to use an input-specific algorithm instead of using a fixed Gaussian filter for all data points. Several methods based on this idea have been proposed, but they either suffer from high computational costs or gain marginal improvement in certified radius. In this work, we show that by exploiting the quasiconvex problem structure, we can find the optimal certified radii for most data points with slight computational overhead. This observation leads to an efficient and effective input-specific randomized smoothing algorithm. We conduct extensive experiments and empirical analysis on CIFAR-10 and ImageNet. The results show that the proposed method significantly enhances the certified radii with low computational overhead.

Blockchain-Based Method for Spatial Retrieval and Verification of Remote Sensing Images.

Remote sensing image is a vital basis for land management decisions. The protection of remote sensing images has seen the application of blockchain's notarization function by many scholars. Yet, research on efficient retrieval of such images on the blockchain remains sparse. Addressing this issue, this paper introduces a blockchain-based spatial index verification method using Hyperledger Fabric. It linearizes the spatial information of remote sensing images via Geohash and integrates it with LSM trees for effective retrieval and verification. The system also incorporates IPFS as an underlying storage unit for Hyperledger Fabric, ensuring the safe storage and transmission of images. The experiments indicate that this method significantly reduces the latency in data retrieval and verification without impacting the write performance of Hyperledger Fabric, enhancing throughput and providing a solid foundation for efficient blockchain-based verification of remote sensing images in land registry systems.

PDMSC: privacy-preserving decentralized multi-skill spatial crowdsourcing

Purpose The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues. Design/methodology/approach This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized. Findings This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious. Originality/value This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.

The inscrutable baseline and the problem of attribution

This paper examines the problem of attribution in the evaluation of energy efficiency program impact. The methodological problem concerns the observability of consumer behavior under the baseline condition of no program intervention. The statistical solution to the problem, which entails randomized exposure of targeted individuals to program influence, is not a viable alternative in most applications. Randomized opt-in and randomized encouragement designs do not conform to this requirement because all targeted individuals are encouraged to participate in the program, resulting in negative exposure bias. Quasi-experimental methods which utilize non-targeted individuals or targeted nonparticipants as baseline surrogates are further subject to selection bias of unknown magnitude and direction. Valid attribution in the general case of unrestricted eligibility depends on prior knowledge of the determinants of measure adoption and program participation. In default of such knowledge, evaluators must rely upon structural assumptions that have no foundation in empirical science. On the other hand, established measurement and verification methods which exploit scientific knowledge of the determinants of end-use energy consumption should be utilized to obtain unbiased estimates of individual measure and gross program energy savings.

Disclosure Effect of Enterprise Risk Management, Corporate Governance, Corporate Social Responsibility on Corporate Value

The purpose of this study is to ascertain the degree to which company value in banking sector companies is influenced by enterprise risk management, corporate governance, corporate social responsibility, and company value between 2019 and 2022. The description and verification method is the one that is employed. The author employs multiple regression analysis, correlation analysis, and coefficient of determination analysis as analytical techniques. The analysis's findings demonstrate that the degree of Company Value (Y) is significantly impacted by the ERM variable (X1). This is evident from the 0.275 t value and a probability (p) = 0.038 and a regression coefficient (β) of 0.032. Company Value (Y) is significantly impacted by the CG variable (X2). The regression coefficient (β) of 0.050 and the t value of 0.362 with probability (p) = 0.002 both demonstrate this. Company Value (Y) is significantly impacted by the CSR variable (X3). The t value of 0.530 and the regression coefficient (β) of 0.045, along with the probability (p) = 0.006, demonstrate this. Company Value (Y) is significantly and simultaneously impacted by the ERM, CG, and CSR variables

The Influence of Democratic Leadership Style and Career Development on Employee Performance Mediated by Jambi Province Regional Secretariat Organization Bureau Employee Job Satisfaction

