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The oil port logistics involves multiple parties including oil tanker owners, port authorities, customs, oil suppliers, and shipping companies. These parties need to exchange a significant amount of data and documentation related to cargo, such as bills of lading, customs declarations, and cargo manifests. This huge amount of data and documentation provides ample opportunities for data manipulation and corruption. Moreover, physical documentation is slow and prone to errors and manipulation. This data can be securely stored and shared between different parties in a tamper-proof and transparent manner using blockchain. Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. The proposed framework provides timely processing of oil trade documents and ensures immutability while increasing trust among the trade entities. In addition, this work implements a private blockchain for the execution of smart contracts, which can ensure that all parties involved in the logistics process comply with pre-agreed rules and regulations. Simulation results validate the effectiveness of the proposed framework in terms of transparency, immutability, network latency, throughput, and resource utilization. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation. [ABSTRACT FROM AUTHOR]

Research on the dynamics of corruption has rarely considered sex as a currency in corrupt transactions. This article puts forward the argument that the abuse of entrusted power in exchange for personal gain is relevant also when sex, rather than money, is the currency. It then contributes with a careful conceptualization of such transactions as sexual corruption. Anchoring our conceptualization in the abuse of entrusted power, the article defines sexual corruption, its elements, categories and delimitations, and proposes a conceptual framework that can be used to develop research on sexual corruption. We also address the implications of recognizing sex as a currency in corrupt transactions, providing insights as to why abuse of power is often overlooked when sex is the currency of the transaction. Our approach offers analytic precision and contributes to setting an agenda for the study of sexual corruption as an obstacle to good governance, at a time when efforts to collect data on sexual corruption are increasing. [ABSTRACT FROM AUTHOR]

Blockchain technology has impacted various sectors and is transforming them through its decentralized, immutable, transparent, smart contracts (automatically executing digital agreements) and traceable attributes. Due to the adoption of blockchain technology in versatile applications, millions of transactions take place globally. These transactions are no exception to adversarial attacks which include data tampering, double spending, data corruption, Sybil attacks, eclipse attacks, DDoS attacks, P2P network partitioning, delay attacks, selfish mining, bribery, fake transactions, fake wallets or phishing, false advertising, malicious smart contracts, and initial coin offering scams. These adversarial attacks result in operational, financial, and reputational losses. Although numerous studies have proposed different blockchain anomaly detection mechanisms, challenges persist. These include detecting anomalies in just a single layer instead of multiple layers, targeting a single anomaly instead of multiple, not encountering adversarial machine learning attacks (for example, poisoning, evasion, and model extraction attacks), and inadequate handling of complex transactional data. The proposed AHEAD model solves the above problems by providing the following: (i) data aggregation transformation to detect transactional and user anomalies at the data and network layers of the blockchain, respectively, (ii) a Three-Layer Hierarchical Ensemble Learning Model (HELM) incorporating stratified random sampling to add resilience against adversarial attacks, and (iii) an advanced preprocessing technique with hybrid feature selection to handle complex transactional data. The performance analysis of the proposed AHEAD model shows that it achieves higher anti-adversarial resistance and detects multiple anomalies at the data and network layers. A comparison of the proposed AHEAD model with other state-of-the-art models shows that it achieves 98.85% accuracy against anomaly detection on data and network layers targeting transaction and user anomalies, along with 95.97% accuracy against adversarial machine learning attacks, which surpassed other models. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the emergence of many new mobile apps and user-centered systems that interact with users by offering choices with rewards. These applications have been promising to address challenging societal problems such as congestion in transportation and behavior changes for healthier lifestyles. Considerable research efforts have been devoted to model the user behaviors in these new applications. However, as real-world user data is often prone to data corruptions, the success of these models hinges on a robust learning method. Building on the recently proposed Latent Decision Threshold model, this article shows that, among the existing robust learning frameworks, the L0-norm-based framework can outperform other state-of-the-art methods in terms of prediction accuracy and model estimation. And based on the L0-norm framework, we further develop a user screening algorithm to identify potential bad actors. [ABSTRACT FROM AUTHOR]

With the progress of the times, cars have become an important means of transportation in our lives. However, with new cars of all kinds, road safety issues still cannot be effectively improved. While countries have responded by enforcing traffic laws and using electronic technology to enhance enforcement, challenges still need to be addressed, including blurred images and misjudgments by police agencies, and even exploiting loopholes in traffic enforcement frameworks to evade consequences through bribery or connections. exacerbating an accident and leaving victims and families in the lurch of the aftermath. To solve this problem, we proposed a data traceability law enforcement system based on blockchain and InterPlanetary File System (IPFS). The system ensures rapid traceability and protects law enforcement data from external attacks. The goal of leveraging blockchain's decentralized nature and smart contracts is to instill accountability and fairness in road safety measures, mitigate the effects of corruption, and pave the way for a safer and more just transportation environment. Our experimental results show that under our stress test of 50 transactions per second, the throughput can be as high as 300 and the maximum delay can reach 2.01s, which is enough to experimentally prove that our system is feasible. Our approach is designed to solve the data corruption problem caused by the centralized server being paralyzed. It applies to all technology enforcement, with high flexibility and scalability for all participants to join or set privileges. we also provide an automated traffic law enforcement system in a smart city. To make the data storage more secure and fair, and not easily damaged by malicious people or tampered with by intentional attackers. This brings a breakthrough to the country's program security. [ABSTRACT FROM AUTHOR]

