Data-driven World Research Articles

Review: Tiny Face Detection and Recognition Techniques

The exponential growth of video and image databases has created a need for intelligent systems to automatically analyze information, as manual efforts are no longer feasible. Faces plays a crucial role in social interaction, conveying identity and emotions, thus requiring efficient and accurate analysis. Deep learning techniques has brought about a significant revolution in face detection, despite their increased computational requirements. This paper presents a comprehensive analysis of representative deep learning-based methods for face detection, focusing on their accuracy and efficiency. It also compares and discusses popular and challenging datasets, including their evaluation metrics. Additionally, a thorough comparison of successful deep learning-based face detectors is conducted, evaluating their efficiency using Floating Point Operations (FLOPs) and latency as metrics. The results and findings of this study can serve as a valuable guide for selecting suitable face detectors for various applications. Moreover, they can contribute to the development of more efficient and accurate detectors. The paper aims to address the pressing needs for intelligent systems that can automatically understand and analyze visual information in an increasingly data-driven world

Consumer manipulation through behavioral advertising

In the contemporary era marked by technological advancements and pervasive use of social media, there has been a notable surge in the collection and subsequent processing of personal data, transforming them into economic assets exploited by corporations. This phenomenon gives rise to a "surveillance society" or "mental capitalism," prominently characterized by the customization of advertisements based on users' consumption profiles and behaviors. Despite its focus on personalized advertising, this practice frequently employs persuasive techniques that possess the potential to influence consumer decisions, prompting concerns about user manipulation and the erosion of decision-making autonomy. This study employs the exploratory deductive method to scrutinize the provisions of one of the European Union's latest regulations tailored for digital platforms, namely the Data Services Act, and its efficacy in addressing the prevailing issues. The research identifies ambiguity within the regulation, particularly regarding its applicability to operations involving the processing of personal data and dark patterns. Additionally, findings emphasize the necessity for active supervision, especially concerning the profiling of children and adolescents. In conclusion, this study underscores the urgency of implementing regulatory measures to ensure more effective protection, mitigating persuasive and obscure practices on social media. Such efforts aim to preserve consumer autonomy and rights in an increasingly digital and data-driven world.

Enhancing Decision-Making Accuracy with Predictive Analytics: A Necessity or a Choice?

In today's data-driven world, the deployment of predictive analytics in decision-making processes is not just a technological advancement but a critical strategic asset. This discussion explores whether employing predictive analytics to enhance decision-making accuracy is a necessity or merely a choice for contemporary organizations. In considering whether employing predictive analytics is a necessity or a choice, one must acknowledge the competitive edge and enhanced accuracy it provides to decision-making processes across industries. While organizations may opt to rely on traditional decision-making processes, the ability to analyze vast datasets and predict future trends offers a decisive advantage that is becoming increasingly difficult to ignore. Therefore, for organizations aiming to remain competitive and effective in their decision-making, the integration of predictive analytics is less a choice and more a necessity. Predictive analytics is not merely a tool but a strategic imperative that, when correctly implemented, can significantly enhance decision-making accuracy and efficiency. As data continues to grow in volume and complexity, the necessity of predictive analytics will only become more pronounced, shaping it as an essential element of modern organizational decision-making frameworks.

CULTIVATING DATA OBSERVABILITY AS THE NEXT FRONTIER OF DATA ENGINEERING: A PATH TO ENHANCED DATA QUALITY, TRANSPARENCY, AND DATA GOVERNANCE IN THE DIGITAL AGE

In the age of increasing process automation and data-driven decision-making, ensuring the reliability, transparency and usability of data is of paramount importance. In this context, the concept of "data observability" has aroused the interest of practitioners and there is also a lot of grey content on it. On the other hand, there is a lack of academic effort to define and build on the concept. This conference paper will therefore examine the importance of "data observability" in modern data ecosystems. The focus is on the definition and characterisation of the concept, the differentiation from other concepts (e.g. data quality, data monitoring, data discovery, data operations) and why this concept appears to be so important in an increasingly data-driven world. In addition, the concept of "data observability" is categorised in the dynamically developing research field of data governance. For this purpose, a multivocal literature review (MLR) was conducted, a form of systematic literature review (SLR) which, in addition to the published (formal) literature (e.g. journal and conference papers), also includes and brings together the grey literature (e.g. blog posts, videos and white papers). The results show that the concept of "data observability" has the potential to revolutionise the way companies manage, analyse and derive insights from their data, ultimately leading to more informed and confident decision-making. Nevertheless, there is still plenty of room for further research into the specific contribution to better data and therefore better business processes and decisions.