This research aims to find out and describe the description of democratic leadership style, career development, job satisfaction and employee performance at the Regional Secretariat Organization Bureau of Jambi Province as well as to find out and analyze the influence of democratic leadership style and career development on employee performance mediated by job satisfaction at the Secretariat Organization Bureau Jambi Province Region. The method used in this research is descriptive verification and quantitative methods. Based on the approach, it can be seen that the number of samples is 62 people. This research uses the Smart PLS 3.0 analysis tool. Based on the description of e-government, competency, work motivation and employee performance in the Bureau. Based on the description of the democratic leadership style, it shows that the democratic leadership style is in the quite good category. Then for fairly good career development, the description of the job satisfaction variable is in the quite satisfied category. Meanwhile, the description of employee performance variables in the category is quite high. Based on the research results, leadership style has a positive and significant effect on job satisfaction, career development has a positive and significant effect on job satisfaction, democratic leadership style has a positive and significant effect on employee performance, career development has a positive and significant effect on employee performance, job satisfaction has a positive and significant effect. significant effect on employee performance, Democratic leadership style has a positive and significant effect on employee performance mediated by job satisfaction and career development does not have a significant effect on employee performance mediated by job satisfaction.

Verification and validation optimization method for signal quality bits in digital control system application software of nuclear power plant

Abstract The digitalization enhances nuclear power plant (NPP) instrumentation and control (I&C) system performance, while simultaneously introducing safety and reliability challenges. To enhance the stability of distributed control system (DCS), and minimize the dangerous failure rate and safe failure rate of safety system, signal quality bits (SQBs) are widely used in NPP DCS to identify the validity of important signals. However, in the past decade or so, numerous unscheduled turbine shutdowns and/or reactor trips have resulted from incorrect SQB design or settings. Therefore, it is necessary to conduct optimization research on the design methods and setting principles of SQBs, establish an optimization method for DCS application software SQB verification and validation (V&V), then minimize DCS faults attributed to SQB. With the aim, this work conducts an in-depth investigation and analysis of the definition, design, setting and transmission of SQB in NPP. A specific optimization method for software V&V to conduct SQB on NPP safety DCS application software is proposed. Twelve concerns in the SQB V&V work are detailed. The reactor trip system ΔT protection logic is utilized as a case to illustrate the detail implementation process of this optimization method. This work provides an engineering technical reference and operational guidance for SQB software V&V, conducive to ensuring the safe and economical operation of NPPs.

PENGARUH KEPUASAN KERJA DAN BUDAYA ORGANISASI TERHADAP ORGANIZATIONAL CITIZENSHIP BEHAVIOR (Studi pada PT. Bank Rakyat Indonesia Kantor Cabang Sumedang)

Companies need employees who can provide extra roles that can have a good impact on the company. The extra role is known as organizational citizenship behavior (OCB). Organizational citizenship behavior is an optional behavior outside of the employee's job desk, but can have a positive impact on the company. There are several factors that can affect OCB from internal factors, one of which is job satisfaction, while one of the external factors is organizational culture. This study aims to determine whether there is an effect of job satisfaction and organizational culture on organizational citizenship behavior at PT. Bank Rakyat Indonesia (Persero) Tbk. Sumedang Branch Office. The method used in this research is descriptive verification method through a quantitative approach. Data analysis techniques used include descriptive analysis, validity and reliability tests, multiple linear regression analysis, t test, F test, and test the coefficient of determination. The results of the study state that job satisfaction and organizational culture have a significant effect on organizational citizenship behavior. Keywords: Job Satisfaction, Organizational Culture, Organizational Citizenship Behavior

Risk Analysis Research on SMS Verification Code and Biometric Recognition Technology