LiDAR sensors have been shown to generate data with various common corruptions, which seriously affect their applications in 3D vision tasks, particularly object detection. At the same time, it has been demonstrated that traditional defense strategies, including adversarial training, are prone to suffering from gradient confusion during training. Moreover, they can only improve their robustness against specific types of data corruption. In this work, we propose LiDARPure, which leverages the powerful generation ability of diffusion models to purify corruption in the LiDAR scene data. By dividing the entire scene into voxels to facilitate the processes of diffusion and reverse diffusion, LiDARPure overcomes challenges induced from adversarial training, such as sparse point clouds in large-scale LiDAR data and gradient confusion. In addition, we utilize the latent geometric features of a scene as a condition to assist the generation of diffusion models. Detailed experiments show that LiDARPure can effectively purify 19 common types of LiDAR data corruption. Further evaluation results demonstrate that it can improve the average precision of 3D object detectors to an extent of 20% in the face of data corruption, much higher than existing defence strategies. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) are the reference architecture to accelerate high-performance computing applications and the training/interference of convolutional neural networks. For both these domains, performance and reliability are two of the main constraints. It is believed that the only way to increase reliability is to sacrifice performance, e.g., using redundancies. We show in this paper that this is not always the case. As a very promising result, we found that most GPUs performance improvements also bring the benefit of increasing the number of executions correctly completed before experiencing a silent data corruption (SDC). We consider four different common GPUs' performance optimizations: architectural solutions, software implementations, compiler optimizations, and threads degree of parallelism. We compare different implementations of a variety of parallel codes and, through beam experiments and applications profiling, we show that the performance improvement typically (but not necessarily) increases the GPU SDC rate. Nevertheless, for the vast majority of the configurations the performance gain is much higher than the SDC rate increase, allowing to process a higher amount of correct data. As we show, the programmer choices can increase up to 25 × the number of correctly completed executions without redesigning the algorithm nor including specific hardening solutions. [ABSTRACT FROM AUTHOR]

In clinical datasets, missing data often occur due to various reasons including non-response, data corruption, and errors in data collection or processing. Such missing values can lead to biased statistical analyses, reduced statistical power, and potentially misleading findings, making effective imputation critical. Traditional imputation methods, such as Zero Imputation, Mean Imputation, and k-Nearest Neighbors (KNN) Imputation, attempt to address these gaps. However, these methods often fall short of accurately capturing the underlying data complexity, leading to oversimplified assumptions and errors in prediction. This study introduces a novel Imputation model employing transformer-based architectures to address these challenges. Notably, the model distinguishes between complete EEG signal amplitude data and incomplete data in two datasets: PhysioNet and CHB-MIT. By training exclusively on complete amplitude data, the TabTransformer accurately learns and predicts missing values, capturing intricate patterns and relationships inherent in EEG amplitude data. Evaluation using various error metrics and R2 score demonstrates significant enhancements over traditional methods such as Zero, Mean, and KNN imputation. The Proposed Model achieves impressive R2 scores of 0.993 for PhysioNet and 0.97 for CHB-MIT, highlighting its efficacy in handling complex clinical data patterns and improving dataset integrity. This underscores the transformative potential of transformer models in advancing the utility and reliability of clinical datasets. [ABSTRACT FROM AUTHOR]

Currently, there is a growing trend among users to store their data in the cloud. However, the cloud is vulnerable to persistent data corruption risks arising from equipment failures and hacker attacks. Additionally, when users perform file operations, the semantic integrity of the data can be compromised. Ensuring both data integrity and semantic correctness has become a critical issue that requires attention. We introduce a pioneering solution called Sec-Auditor, the first of its kind with the ability to verify data integrity and semantic correctness simultaneously, while maintaining a constant communication cost independent of the audited data volume. Sec-Auditor also supports public auditing, enabling anyone with access to public information to conduct data audits. This feature makes Sec-Auditor highly adaptable to open data environments, such as the cloud. In Sec-Auditor, users are assigned specific rules that are utilized to verify the accuracy of data semantic. Furthermore, users are given the flexibility to update their own rules as needed. We conduct in-depth analyses of the correctness and security of Sec-Auditor. We also compare several important security attributes with existing schemes, demonstrating the superior properties of Sec-Auditor. Evaluation results demonstrate that even for time-consuming file upload operations, our solution is more efficient than the comparison one. [ABSTRACT FROM AUTHOR]

In recent years, deep neural networks have achieved significant success in 3D point cloud recognition tasks. However, these models still demonstrate substantial performance challenges in the presence of data corruption. It is crucial to improve the robustness and generalization ability of the model. In this work, we propose a novel framework that combines GPT and CLIP models to enhance the robustness of existing point cloud classification models, which has two main modules: the Text-Image Fusion Module, which includes a GPT-Driven TextGen Processor and FocalView Projection, and the Dual-Path Intelligent Adapter Module. First, the GPT-Driven TextGen Processor leverages GPT-4’s capabilities to generate detailed textual descriptions tailored to point cloud intricacies. FocalView Projection dynamically selects viewpoints based on attention maps, enhancing two-dimensional representations of three-dimensional point clouds. Secondly, the Dual-Path Intelligent Adapter Module achieves fine-tuning and feature adaptation by combining internal and external adapters. Additionally, during the fine-tuning process, we employ a variant of Projected Gradient Descent (PGD) adversarial training, named VPGD, to increase the model’s resilience to adversarial perturbations. Our approach has achieved state-of-the-art results on robust 3D points cloud recognition datasets such as ModelNet40-C and ScanObjectNN-C. [ABSTRACT FROM AUTHOR]