Leveraging Salesforce Analytics for Enhanced Business Intelligence

Salesforce Analytics is a strong business intelligence (BI) solution that turns raw data into actionable insights. Today's data-driven world requires fast, reliable data analysis for business choices. Salesforce Analytics' broad range of solutions helps organizations use data for better decision-making, operational efficiency, and strategic planning. Integration of Salesforce Analytics into company processes has several benefits. First, it gives organizations real-time knowledge to adapt quickly to market developments and client requests. Organizations may track KPIs and trends using customisable dashboards, automatic reporting, and predictive analytics. Real-time visibility empowers decision-makers to prevent concerns from becoming major ones. Salesforce Analytics' unified data access and analysis platform improves departmental cooperation. Team members may easily exchange insights and reports, breaking down silos and promoting data-driven culture. On-demand report creation and sharing guarantees that all stakeholders have the same information, resulting in better aligned and informed decision-making. Customer relationship management is another important Salesforce Analytics function. The software analyzes touchpoint data to help companies understand their consumers. Businesses may detect client preferences, forecast behavior, and tailor marketing by using this data, improving customer happiness and loyalty. Salesforce's AI-powered analytics help foresee client wants and personalize offerings. Salesforce Analytics also improves forecasting and planning. Organizations may forecast future performance better by evaluating previous data and patterns. This skill is crucial in sales forecasting, inventory management, and financial planning, where precise projections ensure operational efficiency and profitability. Another benefit of Salesforce Analytics is third-party data integration. To get a complete picture of their operations, businesses may mix data from social media, email marketing, and e-commerce platforms. Integration improves strategic choices and corporate results by enabling more complete analysis. The platform's versatility lets analytics tools be customized to match corporate demands, boosting its BI solution value. Data security is crucial in the digital era, and Salesforce Analytics takes it seriously. The platform protects critical company data using encryption, access restrictions, and industry standards. Businesses may use their data confidently because of this security assurance. Finally, Salesforce Analytics improves business intelligence by offering real-time insights, collaboration, customer knowledge, and precise forecasting and planning. In today's fast-paced corporate world, its ability to interact with third-party data sources and secure data analysis makes it a useful tool. Salesforce Analytics will allow data-driven decision-making and strategic planning to help companies accomplish their objectives and expand sustainably as they navigate the contemporary market.

To give: ethically storying data

ABSTRACT We live in a data-driven world. The voluminous scale of data gathered can lead to diminished consciousness of ethics whilst economic interests are prioritised. Across recent decades education has come to be heavily data driven and datafied. We have witnessed the dehumanising and increased labour impacts of school datafication. In search for alternatives, we wonder about the role and hope of story. We look to the etymological roots of data to see how a relationship with story may work to foreground ethicality, through attention to positionality, values, presence, representation and form. As education researchers of differing socio-cultural backgrounds, we bring diverse illustrations of how these five considerations feature in our research with educational data and trouble the politics of education data in a post-truth world. To further resonate a merging of story with data for ethical research, we apply Derrida’s metonymic logos: “cinders there are”, reminding us of the traces of life in data. We close with questions to inform ethical consciousness to storied data with transparent attention to positionality, values, presence, representation and form.

Mixed-Integer Optimization with Constraint Learning

In today’s data-driven world, there is a growing opportunity for optimization models to more closely resemble real-world scenarios, namely through learning constraints or objective functions that are not explicitly known and must be estimated through data. In “Mixed-Integer Optimization with Constraint Learning,” the authors establish a novel methodological framework for data-driven decision making. Their approach enables constraints and objectives to be embedded directly from trained machine learning models that are mixed-integer optimization representable including linear models, decision trees, ensembles, and neural networks. The authors propose two different strategies to manage uncertainty in learned constraints. The first is based on the concept of trust region where the convex hull of data points is used to avoid extrapolation. Additionally, they present an ensemble learning method for enforcing constraints across multiple estimators, improving the robustness of the downstream prediction accuracy. Practitioners can access this framework through the “OptiCL” Python package. Case studies on World Food Programme humanitarian aid planning and chemotherapy regimen optimization demonstrate the methodology’s ability to produce scalable and data-informed prescriptions.

