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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

As graphics processing units (GPUs) evolve to offer high performance for general-purpose computations in addition to inherently fault-tolerant graphics applications, soft error reliability becomes a significant concern. Fault injection provides a method of evaluating the soft error vulnerability of target programs. Since performing fault injection experiments for complex GPU hardware structures takes impractical times, the prediction-based techniques to evaluate the soft error vulnerability of general-purpose GPU (GPGPU) programs based on metrics from different domains get crucial for both HPC developers and GPU vendors. In this work, we propose machine learning (ML)-based prediction frameworks for the soft error vulnerability evaluation of GPGPU programs. We consider program characteristics, hardware usage and performance metrics collected from the simulation and the profiling tools. While we utilize regression models to predict the masked fault rates, we build classification models to specify the vulnerability level of the GPGPU programs based on their silent data corruption (SDC) and crash rates. Our prediction models achieve maximum prediction accuracy rates of 95.9, 88.46, and 85.7% for masked fault rates, SDCs, and crashes, respectively. [ABSTRACT FROM AUTHOR]

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Performance-based evaluation criteria (PBEC) are vital for selecting high-quality suppliers and achieving a PPP procurement performance. Through theoretical and institutional analysis, we found that the selection of PBEC centered on operations depends on the discretion of the purchaser. However, in an emerging and transforming PPP market, many factors have affected the scientific exercise of the purchaser's discretion. This means that PPP projects must focus on construction and neglect operation in a certain period. Furthermore, to explore the influencing factors of the definition of PBEC, based on data of 9082 PPP projects between 2009 and 2021 in China, we adopted Ordinary Least Squares to empirically analyze two factors that influence the level of attention that is paid to the operation plan: corruption and accountability. The results indicate that the attention paid to the operation plan significantly increased with the reduction in corruption and the improvement in accountability. Robustness tests demonstrate the robustness of the results. A further heterogeneity analysis shows that the above factors have a more significant impact on non-state demonstration projects and projects with large investments. The contributions of this study are as follows: (1) Theoretically, this paper supplements the relevant research on evaluation criteria and provides new evidence on the impact of corruption and accountability on the defining PBEC. (2) Institutionally, it provides specific paths to limit the discretion of procurement officials when defining evaluation criteria. (3) In practice, it helps procurement officials to scientifically define PBEC and promote the realization of procurement performance. [ABSTRACT FROM AUTHOR]

The article explains the background and rising prevalence of data breaches and gives a brief overview of the existing legislation for data breach security, protection, and notification in the U.S. Topics include court case McMorris v. Carlos Lopez & Assocs., LLC on threefactor test; the U.S. Supreme Court case TransUnion LLC v. Ramirez on data breaches; and the role of Federal Trade Commission (FTC) Act in addressing the same.

Data-driven remaining useful life (RUL) prediction plays a vital role in modern industries. However, unpredictable corruption may occur in the collected sensor data due to various disturbances in the real industrial conditions. To achieve better RUL prediction performance under this situation, we propose a novel multi-task method for RUL prediction, which is named multi-task deep long short-term memory (MTD-LSTM). In MTD-LSTM, convolutional neural network (CNN) and long short-term memory (LSTM) are first employed for feature extraction and fusion. Then, the extracted features are fed into the multi-task learning module, which contains missing value imputation and RUL prediction module. The missing values imputation task and RUL prediction task are performed simultaneously. The purpose of the missing value imputation is to obtain integral degradation information by recovering the complete data; thus, the RUL prediction task performs better under corrupted sensor data. In addition, a novel loss term is proposed to smooth the RUL prediction results without any manual post-processing. The effectiveness of the proposed method is verified on the simulated dataset based on the C-MAPSS dataset. [ABSTRACT FROM AUTHOR]