Corruption poses a significant threat to societies' social and economic fabric, eroding trust and contractual relationships between governments and citizens. This study investigates the repercussions of corruption spillover on individuals and explores how they adjust their motivation to engage in tax cheating by exploiting two distinct types of corruption heterogeneities at both individual and national levels. Leveraging newly collected individual corruption experiences locally from the World Value Survey alongside national‐level corruption data from Transparency International, we employ multilevel hierarchical modeling to estimate the effects of corruption on individual tax behavior. Our findings reveal that corruption negatively impacts people's willingness to comply with tax laws, with individuals who have personally experienced corruption exhibiting a more pronounced erosion of tax morale compared to those who have not. By shedding light on the intricate dynamics of corruption's influence on tax compliance, this paper contributes to our understanding of the broader implications of corruption for people and society. This paper can also inform policymakers and stakeholders in designing targeted interventions to mitigate the adverse consequences of corruption and restore citizens' trust in their government. [ABSTRACT FROM AUTHOR]

In the realm of Ceph distributed storage systems, ensuring swift and symmetrical data recovery during severe data corruption scenarios is pivotal for data reliability and system stability. This paper introduces an innovative FPGA-based Multi-Dimensional Elastic Recovery Acceleration method, termed AMDER-Ceph. Utilizing FPGA technology, this method is a pioneer in accelerating erasure code data recovery within such systems symmetrically. By harnessing the parallel computing power of FPGAs and optimizing Cauchy matrix binary operations, AMDER-Ceph significantly enhances data recovery speed and efficiency symmetrically. Our evaluations in real-world Ceph environments show that AMDER-Ceph achieves up to 4.84 times faster performance compared with traditional methods, especially evident in the standard 4 MB block size configurations of Ceph systems. [ABSTRACT FROM AUTHOR]

Ionizing radiation induces the degradation of electronic systems. For memory devices, this phenomenon is often observed as the corruption of the stored data and, in some cases, the occurrence of sudden increases in current consumption during the operation. In this work, we propose enhanced experimental instrumentation to perform in-depth Single-Event Effects (SEE) monitoring and analysis of electronic systems. In particular, we focus on the Single-Event Latch-up (SEL) phenomena in memory devices, in which current monitoring and control are required for testing. To expose the features and function of the proposed instrumentation, we present results for a case study of an SRAM memory that has been used on-board PROBA-V ESA satellite. For this study, we performed experimental campaigns in two different irradiation facilities with protons and heavy ions, demonstrating the instrumentation capabilities, such as synchronization, high sampling rate, fast response time, and flexibility. Using this instrumentation, we could report the cross section for the observed SEEs and further investigate their correlation with the observed current behavior. Notably, it allowed us to identify that 95% of Single-Event Functional Interrupts (SEFIs) were triggered during SEL events. [ABSTRACT FROM AUTHOR]

Recent advances in quantum crystallography have shown that, beyond conventional charge density refinement, a one‐electron reduced density matrix (1‐RDM) satisfying N‐representability conditions can be reconstructed using jointly experimental X‐ray structure factors and directional Compton profiles (DCP) through semidefinite programming. So far, such reconstruction methods for 1‐RDM, not constrained to idempotency, have been tested only on a toy model system (CO2). In this work, a new method is assessed on crystalline urea [CO(NH2)2] using static (0 K) and dynamic (50 K) artificial experimental data. An improved model, including symmetry constraints and frozen core‐electron contribution, is introduced to better handle the increasing system complexity. Reconstructed 1‐RDMs, deformation densities and DCP anisotropy are analysed, and it is demonstrated that the changes in the model significantly improve the reconstruction quality, even when there is insufficient information and data corruption. The robustness of the model and the strategy are thus shown to be well adapted to address the reconstruction problem from actual experimental scattering data. [ABSTRACT FROM AUTHOR]

The Republic of Azerbaijan public administration holds sensitive data about citizens, including personal and financial information. The increasing frequency and sophistication of cyber-attacks make it essential for public administration to implement robust cyber hygiene practices to protect this data. This article discusses the importance of cyber hygiene in the public administration of Azerbaijan and outlines best practices for ensuring data security. Cyber hygiene similarly refers to practices meant to prevent malware infections, as well as cyber intrusions and data loss or corruption, and maintain a healthy cyber environment. This ensures the health of systems and improves cybersecurity in the same way routine handwashing helps prevent the spread of disease. Given that all organizations use information systems to do business today, all are at risk of exposure to various cyberattacks that can prevent the functioning of information systems or block access to data. Thus, every organization must protect its information system(s), establish procedures and policies, and offer regular training, to establish adequate cyber hygiene practices. [ABSTRACT FROM AUTHOR]

Corruption as a social and cultural epidemic is likely to influence the environmental sustainability and quality of the world we live in, where climate change threatens our survival, both now and in the future. Therefore, in this paper, we use large panel data of 123 countries between 2000 and 2017 to examine the environmental effect of corruption on green growth. Consistent with prior studies and due to the slow-changing nature of corruption, we used the pooled ordinary least square as the primary estimator. We also employ the System-Generalised Method of Moments and Two-Stage Least Square Instrumental Variable analysis to control country-specific effects and simultaneity bias caused by potential endogeneity. The results show a negative and significant relationship between corruption and green growth, suggesting that highly corrupt countries are less likely to improve the environmental consequences of rapid economic growth. Quantitatively, ceteris paribus, a 1% increase in corruption (control of corruption), given its standard deviation, leads to a 15.47% decrease in green growth. This is equivalent to about 0.912 US dollars per kilogram decrease in green growth. In further analyses, we find that the relationship between corruption and green growth is similar in both developed and developing countries implying that no country is immune from the environmental effect of corruption. The findings highlight the need to control corruption to achieve sustainable economic and environmentally friendly development, especially as Agenda 2030 fast approaches. [ABSTRACT FROM AUTHOR]