Introducing Indigenous Perspectives on Data Sovereignty in Engineering Education

We live in a data-driven world, and decisions of technology developers impact how the world uses technology. But the education system does not address data sovereignty. This is more relevant in New Zealand, with M?ori data sovereignty being a concern. In engineering education, ethical aspects of technology are taught from a western perspective. This paper describes a different approach used recently in an undergraduate engineering course, where an indigenous perspective was integrated with the predominant western view. Electrical and Digital Systems is a course taken by all engineering students at the University of Auckland. This course teaches the engineering perspective on how data is collected and processed, and hence is the perfect point to introduce the indigenous data sovereignty perspectives. Student feedback received so far supports the success of this introduction. This also prepares our future workforce to be competent to incorporate indigenous world views in engineering decisions.

Community college students’ self-assessment of data literacy: exploring differences amongst demographic, academic, and career characteristics

PurposeData literacy empowers college students, equipping them with essential skills necessary for their personal lives and careers in today’s data-driven world. This study aims to explore how community college students evaluate their data literacy and further examine demographic and educational/career advancement disparities in their self-assessed data literacy levels.Design/methodology/approachAn online survey presenting a data literacy self-assessment scale was distributed and completed by 570 students at four community colleges. Statistical tests were performed between the data literacy factor scores and students’ demographic and educational/career advancement variables.FindingsMale students rated their data literacy skills higher than females. The 18–19 age group has relatively lower confidence in their data literacy scores than other age groups. High school graduates do not feel proficient in data literacy to the level required for college and the workplace. Full-time employed students demonstrate more confidence in their data literacy than part-time and nonemployed students.Originality/valueGiven the lack of research on community college students’ data literacy, the findings of this study can be valuable in designing and implementing data literacy training programs for different groups of community college students.

A Cross-Institution Information-Sharing Scheme Based on a Consortium Blockchain

In today’s data-driven world, efficient and secure cross-institution information-sharing is an urgent challenge. Traditional information-sharing methods based on access controlling often suffer from issues such as privacy breaches and high communication complexity. To address this issue, this paper proposes a cross-institution information-sharing solution based on a consortium blockchain, in which it combines on-chain transaction consensus with off-chain institution storage, thereby facilitating collaboration among nodes from different institutions on the blockchain. To enhance the efficiency and security of transactions on the blockchain, we also introduce a dynamic and adaptive Practical Byzantine Fault Tolerance (DA-PBFT) consensus protocol, which permits nodes to dynamically join and exit the blockchain network, consequently improving network scalability. Through a reputation mechanism, we swiftly identify and remove faulty and malicious nodes, enhancing the trustworthiness of nodes in the information-sharing network based on consortium blockchain, thereby improving consensus efficiency. We have also employed encryption techniques to enhance the privacy and integrity of data during the process of cross-institution information sharing. A comprehensive analysis of the communication complexity in the information-sharing network confirms the effectiveness and security of our proposed solution. We offer a unique solution to improve the efficiency and security of cross-institution information-sharing while ensuring data integrity and privacy. By addressing the challenges of privacy breaches and high communication complexity in information sharing, we establish a foundation for secure cross-institution data exchange.

A Critical Historic Overview of Artificial Intelligence: Issues, Challenges, Opportunities, and Threats

Artificial intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the big data-driven world is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the cybernetic artificial intelligence (CAI) field has been proposed and analyzed here for the first time. Despite the optimism and enthusiasm surrounding AI, its future may turn out to be a “catastrophic winter” for the whole world, depending on who controls its development. The only hope for the survival of the planet lies in the quick development of CAI and the wise anthropocentric revolution. The text proposes specific solutions for achieving these two goals. Furthermore, the importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values.