Low‐rank matrices play a central role in modelling and computational methods for signal processing and large‐scale data analysis. Real‐world observed data are often sampled from low‐dimensional subspaces, but with sample‐specific corruptions (i.e. outliers) or random noises. In many applications where low‐rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. The authors aim to recover a low‐rank component from incomplete and indirect observations and correct the possible errors. A new low‐rank matrix recovery formula based on generalised Tikhonov regularisation and its solution algorithm are proposed. The proposed method determines the low‐rank component for performing matrix recovery from highly corrupted observations. The authors' recommended algorithm reduces not only the outliers but also random corruptions in the recovering task. The experimental results obtained using both synthetic and real application data demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Public corruption in the government procurement process is rampant and its cost is huge, even among developed countries. Some scholars estimate that about 20%-30% of the values of government projects are lost due to public corruption. In this paper, we examine how public corruption impacts the allocation of U.S. federal contracts. Using the U.S. Department of Justice corruption convictions data and the federal contract data from 2000 to 2018, we find that firms located in more corrupt states receive more federal contract dollars, more important contracts in terms of their contributions to firm revenues, and contracts with higher visibility among federal contractors. We construct an influence/favoritism index that takes into account defense contracts, cost- plus contracts, and multi- year contracts, and document that the index is positively related to corruption levels. These results hold after we conduct several robustness tests, including 2SLS regressions, propensity- score matching analysis, and using alternative corruption measures. Our empirical findings are consistent with the hypothesis that corruption plays an important role in how federal contracts are allocated. [ABSTRACT FROM AUTHOR]

Does regional corruption exacerbate regional carbon emissions? To answer this, based on the spatial Durbin model, this study empirically examines the impact of regional corruption on carbon emission, using panel data from 30 provinces in China during the period 2002–2017. The results show that: (1) there is an indistinctive N-shaped relationship between regional corruption and carbon emissions at the national level. Regional corruption tends to initially aggravate carbon emissions, then contributes to emission reduction, and then finally boosts carbon emissions. However, this effect is not statistically significant. The results suggest that the role of regional corruption on carbon emissions is twofold. Corruption can exacerbate and can also inhibit regional carbon emissions. (2) Pronounced regional heterogeneity exists with regard to the influence of corruption on carbon emissions. Regional corruption and carbon emissions show a significant N-shaped dynamic relationship in China's central region, while the relationship is not significant in the eastern and western regions. (3) The impact of regional corruption on carbon emissions varies with time. For 2002–2009, regional corruption did not have a significant effect on carbon emissions. For 2010–2017, the direct effect became significant, and an apparent N-shaped relationship formed between regional corruption and carbon emissions. Based on the empirical results, this paper proposes several policy recommendations regarding corruption and carbon governance. [ABSTRACT FROM AUTHOR]

Perceived government corruption is an important indicator of depressive symptoms. Recent studies have explored the relationship between perceived government corruption and depressive symptoms in a cross-cultural context, but the underlying mechanisms need further research. This paper examines the impact of perceived government corruption on depressive symptoms in China and the moderating role of social status. Based on the 2018 wave of China Family Panel Studies (CFPS2018), 14,116 respondents aged between 16 and 96 were selected. The results revealed: (1) Perceived government corruption was significantly positively correlated with depressive symptoms. (2) Social class had an inhibitory effect in moderating the relationship between perceived government corruption and depressive symptoms. (3) The moderating effect was only significant for respondents who received education between junior high school and a bachelor's degree. The findings provide policy implications for developing countries and transitional societies like China. To build a more psychologically healthy society, we need to strengthen anti-corruption, stimulate social mobility, and improve people's sense of gain in the future [ABSTRACT FROM AUTHOR]

