Workarounds in Software Forms: A User Study

Data is an important thing as a base of an analytic or a hypothesis for concluding participant vote data in the Indonesian General Election. The data needs to be processed and secured, so the integrity of the data is in good condition. It also needs to protect the participant voting rights, so the information is correctly displayed. The research problems include creating a system that protects the integrity of election data and creating a system that protects election rights for each voter. Based on the problems, this research discusses a blockchain-based electronic voting information system that would secure the integrity of data and also protecting the participant voting rights in a General Election. The system uses Ethereum as a blockchain with Solidity as a programming language to build a smart contract and is built in Microsoft Windows platform. In this research, consortium blockchain and biometric fingerprint authentication are used as a problem-solving method, and waterfall steps is used as a system development method. The result of this research is a proposed design of the e-voting system. The conclusion based on this research is a blockchain-based e-voting system that secures the integrity of the data in a selection process and ensures protection to each vote right. Index Terms”Authentication; Blockchain; Data; Integrity; E-Voting; System

Mobile crowdsensing (MCS) has a huge potential to provide societal benefits by effectively utilizing the sensing, computing, and networking capability of mobile devices, which have become ubiquitous especially in the developed world. However, many challenges come along with MCS such as energy cost, privacy issues, and the data integrity of crowdsensed data. In this paper, we focus on analyzing the data integrity of mobile crowdsensed data in a user study of 60 people who participate in a crowdsensing campaign that collects barometric pressure data from various locations of our campus. Each set of 20 users runs one of the three different crowdsensing frameworks, one of which, called SENSE-AID, is an energy-efficient framework designed by us. We analyze the characteristics of the data with respect to their integrity. From our analysis, we find that for 90 MCS tasks there is a surprisingly high number of outlier percentage (close to 20%), only about 10% of the participants report data for the entire duration of 7 days of the MCS campaign, and using a reputation system to filter out spurious data values helps in getting within 0.8% of the ground truth barometric pressure, whereas not using the reputation system leads to a discrepancy of about 20%. We hope our analysis will provide a useful reference to MCS researchers and developers in their future work running MCS campaigns.

In the early 1990’s, various investigations were launched into higher education policies for post-apartheid South Africa. It was in these debates that the South African Post Secondary Education (SAPSE) funding formula was established. It was later established that it was a flawed document as suggested by Bunting (1994:141-149) and cited by Cloete, Maassen, Fehnel, Moja, Gibbon and Perold (2007:93). The SAPSE funding framework which was established in 1982 for the historically White universities applied throughout the late 1980s and early 1990s to all other universities and technikons, was eventually abolished. This was essentially an apartheid funding framework and it could not be used in the current transformed higher education system. The New Funding Framework (2003) gives advantage to comprehensive and traditional universities. It is also graduate output driven. The HEMIS transformed system is committed to equity and strong linkages with national development needs. It was argued by most stakeholder Institutions that with the abolition of the SAPSE funding framework, it would result in major benefits for some groups of institutions and equate to major costs for others. Thus, the significance of this study is to ensure an effective and efficient way for the data to be maintained in order to support the mission and vision of the Durban University of Technology. Bitzer (2009:354) states that South African universities have become highly unpredictable. Universities have moved from an isolated, divided system in the apartheid era toward a single coordinated system. This system has higher levels of participation and responsiveness. Yet it is within a developing economy and poor quality of primary and secondary education that South African higher education therefore faces a multitude of challenges. Bitzer (2009:354) further explains how conditions of unpredictability further worsens the complexities of planning, policy setting and decision making which then impacts negatively on corporate governance and management activities. The purpose was to investigate data management as a strategic information tool and its importance at the Durban University of Technology. The problem revolved around, inter alia, data management and accuracy as structured interventions impacting sound decision making. The research design adopted a quantitative methodological approach that used a precoded self administered questionnaire. The empirical component involved a survey method considering that it was an in-house investigation where the target population equated to 174 respondents. A significant 74% response rate was obtained using the personal method of data collection. Several hypotheses were formulated relating to data quality initiatives, data owners and their responsibility and frequency of data analysis in order to determine accuracy. These were tested using the Pearson chi-square test and data that was analyzed to determine frequencies and percentages of responses. The data was analyzed using the computerized Statistical Program for Social Sciences (SPSS) program. A highly significant finding was that 95.31% of the respondents strongly agreed that data management and integrity is of utmost importance at the Durban University of Technology. One recommendation suggests that for the Durban University of Technology to manage its data as an asset, a policy on data integrity and integration policy should be developed and implemented. Another recommendation highlighted that staff should strive to attain proper classification on he database, considering that this directly impacts on the accuracy of the HEMIS submissions to the Ministry of Education for the state allocated subsidy. The study concludes with directions for further research as well. DOI: 10.5901/mjss.2014.v5n15p313