This paper delves into the ongoing transformation in the domain of digital identity security. It specifically focuses on the paradigm shift from traditional methods of authentication, such as passwords and text message verification codes, towards the use of biometric recognition technology. By scrutinizing the inherent weaknesses linked with the utilization of text message verification codes, the paper underlines the demand for more trustworthy and secure solutions for authentication. Following this, biometric authentication is thoroughly examined, with a highlight on its unique features, the convenience it provides to users, and its capacity for ongoing verification. The discussion extends to practical applications of biometrics in a variety of sectors, including healthcare and high-security workplaces, as well as in situations where employing text message verification is unsuitable. A focal point of the discussion is multi-modal biometric authentication. While acknowledging the potential hurdles and future possibilities tied to the adoption of biometric technology, the paper offers an objective perspective. The overall intent of this research is to furnish insights that will enable informed decision-making regarding the deployment and improvement of digital identity verification methods across a wide range of industries.

Development of a digital star-shot analysis system for comparing radiation and imaging isocenters of proton treatment machine.

To directly compare the radiation and imaging isocenters of a proton treatment machine, we developed and evaluated a real-time radiation isocenter verification system. The system consists of a plastic scintillator (PI-200, Mitsubishi Chemical Corporation, Tokyo, Japan), an acrylic phantom, a steel ball on the detachable plate, Raspberry Pi 4 (Raspberry Pi Foundation, London, UK) with camera module, and analysis software implemented through a Python-based graphical user interface (GUI). After kV imaging alignment of the steel ball, the imaging isocenter defined as the position of the steel ball was extracted from the optical image. The proton star-shot was obtained by optical camera because the scintillator converted proton beam into visible light. Then the software computed both the minimum circle radius and the radiation isocenter position from the star-shot. And the deviation between the imaging isocenter and radiation isocenter was calculated. We compared our results with measurements obtained by Gafchromic EBT3 film (Ashland, NJ, USA). The minimum circle radii were averaged 0.29 and 0.41mm while the position deviations from the radiation isocenter to the laser marker were averaged 0.99 and 1.07mm, for our system and EBT3 film, respectively. Furthermore, the average position difference between the radiation isocenter and imaging isocenter was 0.27mm for our system. Our system reduced analysis time by 10min. Our system provided automated star-shot analysis with sufficient accuracy, and it is cost-effective alternative to conventional film-based method for radiation isocenter verification.

Quali-quanti visual methods and political bots

Computational social science research on automated social media accounts, colloquially dubbed “bots”, has tended to rely on binary verification methods to detect bot operations on social media. Typically focused on textual data from Twitter (now rebranded as "X"), these inference-based methods are prone to finding false positives and failing to understand the subtler ways in which bots operate over time, through visual content and in particular contexts. This research brings methodological contributions to such studies, focusing on what it calls “bolsobots” in Brazilian social media. Named after former Brazilian President Jair Bolsonaro, the bolsobots refer to the extensive and skilful usage of partial or fully automated accounts by marketing teams, hackers, activists or campaign supporters. These accounts leverage online political culture to sway public opinion for or against public policies, opposition figures, or Bolsonaro himself. Drawing on empirical case studies, this paper implements quali-quanti visual methods to operationalise specific techniques for interpreting bot-associated image collections and textual content across Instagram, TikTok and Twitter/X. To unveil the modus operandi of bolsobots, we map the networks of users they follow (“following networks”), explore the visual-textual content they post, and observe the strategies they deploy to adapt to platform content moderation. Such analyses tackle methodological challenges inherent in bot studies by employing three key strategies: 1) designing context-sensitive queries and curating datasets with platforms’ interfaces and search engines to mitigate the limitations of bot scoring detectors, 2) engaging qualitatively with data visualisations to understand the vernaculars of bots, and 3) adopting a non-binary analysis framework that contextualises bots within their socio-technical environments. By acknowledging the intricate interplay between bots, user and platform cultures, this paper contributes to method innovation on bot studies and emerging quali-quanti visual methods literature.