The article presents the data science approach of network analysis applied to the study of organized crime and corruption. The scientific and intellectual contributions of network studies and their potential usefulness in the field of criminal matters are highlighted. Keywords such as criminology, organized crime, network analysis, corruption, control, and dismantling are mentioned. Complex criminal network studies have used data science to analyze cases of Italian and American mafias involved in white-collar crimes, fraud, political conspiracies, and money laundering. In addition, network analysis has been applied to the study of corruption practices in public procurement processes. The lack of development of theories and strategies for crime and corruption control based on these studies is emphasized. Complex network analysis offers new perspectives for the study of crime and corruption, but it is necessary to establish bridges between disciplines and develop metrics that guide policies and investigative procedures. The text presents studies on network analysis applied to criminal and corruption activity. It also addresses network analysis in the context of public procurement. Previous studies that have identified the concentration and diversification of companies, as well as the structure and dynamics of networks, are mentioned. It is concluded that the larger the dominant group in an individual contracting market, the more likely acts of corruption within those groups will be investigated. Furthermore, the potential of network analysis in criminal theory and professional practice, especially in environmental criminology, is discussed. The text addresses the use of complex network analysis in the study of crime and corruption. Different methods used in these studies, such as anthropology, ethnography, and deviation models, are mentioned. It is highlighted that there is no theory that encompasses the complexity of the phenomenon, and the importance of creating predictive theories in criminology is emphasized. The selection of the data sample is also mentioned as key in complex network analysis, and the lack of access to information due to information concealment by actors and lack of transparency in governments is noted. The usefulness of these analyses as evidence in judicial processes is highlighted, and different data sources used in these studies are mentioned. Complex network analysis applied to crime and corruption groups is useful in providing knowledge and innovative methods. However, there is still a lack of sufficient studies focusing on providing law enforcement and policing actions with methodologies and strategies derived from complex network analysis. It is necessary to prove that these theories and strategies reduce crime and control the environment, without falling into the error of a theory that fits all problems. Complex network analysis is not a solution for all types of crime, but it can be useful in group or associative actions, such as organized crime, when combined with other compatible methods and theories. The document titled "Topology, robustness, and structural controllability of the Brazilian federal police criminal intelligence network" analyzes the topology, robustness, and structural controllability of the criminal intelligence network of the Brazilian Federal Police. Other documents mentioned in the text address topics such as the elimination of corruption in the public procurement process, the structure and dynamics of corruption networks extracted from deferred prosecution agreements, and the closure of networks and integration in the 20th-century American mafia. Studies on the detection of criminal organizations based on police data and corruption in organized crime in Chicago are also mentioned. The document provides a list of bibliographic references related to the topic. [Extracted from the article]

Objective: This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal. Methods: we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large ($\mathbf{N}\ = \ 385\ \text{subjects}$). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT). Results: Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement ($\mathbf{SN}{\mathbf{R}_{\mathbf{imp}}}$) and lower mean squared error ($\mathbf{MSE}$) when compared with that of the three previous methods (averaged $\boldsymbol{S\!N\!R}_\text{imp} = 35.25\,{\text{dB}}$ , and ${\boldsymbol{M\!S\!E\ }} = 0.028$ on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68. Conclusion: The results presented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails. Significance: Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data. [ABSTRACT FROM AUTHOR]

When faced with the issue of different feature distribution between training and test data, the test data may differ in style and background from the training data due to the collection sources or privacy protection. That is, the transfer generalization problem. Contrastive learning, which is currently the most successful unsupervised learning method, provides good generalization performance for the various distributions of data and can use labeled data more effectively without overfitting. This study demonstrates how contrast can enhance a model’s ability to generalize, how joint contrastive learning and supervised learning can strengthen one another, and how this approach can be broadly used in various disciplines.

The edge computing paradigm has recently drawn significant attention from industry and academia. Due to the advantages in quality-of-service metrics, namely, latency, bandwidth, energy efficiency, privacy, and security, deploying artificial intelligence (AI) models at the network edge has attracted widespread interest. Edge-AI has seen applications in diverse domains that involve large amounts of data. However, poor dataset quality plagues this compute regime owing to numerous data corruption sources, including missing data. As such systems are increasingly being deployed in mission-critical applications, mitigating the effects of corrupted data becomes important. In this work, we propose a strategy based on data imputation using neural inversion, DINI. It trains a surrogate model and runs data imputation in an interleaved fashion. Unlike previous works, DINI is a model-agnostic framework applicable to diverse deep learning architectures. DINI outperforms state-of-the-art methods by at least 10.7% in average imputation error. Applying DINI to mission-critical applications can increase prediction accuracy to up to 99% (F1 score of 0.99), resulting in significant gains compared to baseline methods. [ABSTRACT FROM AUTHOR]