Real Property Transactions in the Network Society: Platform Real Estate, Housing Hactivism, and the Re-scaling of Public and Private Power

Technology is rapidly transforming the landscape of land ownership and housing transactions, creating new types of consumer risk and new regulatory challenges. As markets, legal systems, and housing consumers navigate the new opportunities and risks of “platform real estate” (or “PropTech”), the underlying land laws, policies, and practices that produce the material context and legal framework for real property transactions, and against which consumer risk and regulation must be understood, require “re-scaling.” In this article, we offer a theoretical framework for this re-scaling project, drawing on our earlier work to develop Resilient Property Theory (RPT) for analysing complex, large-scale property questions using methods that—in a departure from liberal property theories—pay attention to the public role of the state. Against a backdrop in which narratives of private property law defined real property transactions as “private realm” activities, while consumer law and policy provided the vehicle for state-backed regulation of specifically defined transactions based on a risk-based approach, this article brings the state back into view to reflect on new configurations of risk in consumer housing transactions. In the de-materialized realm of the “network society,” networks, platforms, and innovations are recalibrating housing transactions. In this data-driven world, land transactions are financialized, depersonalized, and increasingly remote from the materiality of land and the consumption of housing. As new capabilities in digital land transaction systems reach back into the underlying law of ownership, official (state), insider (global capital markets), and outsider (social movement activists) networks have evolved to leverage their relative positionality. This article uses techniques developed in RPT to examine the re-scaling of risk in real property and housing transactions through digital network technologies. We consider the implications of resilience needs in the network society in relation to the public sovereignty of the state, the private sovereignty of land ownership, and practices of resistance to public and private sovereignty through “housing hacktivism.” Finally, we argue that conceptions of consumer vulnerability and risk and embedded ideas about the relationships between private property law and consumer law and policy in real property transactions must evolve to take account of these effects.

Vaccine hesitancy for COVID19: what is the role of statistical literacy?

Vaccination is an important measure used to control the spread of COVID19. The estimation of risk versus benefit of vaccination is based on the understanding of information about the vaccine. Statistics are frequently part of communications about COVID19. Individuals that do not have an adequate foundation of statistical knowledge may not be able to properly assess associated risks and benefits. This study aims to assess the association between statistical literacy and hesitation to receive the COVID19 vaccine. A nationally representative sample of 2,138 adults, recruited through CINT United States, Inc., (Lawrenceville, NJ; http://www.cint.com), completed an internet survey in the summer of 2021. This survey collected demographic measures and information about COVID19 vaccination status. The competency of respondents on various basic statistical concepts was assessed along with the corresponding confidence of respondents in their answers. A multivariable logistic regression model was constructed to assess the relationship between vaccine hesitancy and statistical literacy while controlling for covariates of interest. Statistical literacy was found to have a negligible association with COVID19 vaccine hesitancy (OR 1.01; 95% CI 1.00-1.02). In addition, differences in the proportion receiving the COVID19 vaccine between political affiliations, income levels, race groups, and ethnicities were observed. The statistical knowledge of the general American public is not commensurate with the need to be literate in basic statistical concepts in the data-driven world in which we live. An effective way to stem vaccine hesitancy may rely on increased statistical knowledge to not be biased by preconceived beliefs shaped by misinformation.

Application of Bat Algorithm for Data Anonymization

The rapid proliferation of digital data has raised significant concerns regarding privacy and data security, necessitating the development of effective data anonymization techniques. This research presents a novel application of the Bat Algorithm (BA) for data anonymization, a nature-inspired optimization algorithm that mimics the echolocation behavior of Bats. The proposed approach leverages the BA's unique search capabilities to achieve a delicate balance between data utility and privacy preservation, a critical aspect in today's data-driven world. By treating data attributes as potential solutions and employing the BA's search process, the algorithm iteratively identifies and modifies sensitive attributes while minimizing information loss. This research contributes to the developing field of research on data anonymization by introducing a nature-inspired optimization technique that offers a promising alternative to traditional anonymization methods. Experimental results on various real-world datasets demonstrate the effectiveness of the proposed approach in achieving robust privacy protection while maintaining data quality, outperforming existing anonymization methods in terms of utility and computational efficiency. Furthermore, the proposed BA-based data anonymization approach exhibits versatility, scalability, and adaptability, making it suitable for diverse application domains, from healthcare and finance to social media and beyond. In summary, this study highlights the potential of the Bat Algorithm as a valuable tool in the field of data anonymization, offering a promising avenue for addressing the privacy challenges associated with the ever-expanding digital data landscape.