The purpose of this study was to investigate data management as a strategic information tool and its importance at the Durban University of Technology. The problem revolved around, inter alia, data management and data accuracy as structured interventions impacting on sound decision making. There were many challenges identified with regard to data management at the Durban University of Technology. The research design adopted a quantitative methodological approach that was used for collecting data through the use of a precoded self administered questionnaire. The empirical component involved a survey method considering that in was an in-house investigation and the target population equated to only 174 respondents. A significant response rate of 74% was obtained using the personal method for the data collection. Several hypotheses were formulated relating to data quality initiatives, data owners and their responsibility and frequency of data analysis in order to determine accuracy. These were tested using the Pearson chi-square test as well as data that was analyzed to determine frequencies and percentages of responses. The data was analyzed using the computerized Statistical Program for Social Sciences (SPSS) program. There were some significant findings that emerged from the empirical analysis. A highly significant finding was that 95.31% of the respondents strongly agreed that data management and integrity is of utmost importance at the Durban University of Technology. One of the recommendation suggest that an imperative for the Durban University of Technology to manage its data as an asset, a policy on data integrity and integration policy should be developed and implemented. Another recommendation highlighted and staff should strive to attain proper classification on the database, considering that this directly impacts on the accuracy of the HEMIS submissions to the Ministry of Education for the state allocated subsidy. The study concludes with directions for further research as well.

Data integration plays a pivotal role in modern organizations by consolidating data from disparate sources to facilitate analysis, decision-making, and strategic planning. However, the success of data integration initiatives heavily depends on the quality of integrated data. In this paper, we propose a comprehensive data quality framework tailored specifically for the integration process. The framework encompasses strategies for data cleaning, Upholding data consistency and Integrity, Exception Handling Mechanisms, monitoring, documentation, and governance. We present implementation guidelines and discuss the importance of each component in ensuring the reliability, accuracy, and consistency of integrated data. Furthermore, we illustrate the practical application of the framework through case studies in various industries, highlighting its effectiveness in addressing real-world integration challenges.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

The global population rise has increased vehicles on roads, complicating traffic management. Inefficient traffic control systems cause significant economic losses owing to commuter time wastage, high energy consumption, and greenhouse gas emissions. Traffic signal control systems (TSCSs) are vital in traffic management, impacting traffic flow significantly; therefore, studies are exploring new optimization approaches that adapt to changing traffic conditions. However, they concentrate on either new technology infusion or on control algorithm optimization, and do not holistically address the architectural configuration of the system. In this study, we presented a unique case study by applying an existing systematic framework to the TSCS system architecture design and selection process. This application demonstrates that TSCS enhancement is a multifaceted process that requires a comprehensive assessment of not only technical aspects, such as the control algorithm, but also factors including system architecture, security, and data integrity. Because of the increasing reliance of TSCSs on data exchange between their various subsystems, this case study also adopted a cybersecurity perspective of the system and introduced cyber resiliency as a crucial metric for evaluating TSCS architecture performance. Furthermore, through the applied framework, an optimal TSCS architectural configuration with executable options was identified by generating multiple TSCS architectural configurations using decision option patterns and identifying those on the Pareto frontier to understand the architectural decision‐making process. Traffic engineers and transportation planners can use this case study application as a guide to optimize TSCSs employed in existing transportation networks and design more efficient transportation networks for future urban development.