Security and protection of the data is the core objective of every organization, but since cyber-attacks got more advanced than ever before, the data is compromised more often, resulting in financial loss, life loss, or privacy breaches as its consequences. There must be a system that can deal with the increasing number of cyber-attacks in flight operations, which are increasing in numbers and sophistication. Since we know that the traditional intrusion detection system is not capable enough to protect the data and as many human lives are at stake in flight operations, an unfortunate data corruption attack could give rise to a catastrophe. In this paper, we proposed a blockchain-based intrusion detection system for flight operations framework to protect the data's privacy and avoid data corruption in flight operations. Blockchain not only protects data from corruption but also circumvents the challenges faced by intrusion detection systems which include trust and consensus building between different nodes in a network that can enhance the capability of the intrusion detection system. [ABSTRACT FROM AUTHOR]

Many cloud services are relying on distributed key-value stores such as ZooKeeper, Cassandra, HBase, etc. However, distributed key-value stores are notoriously difficult to design and implement without any mistakes. Because data consistency is the contract for clients that defines what the correct values to read are for a given history of operations under a specific consistency model, consistency violations can confuse client applications by showing invalid values. As a result, serious consequences such as data loss, data corruption, and unexpected behavior of client applications can occur. Software bugs are one of main reasons why consistency violations may occur. Formal verification techniques may be used to make designs correct and minimize the risks of having bugs in the implementation. However, formal verification is not a panacea due to limitations such as the cost of verification, inability to verify existing implementations, and human errors involved. Implementation-level model checking has been heavily explored by researchers for the past decades to formally verify whether the underlying implementation of distributed systems have bugs or not. Nevertheless, previous proposals are limited because their invariant checking is not versatile enough to check for the wide spectrum of consistency models, from eventual consistency to strong consistency. In this work, consistency oracles are employed for consistency invariant checking that can be used by implementation-level model checkers to formally verify data consistency model implementations of distributed key-value stores. To integrate consistency oracles with implementation-level distributed system model checkers, the partial-order information obtained via API is leveraged to avoid the exhaustive search during consistency invariant checking. Our evaluation results show that, by using the proposed method for consistency invariant checking, our prototype model checker, VConMC, can detect consistency violations caused by several real-world software bugs in a well-known distributed key-value store, ZooKeeper. [ABSTRACT FROM AUTHOR]

In this study, we investigate advances in reversible data hiding (RDH), a critical area in the era of widespread digital data sharing. Recognizing the inherent vulnerabilities such as unauthorized access and data corruption during data transmission, we introduce an innovative dual approach to RDH. We use the EMD (Exploiting Modification Direction) method along with an optimized LSB (Least Significant Bit) replacement strategy. This dual method, applied to grayscale images, has been carefully developed to improve data hiding by focusing on modifying pixel pairs. Our approach sets new standards for achieving a balance between high data embedding rates and the integrity of visual quality. The EMD method ensures that each secret digit in a 5-ary notational system is hidden by 2 cover pixels. Meanwhile, our LSB strategy finely adjusts the pixels selected by EMD to minimize data errors. Despite its simplicity, this approach has been proven to outperform existing technologies. It offers a high embedding rate (ER) while maintaining the high visual quality of the stego images. Moreover, it significantly improves data hiding capacity. This enables the full recovery of the original image without increasing file size or adding unnecessary data, marking a significant breakthrough in data security. [ABSTRACT FROM AUTHOR]

This study analyzes the impact of e-governance on corruption using a panel dataset comprising 110 countries over the period from 2003 to 2021. The specific focus of the research is on the relationship between the E-government Development Index (EGDI) and its four components, namely the Online Service Index (OSI), Human Capital Index (HCI), Telecommunication Infrastructure Index (TII), and E-Participation Index (EPI), and their effects on the Corruption Perceptions Index (CPI). To examine these relationships, the empirical analysis employs a fixed effect model, which is a suitable statistical approach for panel data analysis. The findings of the study reveal significant negative associations between the EGDI, OSI, HCI, and TII with corruption levels across countries worldwide. This implies that higher levels of e-government development, online service provision, human capital, and telecommunication infrastructure are associated with lower corruption perceptions. However, the EPI does not demonstrate a statistically significant relationship with the CPI. Based on these results, the paper provides recommendations for governments to prioritize two key areas. Firstly, governments should invest in the development of e-government infrastructure to enhance transparency, accountability, and efficiency in public administration, thereby reducing corruption. Secondly, initiatives aimed at enhancing public awareness and understanding of e-governance should be prioritized, as they contribute to increased citizen engagement and participation, which can ultimately lead to improved governance practices and reduced corruption levels. [ABSTRACT FROM AUTHOR]