Verification Methods for the Lyapunov–Krasovskii Functional Inequalities

Verification Methods for the Lyapunov–Krasovskii Functional Inequalities

Imaging of moho topography with conditional generative adversarial network from observed gravity anomalies

Accurate estimation of Moho topography plays a crucial role in understanding Earth’s structure, geodynamic processes, and resource exploration. This study presents a novel approach that utilizes conditional Generative Adversarial Networks (cGAN) to reveal Moho topography based on observed gravity anomalies. Synthetic training datasets of Moho topography were generated using the FFT filtering method due to the scarcity of true datasets. Spherical prism-based forward gravity modeling was employed to evaluate the resulting gravity anomalies. We compared the performance of our developed deep learning algorithm cGAN (conditional Generative Adversarial Networks) with a traditional inversion technique using various synthetic datasets, and a real case study in southern peninsular India, a geologically diverse region comprising ancient continental tectonic blocks. Bott’s inversion scheme was employed as a verification method for the Moho surface estimation using the presented deep learning model. Using spherical prism-based forward gravity modeling, observed gravity anomalies were corrected for multiple factors such as topography, bathymetry, sediments, crustal heterogeneities, and mantle heterogeneities. By removing these effects, we isolated the gravity contribution solely related to pure Moho undulation. The mean Moho depth and density contrast between the crust and mantle were derived from seismic constraints for improving estimation accuracy. The findings demonstrate the potential of the cGAN and spherical prism-based gravity modeling approach in accurately estimating the Moho topography, offering insights into Earth’s subsurface structures and enhancing our understanding of geodynamic processes and resource exploration efforts.

ChildAugment: Data augmentation methods for zero-resource children's speaker verification.

The accuracy of modern automatic speaker verification (ASV) systems, when trained exclusively on adult data, drops substantially when applied to children's speech. The scarcity of children's speech corpora hinders fine-tuning ASV systems for children's speech. Hence, there is a timely need to explore more effective ways of reusing adults' speech data. One promising approach is to align vocal-tract parameters between adults and children through children-specific data augmentation, referred here to as ChildAugment. Specifically, we modify the formant frequencies and formant bandwidths of adult speech to emulate children's speech. The modified spectra are used to train emphasized channel attention, propagation, and aggregation in time-delay neural network recognizer for children. We compare ChildAugment against various state-of-the-art data augmentation techniques for children's ASV. We also extensively compare different scoring methods, including cosine scoring, probabilistic linear discriminant analysis (PLDA), and neural PLDA. We also propose a low-complexity weighted cosine score for extremely low-resource children ASV. Our findings on the CSLU kids corpus indicate that ChildAugment holds promise as a simple, acoustics-motivated approach, for improving state-of-the-art deep learning based ASV for children. We achieve up to 12.45% (boys) and 11.96% (girls) relative improvement over the baseline. For reproducibility, we provide the evaluation protocols and codes here.

The Particle-Based Method Utilization on The Various Obstacles Effect to The Two-Fluids Dam Break Simulation

The Molten Salt Fast Reactor (MSFR), a prominent Generation IV reactor design, offers improved safety by eliminating graphite as a moderator and incorporating a passive safety system. Understanding the fuel salt relocation mechanism is crucial in ensuring the reactor’s safety and performance. This study presents a particle-based approach using the Semi-Implicit Moving Particle (MPS) method, which accurately represents fluid as particles carrying essential physical properties. We validate the MPS method as an alternative tool for investigating fuel salt relocation in the MSFR, comparing its results with the well-established δ-SPH method for verification. The simulations encompass scenarios with and without obstacles, involving water-PEO fluids known for their distinct densities and propensity to induce splashing phenomena. The obstacles used are made in several size variations, such as semicircular, rectangular, and triangular obstacles. The excellent alignment of MPS method results with δ-SPH method outcomes confirms its effectiveness in predicting freeze plug melting and fuel salt relocation in the MSFR, providing valuable insights into the passive safety mechanisms. This research contributes to enhancing the safety and efficiency of advanced molten salt reactor designs. Where it can be concluded that the simulation results of the MPS method are almost the same as the simulation results of the δ-SPH method.