High-dimensional data that often lie in low-dimensional subspaces are ubiquitous in many fields of signal and image processing, pattern recognition, machine learning, etc. Finding sparse representations of data points in a dictionary built by using the collection of data helps to study low-dimensional subspaces. In this paper, we consider the problem of subspace-sparse representation for data composed of two distinct features. Applying different measures to the coefficients of the two distinct features under different dictionaries makes the model used in this paper more general. We present theoretical guarantee for subspace-sparse representation under conditions on the subspaces and data. The program used in this paper can handle data with structured corruption, missing entries, sparse outliers and random bounded noise well. [ABSTRACT FROM AUTHOR]

Energy harvesting systems have emerged as an alternative to battery-operated Internet of Things (IoT) devices. To deal with frequent power outages in the absence of battery, energy harvesting systems rely on a capacitor-backed checkpoint mechanism also known as just-in-time (JIT) checkpointing. It checkpoints volatile data in nonvolatile memory (NVM) just before a power outage occurs—using the energy buffered in the capacitor—and restores the checkpointed data from NVM in the wake of the outage. While the JIT checkpointing gives an illusion that volatile data survive a power outage as if they were nonvolatile, it turns out that due to capacitor degradation, energy harvesting systems can unexpectedly fail the JIT checkpointing, losing or corrupting data across the outage. To address the problem, this article presents an operating system-driven solution called CapOS. At a high level, CapOS diagnoses the capacitor in a reactive yet safe manner. When the JIT checkpoint failure occurs, CapOS detects the capacitor degradation without causing the data corruption. To recover from such a capacitor error, CapOS electrically isolates the degraded capacitor—so that it restores its original capacitance by itself with the help of capacitor’s resilient nature—and disables the JIT checkpointing. In case, power outages occur during the capacitor isolation, CapOS leverages undo logging with interval-based checkpointing for their recovery. Once the capacitor is fully recovered, CapOS gets back to the capacitor-based JIT checkpointing. The experimental results demonstrate that CapOS can effectively address the capacitor error of energy harvesting systems at a low run-time cost, without compromising the recovery of power outages. [ABSTRACT FROM AUTHOR]

Duplicate writes are prevalent in storage systems, originating from data duplication, journaling, and data relocations, etc. As flash-based solid state drives (SSDs) have been widely deployed, duplicate writes can significantly degrade their performance and lifetime. Prior studies have proposed innovative approaches that exploit the address remapping utility inside an SSD to eliminate duplicate writes. However, remap operations modify the logical-to-physical (L2P) address mapping table while the physical-to-logical (P2L) mappings persisted on flash memory remain unchanged. Such inconsistency between L2P and P2L mappings may cause data corruption and has long been a major obstacle to utilize SSD address remapping. In this article, we propose a novel SSD design, called Remap-SSD-LH, that realizes the full potential of SSD address remapping. It provides a remap primitive, which allows the host software and SSD firmware to perform logical writes of duplicate data at almost zero cost. To ensure mapping consistency as well as fast mapping lookups, Remap-SSD-LH employs a local log scheme based on hybrid storage. A local log is maintained for each flash garbage collection unit to record relevant P2L mapping changes induced by remap operations. The logs are stored in small nonvolatile RAM (NVRAM), e.g., capacitor-protected DRAM, and can be destaged to flash memory if NVRAM is full. We verify Remap-SSD-LH on a software SSD emulator with three case studies: 1) intra-SSD deduplication; 2) SQLite journaling; and 3) F2FS cleaning. The experimental results show that Remap-SSD-LH can maximally and efficiently exploit address remapping to improve SSD performance and lifetime. [ABSTRACT FROM AUTHOR]