Digital transformation is fundamentally reshaping organizational accounting functions, driven by the rapid adoption of automation technologies, data analytics, and artificial intelligence (AI). Across industries, finance and accounting systems are evolving from manual, transaction-focused operations into integrated, real-time digital platforms that process high volumes of structured and unstructured data. From a broad perspective, this transformation is redefining how financial information is generated, controlled, and assured, introducing both opportunities for enhanced efficiency and new risks related to system complexity, data integrity, and governance oversight. This study examines the accounting implications of digital transformation with particular emphasis on automation and AI and their effects on financial controls and audit processes. It explores how technologies such as robotic process automation, continuous accounting systems, and AI-driven analytics are altering traditional internal control environments, shifting controls from manual approvals to embedded, system-based mechanisms. The analysis narrows its focus to audit implications, highlighting changes in audit planning, evidence collection, risk assessment, and professional judgment as auditors increasingly rely on automated controls, continuous monitoring, and advanced analytics. While these technologies enhance control effectiveness and audit coverage, they also introduce challenges related to explainability, model risk, cyber security, and regulatory compliance. The study provides a structured conceptual perspective that integrates accounting, control theory, and auditing practice, offering insights into how organizations and auditors can adapt governance frameworks to maintain assurance, accountability, and trust in digitally transformed financial environments.

Transmission of digital content has increases tremendously these days. Privacy and security of the transmitted data has become an important concern in multimedia applications because multimedia data has been an essential part of our lives, from instant messaging applications to social media. For security of the data we use different techniques like cryptography, steganography, and scrambling. Cryptography is one of the main techniques used for information security which includes many forms like digital signatures, authentication etc. and performs system security, confidentiality, data integrity and other functions. This paper works on dual layer security systems using (Elliptic-Curve cryptography) ECC and (Modified Advanced Encryption Standard) MAES called EMAES on Multimedia files (Text, Audio, and Video). Using this approach, unauthorized viewing of the multimedia file can be prevented. Also, this algorithm provides a high level of security and efficiency as compared to existing ones. Comparison of results with other algorithms using parameters like SSIM, FSIM, PSNR and execution time shows that all these parameters are improved 20% or more. The implementation is carried out in MATLAB 2018a.

Nowadays, intelligent information technology can implement high-level information processing and decision-making activities that can support risk assessment of autonomous. Risk assessment is a critical process for deploying autonomous ships, ensuring these innovative vessels' safe and efficient operation. There is a need to identify, analyze, and mitigate potential risks associated with system reliability, collision avoidance, cybersecurity, environmental conditions, human interaction, regulatory compliance, sensor performance, data integrity, emergency response, and testing and validation. This work provides an overview of the essential considerations and objectives of risk assessment in autonomous boats. We used the multi-criteria decision-making model to deal with various criteria. The Ranking of Alternatives through Functional Mapping Criterion Sub-Intervals into Single Interval (RAFSI) method is applied to rank the alternatives. We used the ten criteria and twenty options in this study. The results show that the proposed framework can provide a comprehensive risk assessment framework that can enable stakeholders to gain insights into potential hazards and vulnerabilities unique to autonomous ships.

The focus of present research paper is to manage data integrity and trustworthiness of issues involved in software testing phase of software development life cycle. Many times, it seems that, data integration left behind the software testing phase and it is directly focused at software deployment or delivery time. To avoid issues due to lack of data integration, we developed algorithm which can track integration of test case prioritization along with trustworthiness of test case execution. This paper demonstrates how software testing can be efficient with management of data integrity factor to avoid major security issues. One of the main advantages of our approach is that domain specific semantics can be integrated with the data quality test cases prioritization, thus being able to discover test feed data quality problems beyond conventional quality measures.