Background: The exponential data growth emphasises the importance of efficient information flow in organisations, especially in the financial sector. Data quality significantly influences decision-making, necessitating reliable Data Quality Management (DQM) frameworks. Previous studies propose DQM to maintain data quality through regulation, technology, measurement, evaluation, and improvement. Researchers highlight high-quality data benefits in private organisations but note the lack of improvement in data utilisation in public organisations. In Indonesia, data accuracy and quality are crucial for financial policies, with frequent reports of data inaccuracies in the Directorate General of Customs and Excise (DJBC), demanding standardised DQM practices. However, However, prior studies have yet to provide comprehensive and practical solutions to improve DQM practices. This study therefore aims to measure the DQM maturity, provide recommendations based on best practices, and formulate a practical strategy for improvements along with indicators tailored to the organisation, a topic that previous research has not explored. Methods: This study falls under a mixed method approach (a quantitative study followed by a qualitative study) and employs a three-stage methodology. The authors conduct maturity assessment using Loshin model through an assisted enumeration from 5 key stakeholders followed by recommendations based on the Data Management Body of Knowledge (DMBOK) and strategy formulation from internal documents and interview. Results: The data analysis yielded a DQM maturity score of 3.10, indicating a "defined to managed" level of maturity. Among eight components, only one receives a Managed level, two components are in the Defined level and the rest belongs to a Repeatable level. This study also proposes three strategies to bolster DQM by targeting 49 weak points, which will be progressively and sequentially implemented over a three-year period, using twelve possible solutions. Conclusion: The study highlights the importance of efficient data flow, particularly in the financial sector, and suggests DQM for maintaining data quality. DJBC's import DQM level is assessed using Loshin's measurements, revealing areas for improvement through key DMBOK activities. Recommendations include data governance, strategic planning, and sequential DQM implementation. The study concludes by formulating a practical approach to be applied in a three-year span with ten indicators to measure success. [ABSTRACT FROM AUTHOR]

The empirical application of polarization and depolarization current (PDC) measurement of transformers facilitates the extraction of critical insulation-sensitive parameters. This technique, rooted in time-domain dielectric response analysis, forms the bedrock for parameterization and insulation modeling. However, the inherently time-consuming nature of polarization current measurements renders them susceptible to data corruption. This article explores deep-learning-based short-duration techniques for forecasting polarization current to address this limitation. By incorporating spatial shortcuts, the residual long short-term memory (LSTM) network facilitates the seamless propagation of spatial and temporal gradients. Furthermore, the relative forecasting assessment of the proposed residual LSTM model's performance is made against traditional LSTM, attention LSTM, gated recurrent units (GRU), and convolutional neural network (CNN) models. Thus, optimal model selection strategies are evaluated based on their capability to capture extended dependencies and short-term information present in the data. In addition, the Monte Carlo dropout prediction is employed to estimate uncertainty in polarization current forecasts. The findings demonstrate that the proposed residual LSTM network model for polarization current forecasting yields the lowest error metrics and maintains prediction consistency over the testing duration. Thus, the proposed approach significantly reduces PDC measurement time, providing an effective means to develop proactive maintenance strategies for evaluating the insulation condition of transformers. [ABSTRACT FROM AUTHOR]

This study aims to examine the effects of corruption perception and cultural dimensions on national innovation in international societies. To achieve this, data from the Corruption Perception Index (CPI) provided by the non-governmental organization Transparency International, information on Hofstede's Cultural Dimensions, and data available in the Global Innovation Index (GII) reports were utilized for a total of 59 countries spanning from 2010 to 2022. The panel regression method was employed, with national innovation as the dependent variable and corruption perception and cultural dimensions as independent variables. The findings indicate that in societies where there is a higher perception of corruption, i.e., where fewer corrupt acts are tolerated, there are better national innovation indices. Additionally, countries characterized by lower power distance, greater individualism, lower uncertainty avoidance, higher long-term orientation, and greater indulgence exhibit superior national innovation indices. These findings contribute to various market stakeholders by signaling interactions on how corruption can negatively influence innovation generation within a society, and how intrinsic cultural aspects of these nations impact this innovation index, which may represent a country's level of competitiveness on a global scale. The results are relevant as they provide insights for policymakers regarding the effects of corrupt practices on innovation, which in turn affects the country's competitive image in international markets. [ABSTRACT FROM AUTHOR]

The purpose of this research is to provide a valuable study about the development of literature on corruption prevention during the last decade suggesting the way for the future agenda. We collected documents with the aims to develop a structured literature review (SLR). We collected 113 documents adopting a not grey analysis. Our results shows two main research areas: (i) the corruption prevention understanding its features and using accounting and ethical activities in the business and government procurement and (ii) anticorruption measures, disclosure and behaviours as renewed tools to increase corporate governance sustainability and corporate social responsibility. This is the first time for proposing the SLR on corporate corruption preventions and renewed models directed to build an updated background and emerging issues. This paper is original because proposes for the first time the literature collection of corporate corruption prevention's models providing valuable insights, critique and definition for the future research. [ABSTRACT FROM AUTHOR]

Corruption is widely believed to contribute to homelessness following a disaster triggered by natural hazards, as it is known to weaken resilience, deplete resources and stifle recovery. We test the association between corruption and post‐disaster homelessness using data from the Emergency Events Database and data on corruption in the construction sector from the World Bank's Enterprise Surveys. Using instrumental variables and other estimation techniques, we find that corruption is associated with significantly more people left homeless following disasters. Corruption in the construction sector appears to be driving this result. These findings underline the importance of tackling corruption and poor construction practices in vulnerable communities. [ABSTRACT FROM AUTHOR]

Information from multiple data sources is increasingly available. However, some data sources may produce biased estimates due to biased sampling, data corruption or model misspecification. Thus there is a need for robust data combination methods that can be used with biased sources. In this paper, a robust data fusion-extraction method is proposed. Unlike existing methods, the proposed method can be applied in the important case where researchers have no knowledge of which data sources are unbiased. The proposed estimator is easy to compute and employs only summary statistics; hence it can be applied in many different fields, such as meta-analysis, Mendelian randomization and distributed systems. The proposed estimator is consistent, even if many data sources are biased, and is asymptotically equivalent to the oracle estimator that uses only unbiased data. Asymptotic normality of the proposed estimator is also established. In contrast to existing meta-analysis methods, the theoretical properties are guaranteed for our estimator, even if the number of data sources and the dimension of the parameter diverge as the sample size increases. Furthermore, the proposed method provides consistent selection for unbiased data sources with probability approaching 1. Simulation studies demonstrate the efficiency and robustness of the proposed method empirically. The method is applied to a meta-analysis dataset to evaluate surgical treatment for moderate periodontal disease and to a Mendelian randomization dataset to study the risk factors for head and neck cancer. [ABSTRACT FROM AUTHOR]