In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness of a large class of learning algorithms that are formulated as low-rank semi-definite programming (SDP) problems. Traditional formulations use the square loss, which is notorious for being sensitive to outliers. We propose to replace this with more robust noise models, including the $\ell _1$ ℓ 1 -loss and other nonconvex losses. However, the resultant optimization problem becomes difficult as the objective is no longer convex or smooth. To alleviate this problem, we design an efficient algorithm based on majorization-minimization. The crux is on constructing a good optimization surrogate, and we show that this surrogate can be efficiently obtained by the alternating direction method of multipliers (ADMM). By properly monitoring ADMM's convergence, the proposed algorithm is empirically efficient and also theoretically guaranteed to converge to a critical point. Extensive experiments are performed on four machine learning applications using both synthetic and real-world data sets. Results show that the proposed algorithm is not only fast but also has better performance than the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Extensive research efforts are being carried out to evaluate and improve the reliability of computing devices either through beam experiments or simulation-based fault injection. Unfortunately, it is still largely unclear to which extend fault injection can provide an accurate error rate estimation at early stages and if beam experiments can be used to identify the weakest resources in a device. The importance and challenges associated with a timely, but yet realistic reliability evaluation grow with the increase of complexity in both the hardware domain, with the integration of different types of cores in an SoC (System-on-Chip), and the software domain, with the OS (operating system) required to take full advantage of the available resources. In this paper, we combine and analyze data gathered with extensive beam experiments (on the final physical CPU hardware) and microarchitectural fault injections (on early microarchitectural CPU models). We target a standalone Arm Cortex-A5 CPU and an Arm Cortex-A9 CPU integrated into an SoC and evaluate their reliability in bare-metal and Linux-based configurations. Combining experimental data that covers more than 18 million years of device time with the result of more than 176,000 injections we find that both the SoC integration and the presence of the OS increase the system DUEs (Detected Unrecoverable Errors) rate (for different reasons) but do not significantly impact the SDCs (Silent Data Corruptions) rate which is solely attributed to the CPU core. Our reliability analysis demonstrates that even considering SoC integration and OS inclusion, early, pre-silicon microarchitecture-level fault injection delivers accurate SDC rates estimations and lower bounds for the DUE rates. [ABSTRACT FROM AUTHOR]

A Fault Attack (FA) is performed mainly under the data corruption model and poses a threat to security chips. Instruction corruption can enact the same purpose at the behavioral level, which is produced by interfering with the instruction system. Laser Fault Injection (LFI) on program memory during the instruction-fetching process, which we refer to as an instruction-fetching attack, is studied in this paper. This process bears the ability to produce a controllable instruction-fetching fault. Our work shows the implementation of the attack and its specific application case on an 8-bit microcontroller. The main contributions of this paper include: (1) We have mapped the sensitive areas precisely to the faulted instructions via laser injection and implemented controllable instruction tampering. (2) A Collision Fault Attack (CFA) scheme based on instruction-fetching fault is proposed. (3) The impacts of the faulted instructions are fully explored, including the influence on subsequent operations and key recovery. (4) The fault mechanism of the on-chip Flash is further investigated. Instruction-fetching fault means that the controller fetches a tampered instruction from the program memory under external interference, which likely gives rise to an invalid or incorrect operation. The experiment confirms that this specific fault can induce particular types of faults that are different to realize, e.g., the byte-fault model in CFA. The realization, application and mechanism of instruction-fetching fault are discussed in detail. [ABSTRACT FROM AUTHOR]

The decision-making process for attaining Sustainable Development Goals (SDGs) can be enhanced through the use of predictive modelling. The application of predictive tools like deep neural networks (DNN) empowers stakeholders with quality information and promotes open data policy for curbing corruption. The anti-corruption drive is a cardinal component of SDG 16 which is aimed at strengthening state institutions and promoting social justice for the attainment of all 17 SDGs. This study examined the implementation of the SDGs in Nigeria and modelled the 2017 national corruption survey data using a DNN. We experimentally tested the efficacy of DNN optimizers using a standard image dataset from the Modified National Institute of Standards and Technology (MNIST). The outcomes validated our claims that predictive analytics could enhance decision-making through high-level accuracies as posted by the optimizers: Adam 98.2%; Adadelta 98.4%; SGD 94.9%; RMSProp 98.1%; Adagrad 98.1%. [ABSTRACT FROM AUTHOR]