The sensitivity of health-care information and its accessibility via the Internet and mobile technology systems is a cause for concern in these modern times. The privacy, integrity and confidentiality of a patient's data are key factors to be considered in the transmission of medical information for use by authorised health-care personnel. Mobile communication has enabled medical consultancy, treatment, drug administration and the provision of laboratory results to take place outside the hospital. With the implementation of electronic patient records and the Internet and Intranets, medical information sharing amongst relevant health-care providers was made possible. But the vital issue in this method of information sharing is security: the patient's privacy, as well as the confidentiality and integrity of the health-care information system, should not be compromised. We examine various ways of ensuring the security and privacy of a patient's electronic medical information in order to ensure the integrity and confidentiality of the information.

Cyber security mitigation is becoming an increasingly urgent aspect in today's digital era, in response to the threat of theft and disruption to information systems, software and hardware. The Covid-19 pandemic has exacerbated this situation by increasing the chances of success of cyberattacks, as the number and scope of such attacks increases. The impact of increasing levels of cyberattacks is the wider disruption to various sectors. This study aims to investigate cybersecurity issues that arose during the Covid-19 pandemic. Researchers analyzed the current conditions as well as cybercrime acts and various types of cyberattacks carried out during this period. In addition, this research also proposes a practical approach to mitigating the risk of cyber attacks that can be implemented by organizations. It is important for organizations to strengthen the protection of their data and critical assets by implementing a comprehensive cybersecurity approach. The results of this study present various techniques for detecting and avoiding cybercrime threats, even after the pandemic is over. By implementing this approach, damage caused by cyber attacks can be minimized, maintaining the integrity of an organization's data and systems. For future research, it is necessary to carry out further development in the field of cyber security by integrating the latest technologies such as artificial intelligence, blockchain, Internet of Things, and other technologies. In this way, efforts to mitigate cyber attacks can continue to be improved, maintaining the integrity and security of information systems in this ever-growing digital era. This research makes an important contribution to understanding and addressing cyber security issues during the pandemic and provides a foundation for further development in the future.

Many data driven organisations need to integrate data from multiple, distributed and heterogeneous resources for advanced data analysis. A data integration system is an essential component to collect data into a data warehouse or other data analytics systems. There are various alternatives of data integration systems which are created in-house or provided by vendors. Hence, it is necessary for an organisation to compare and benchmark them when choosing a suitable one to meet its requirements. Recently, the TPC-DI is proposed as the first industrial benchmark for evaluating data integration systems. When using this benchmark, we find some typical data quality problems in the TPC-DI data source such as multi-meaning attributes and inconsistent data schemas, which could delay or even fail the data integration process. This paper explains processes of this benchmark and summarises typical data quality problems identified in the TPC-DI data source. Furthermore, in order to prevent data quality problems and proactively manage data quality, we propose a set of practical guidelines for researchers and practitioners to conduct data quality management when using the TPC-DI benchmark.

Nowadays, organizations cannot satisfy their information needs from one data source. Moreover, multiple data sources across the organization fuels the need for data integration. Data integration system’s users pose queries in terms of an integrated schema and expect accurate, unambiguous, and complete answers. So the data integration system is not limited to, getting the answers to the queries from the sources, but also it is extended to detect and resolve the data quality problems appeared due to the integration process. The most crucial component in any data integration system is the mappings constructed between the data sources and the integrated schema. In this paper a new mapping approach is proposed to map not only the elements of the integrated schema as done by the existing approaches, but also it maps other elements required in detecting and resolving the duplicates. It provides a means to facilitate future extensibility and changes to both the sources and the integrated schema. The proposed approach provides a linkage between the fundamental components required to provide accurate and unambiguous answers to the users’ queries from the integration system.