Much of the empirical research on corruption for the past 45 years has focused on perception-based definitions and measurements. Citizens' perceptions, their attitudes and (self)reported experiences of corruption have been widely studied through different perception-based measures obtained in surveys, interviews, and experiments applied to citizens in general, and experts, business leaders, politicians, or public officials. Notwithstanding the significant progress made to understand the complexity of citizens' understandings, judgements and practices, we are still unable to decipher by what criteria they establish what is or is not corruption and what types of corruption are susceptible of being condemned/tolerated. This paper makes an innovative contribution to fill this gap. We propose a methodological design to identify and measure different perception-based definitions of corruption based on two contrasting normative perspectives: deontological and consequentialist ethics. We identified four groups: the Virtuous; the Intransigent; the Pragmatic; and the Hypocrite. Using survey data from a national sample of Portuguese citizens, we employ discriminant analysis and logistic regression models to differentiate individual profiles in terms of process- and outcome-based social definitions of corruption and explore the explanatory factors that account for these different conceptualisations and their different degree of tolerance towards corruption. [ABSTRACT FROM AUTHOR]

Background: Public health emergencies raise significant concerns about corruption and accountability; however, these concerns can manifest in different ways across diverse locations. For instance, more developed countries with a stronger rule of law may experience more corruption in vaccine procurement, whereas developing countries may experience more corruption at the point of distribution and delivery to end users. This research focuses on corruption concerns in Nigeria, specifically examining the procurement and distribution of COVID-19 vaccines. Methods: This paper utilizes a scoping review and a qualitative research approach. Key informants (n = 40) involved in the procurement and distribution of COVID-19 vaccines across two states in Nigeria were interviewed. Findings from the scoping review were summarized, and collected data were inductively coded and analysed in themes, revealing clear examples of implementation irregularities and corruption in the country's COVID-19 vaccination processes. Results: Vaccination programme budgeting processes were unclear, and payment irregularities were frequently observed, resulting in vaccinators soliciting informal payments while in the field. Recruitment and engagement of vaccination personnel was opaque, while target vaccination rates incentivized data falsification during periods of vaccine hesitancy. Accountability mechanisms, such as health worker supervision, vaccination data review, and additional technical support provided by donors were implemented but not effective at preventing corruption among frontline workers. Conclusions: Future accountability measures should be evidence-driven based on findings from this research. Personnel recruitment, contracting, budgeting, and remuneration should focus on transparency and accountability. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to measure and investigate the determinants of corruption across Indian states. Design/methodology/approach: This research begins by developing a corruption index (CI) based on official data on corruption cases. Second, the authors also create an adjusted corruption index (ACI) using the stochastic frontier modelling approach to address corruption case under-reporting. Third, they use a panel feasible generalised least square (FGLS) technique to discover corruption determinants. Findings: The results show that approximately 77% of corruption cases in India go under-reported, which is a major concern. The results also show that per capita income, government spending, law and order and urbanisation are the important factors affecting corruption at the regional level. Practical implications: The findings of the study emphasise the need to address the huge under-reporting of corruption data. From a policy perspective, the governments need to emphasise factors like per capita income, government spending, law and order and urbanisation to tackle corruption in India. Originality/value: Corruption is a complex global phenomenon, and several studies have conducted detailed research on the causes of corruption at all levels (regional and cross national), but this study differs from previous studies in the following ways. First, the authors used a more objective measure of corruption by developing a CI at the state level. Second, the study uses stochastic frontier analysis, which is novel in the literature on corruption analysis, to address the most critical component of under-reporting in corruption data. Finally, the study attempts to examine the factors of corruption at the regional level, which is again innovative in nature. [ABSTRACT FROM AUTHOR]

Recently, medical records have been stored and shared locally, which creates a risk of data loss or corruption; these records may be stored and shared through cloud-based central data centers; nonetheless, this strategy has disadvantages, such as the need for significant storage space and the privacy concerns associated with network-wide data sharing. Medical records require increased security and confidentiality, furthermore, these records must be protected when transmitted to and shared with doctors of the same specialization, to solve this problem, blockchain technology was used as a decentralized technology that offers a secure and immutable way to store and protect information. In this paper, a website is designed that uses blockchain technology to save medical records and employs smart contract technology in the ethereum blockchain to govern the creation and display of files and sharing. An interplanetary file system was used to offer a mechanism for the decentralized storage of medical images and reports while maintaining their accessibility on a global scale, it is available only for doctors who have the authority to access them, and the proposed system proved efficient in saving and sharing medical records with high security and less cost. [ABSTRACT FROM AUTHOR]

The research focuses on bank capitalization and macroeconomic stability, including corruption and the shadow economy. A well-capitalized banking system and a low corruption level are important for maintaining macroeconomic stability and reducing the size of the shadow economy. The paper is aimed at assessing the relationship between bank capitalization and macroeconomic stability, which includes corruption and shadow economy through canonical analysis. The research is conducted on the basis of financial and economic reporting of 35 countries with different levels of socio-economic development during 2010–2021 based on data from the World Bank and the European Central Bank. The main input blocks macroeconomic stability – corruption – shadow economy are characterized. During the research, the following tools were used: – methods of grouping, analysis and comparison in determining the characteristics of the elements of the chain «level of capitalization of the banking system – macroeconomic stability – corruption – shadow economy»; – factor analysis by applying the method of principal components in the selection of statistically significant indicators of the bank capitalization level and macroeconomic stability. The quantitative assessment of the bank capitalization level revealed the following dependencies: there is a strong correlation between return on assets and the level of non-performing loans, return on assets and return on equity. Using the principal components method, the following statistically significant indicators of macroeconomic stability were identified: GDP, Gini index, corruption perception index, corruption control index, and political stability index. Research, based on the canonical analysis, determined that 71.1 % of changes in macroeconomic stability indicators are explained by fluctuations in the level of bank capitalization. The level of non-performing loans has a negative impact on macroeconomic stability, while the volatility of return on assets and return on equity has a greater positive impact on the development of the country’s economy. The obtained results can be used by banks in the development of their resource and management policies, in the analysis of the volatility of capitalization level, by state bodies in the development of national policies of the country’s economic development. [ABSTRACT FROM AUTHOR]

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results. The phenotype-genotype reference map (PGRM) is a curated set of GWAS associations that facilitates efficient phenome-wide replication studies with biobank data. The PGRM can be used to detect data corruption, assess model parameters, and explore factors that affect replicability of findings. [ABSTRACT FROM AUTHOR]

In the space environment, cosmic rays and high-energy particles may cause a single-event upset (SEU) during program execution, and further cause silent data corruption (SDC) errors in program outputs. After extensive research on SEU and SDC errors, it has been found that SDC errors in the routing program in satellite networks may lead to the emergence of Sinkhole (SH) and Grayhole (GH) nodes in the network, which may cause damage to satellite networks. To find and solve the problems in time, a digital-twin-based detection and protection framework for SDC-induced SH and GH nodes in satellite networks is proposed. First, the satellite network fault model under SEU and the generation mechanism of SH and GH nodes induced by SDC errors are described. Then, the data structure based on digital twins required by the proposed detection and protection framework is designed, and the detection methods of SH and GH nodes induced by SDC errors are proposed. SKT and LLFI simulation tools are used to build a simulated Iridium satellite network and carry out fault injection experiments. Experiment results show that the accuracy of the proposed detection method is 98–100%, and the additional time cost of routing convergence caused by the proposed framework is 3.1–28.2%. Compared with existing SH and GH detection methods, the proposed methods can timely and accurately detect faults during the routing update stage. [ABSTRACT FROM AUTHOR]

Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods. [ABSTRACT FROM AUTHOR]

Multi-classification of corrupted matrix data is a significant problem in machine learning and pattern recognition. However, most of the existing methods can only handle the clean data or the corrupted data with the know statistical information of noises. Besides, they usually reshape the matrix data into a vector as the input, very likely to destroy the structure of the raw data, thereby reducing the model performance. In order to address the above issues, a general robust low–rank multinomial logistic regression is proposed for corrupted matrix data. The proposed approach has three outstanding merits as follows: (1) by using the multinomial logistic regression combined with three regularization terms corresponding to matrix structure, low–rank and sparsity, the clean data recovery and the classification are simultaneously fulfilled; (2) the proposed method can adapt to more general corrupted matrix data since it does not require strong statistical assumptions about noise, and (3) the theoretical analysis is provided to show the convergence of the proposed multi-block ADMM algorithm, and such a convergence can be rigidly guaranteed by introducing two auxiliary variables such that the coefficients of the equality constraints are orthogonal. Finally, extensive experimental results have demonstrated the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization's 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver's impairment level by monitoring the driver's blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model. [ABSTRACT FROM AUTHOR]

Reliability of Static Random Access Memories (SRAMs) is immensely affected by radiation induced soft errors. Multiple Cell Upsets (MCUs) are caused by these soft errors and this lead to data corruption in several SRAM cells. Error Correcting Codes are normally exploited to alleviate the effect of MCUs. Single Error Correction–Double Error Detection (SEC–DED) codes are not the appropriate preference against MCUs rather these codes are suitable for protecting SRAM cells against Single Cell Upset. Single Error Correction–Double Error Detection–Double Adjacent Error Correction (SEC–DED–DAEC) codes are more suitable due to increasing trends of single and double-adjacent errors in SRAM cells. In this paper, a new SEC–DED–DAEC code has been proposed which is proficient of correcting single and double-adjacent errors in SRAMs. Our newly proposed code has been termed as Single and Double-adjacent Error Correcting Code (SDECC). Proposed codecs have been designed by employing new parity check matrices for different word lengths which are frequently applied in memory. The mis-correction probability of proposed SDECC codes are up to 88.24% and 80.67% lesser for 2-random and 3-random bit errors respectively with respect to related existing designs. Proposed codecs have been replicated and synthesized in ASIC environment. It is observed that area and power consumption of the proposed codecs are reduced up to 8.93% and 21.47% compared to the recently published results respectively. [ABSTRACT FROM AUTHOR]

We consider median of means (MOM) versions of the Stahel–Donoho outlyingness (SDO) [ 23 , 66 ] and of the Median Absolute Deviation (MAD) [ 30 ] functions to construct subgaussian estimators of a mean vector under adversarial contamination and heavy-tailed data. We develop a single analysis of the MOM version of the SDO which covers all cases ranging from the Gaussian case to the |$L_2$| case. It is based on isomorphic and almost isometric properties of the MOM versions of SDO and MAD. This analysis also covers cases where the mean does not even exist but a location parameter does; in those cases we still recover the same subgaussian rates and the same price for adversarial contamination even though there is not even a first moment. These properties are achieved by the classical SDO median and are therefore the first non-asymptotic statistical bounds on the Stahel–Donoho median complementing the |$\sqrt{n}$| -consistency [ 58 ] and asymptotic normality [ 74 ] of the Stahel–Donoho estimators. We also show that the MOM version of MAD can be used to construct an estimator of the covariance matrix only under the existence of a second moment or of a scatter matrix if a second moment does not exist. [ABSTRACT FROM AUTHOR]

Deep convolutional networks are vulnerable to image translation or shift, partly due to common down-sampling layers, e.g., max-pooling and strided convolution. These operations violate the Nyquist sampling rate and cause aliasing. The textbook solution is low-pass filtering (blurring) before down-sampling, which can benefit deep networks as well. Even so, non-linearity units, such as ReLU, often re-introduce the problem, suggesting that blurring alone may not suffice. In this work, first, we analyse deep features with Fourier transform and show that Depth Adaptive Blurring is more effective, as opposed to monotonic blurring. To this end, we propose a novel Depth Adaptive Blur-pool (DAB-pool) module to replace existing down-sampling methods. Second, we introduce a novel activation function – with a built-in low pass filter, as an additional measure, to keep the problem from reappearing. From experiments, we observe generalisation on other forms of transformations and corruptions as well, e.g., rotation, scale, and noise. We evaluate our method under three challenging settings: (1) a variety of image translations; (2) adversarial attacks – both ℓ p bounded and unbounded; and (3) data corruptions and perturbations. In each setting, our method achieves state-of-the-art results and improves clean accuracy on various benchmark datasets. [ABSTRACT FROM AUTHOR]

Introduction. In the periods from 2014 to 2019 and from 2019 to 2022, Ukraine implemented various economic and social policies that resulted in diverse socio-economic consequences. This article aims to conduct a comparative analysis of specific aspects of social and economic policy during these periods. Analysis of scientific research. Research on specific theoretical and practical aspects of state economic and social policy implementation in Ukraine during the periods from 2014 to 2019 and from 2019 to 2022 has been conducted by prominent scientists including S. V. Mocherny, Yu. S. Shemchushenko, O. P. Orlyuk, and others. The examination of the socio-economic consequences resulting from the implementation of economic and social policies in Ukraine during the periods from 2014 to 2019 and from 2019 to 2022 remains insufficiently explored, necessitating comprehensive scientific research in this field. The purpose of this scientific article is to investigate specific aspects of economic and social policy implemented in Ukraine during the periods from 2014 to 2019 and from 2019 to 2022. The main tasks of the article are to provide a theoretical and mathematical description of specific aspects of economic and social policy during the periods from 2014 to 2019 and from 2019 to 2022, as well as to analyze their consequences in Ukraine. Research methodology and methods. During the writing of the article, several special research methods were employed. These methods include direct observation, which involved gathering information on various indicators such as the number of points in the Corruption Perception Index (CPI) in Ukraine from 2013 to 2022, changes in real gross domestic product (GDP) from 2016 to 2020, the amount of state and state-guaranteed debt from January 2016 to January 2022, and Ukraine's position in the democracy rating from 2014 to 2022. Information processing methods were used to develop tables for calculating the growth and changes in the number of points in the CPI in Ukraine from 2013 to 2022, as well as the amount of state and state-guaranteed debt from January 2016 to January 2022. Graphical analysis was conducted to examine the dynamics of the CPI in Ukraine from 2013 to 2022, changes in GDP from 2016 to 2021, the size of the state and state-guaranteed debt from January 2016 to January 2022, and Ukraine's position in the democracy rating from 2014 to 2021. The time range for analyzing changes in GDP and the national debt was chosen by the author to minimize the impact of the acute phases of the conventional Russian-Ukrainian war during 2014-2015 and after February 24, 2022. Similarly, the time range for the CPI from 2013 to 2022 was selected to demonstrate that the conventional war does not have a decisive influence on the fight against corruption. Additionally, the time range for Ukraine's position in the Democracy Rating from 2014 to 2021 was chosen to mitigate the impact of the large-scale conventional Russian-Ukrainian war that began on February 24, 2022 Research results. According to the data from the Corruption Perception Index, it was found that effective measures were taken by the Ukrainian authorities to combat corruption during the period of 2014-2018. However, from 2019 to 2023, the progress in the fight against corruption slowed down. The analysis also demonstrates that following the cessation of active hostilities that took place between 2014 and 2015, the economic policies implemented by the state authorities facilitated consistent economic growth for sixteen quarters, spanning from 2016 to 2019. However, after a change in power occurred in 2019, economic growth shifted from positive to negative starting from 2020. During the period of 2016-2019, the government's economic policies not only focused on fostering economic growth but also aimed at reducing the external debt burden on the economy. Conversely, following the change in power in 2019, the new leadership pursued an active policy of foreign borrowing, resulting in an increase in the debt burden on the Ukrainian economy. Scientific novelty of the research results. A comparative mathematical analysis was conducted to examine specific aspects of state economic and social policies in Ukraine during the periods from 2014 to 2019 and from 2019 to 2022. Practical significance of the obtained research results. The author believes that conducting a qualitative comparative mathematical analysis of specific aspects of state economic and social policy in Ukraine during the periods from 2014 to 2019 and from 2019 to 2022 will facilitate the identification of more effective methods for implementing economic and social policies. This, in turn, will contribute to the establishment of an independent and efficient state [ABSTRACT FROM AUTHOR]