Enhancing Data Security and Predictive URL Analysis: A Comprehensive Approach to Preventing Data Breaches

Abstract: There has never been a time in history when starting and running a profitable business has been more difficult due to the upward trending rising trend in data breaches. Data breaches can have an immediate impact on hundreds of millions or conceivably billions of individualities in the data-driven world of the moment. Data breaches have grown in scope along with digital metamorphosis as attackers take advantage of our every data. Various measures are taken by many companies to control data breaches in order to prevent them. A business or firm may employ techniques like data encryption, human error, data backup and recovery, and data security software. In the case of middle-level and low-level organizations suffering the most cyber-attacks, Many businesses are successful in defending their data from intruders, typically large multinational corporations which engage a specialist and safeguard their own data. Therefore, we can employ some of the standard data breach prevention strategies in our project to ensure that the domain can receive its user data without any consequences. Our project proposes to improve data security where user data must reach their domain without any disruption.

A Review of Statistics Education Reform Efforts in Korea

Statistics education reform in Korea has played a critical role in innovating school mathematics education, as it provides students with skills necessary for a data-driven world. Over the past two decades, numerous researchers, teachers, and governmental support have been devoted to enhancing statistics education. The impact of these reform efforts has led to significant changes in the goals, learning content, structure, instructional methods, and assessment approaches in the national mathematics curriculum, mathematics textbooks, and mathematics classrooms. This article outlines key contributions to improving statistics education since the end of 1990s. It reviews the trend of statistics education research and government initiatives in developing a research-based statistics education system and highlights major changes in the national mathematics curricula and the mathematics textbooks: namely, the enhancement of real-world connection, the integration of statistical problem solving approaches, and the incorporation of technology in statistics education at all school levels. The article also presents prospects and challenges for continuing the reform of statistics education in Korea, based on lessons learned during the review of the reform efforts.

Optimizing Product Choices through A/B Testing and Data Analytics: A Comprehensive Review

In an increasingly data-driven world, businesses seek to enhance their strategies and performance through effective optimization methods. One such method is A/B testing, a potent tool enabling the comparison of different versions of products or services to determine superior performance. This research paper delves into the fundamentals of A/B testing and its potential to drive improved outcomes. By investigating user funnels and journeys, opportunities for improvement emerge, forming the foundation for hypothesis development. The hypothesis, a crucial element, involves educated conjectures driven by data, research, or experience, closely linked to specific problems or opportunities. Designing a robust test includes power analysis for sample size calculation, as well as considerations such as randomization, control variables, and appropriate statistical analyses. Subsequently, the paper delves into statistical tests like t-test, z-test, and chi-squared test, determining the statistical significance of observed differences. Interpretation of A/B test outcomes involves statistical significance, effect size, user behavior analysis, practical significance, and replicability. The paper concludes by envisioning the role of AI in reshaping A/B testing, automating tasks, processing real-time data, and testing multiple hypotheses efficiently. This revolution offers data professionals unparalleled insights and possibilities for the future

Artificial intelligence in knowledge management: application insights and guidelines

The development of digital technologies has aroused increasing interest among researchers and managers due to the inherent transformations in productive systems, where knowledge is recognized as a key element. In this context, this study represents an exploratory perspective that does not aim for exhaustiveness, but rather a specific deep dive into the application of artificial intelligence in knowledge management. The methodology employed combines literature review, bibliometrics, and brainstorming. Through a detailed analysis of 12 articles, it was identified that the scientific production on this subject is promising, showcasing new possibilities for knowledge management in a data-driven world. Additionally, the authors propose eight application guidelines for these concepts, which serve as transient observation points that vary according to the complexity of each organization, but when practiced, tend to result in improvements across any scenario. The empirical validation of these guidelines is suggested as future work.

Insights from senior communicators: Navigating obstacles, leveraging opportunities, and leading teams to capitalize on data and analytics

While data, digital analytics, and technology have the possibility of further elevating public relations to a strategic function, many communicators cite barriers, including that many on their teams lack digital data and analytics competency. Researchers interviewed 28 senior-level communicators who have experience working with digital data and analytics. The study sought to examine how communicators are adapting to and leading teams in this data revolution by exploring two primary research questions: (1) What are the perceived opportunities and challenges with digital data and analytics? and (2) How can communicators create an environment to encourage discovery and innovation connected to digital data and analytics? Participants described the challenges of managing vast data streams and alignment, staying current with digital trends and technology, and dealing with the shortcoming of AI tools and algorithms. When asked about opportunities, participants described how communicators are uniquely positioned to align data and analytics efforts to help them understand audiences, foster mutually beneficial relationships with publics, and share stories with these publics and senior counsel that contribute to organizational objectives. Participants said they develop data competency among their communication teams by fostering a creative and curious culture, creating diverse teams, and championing employees. In summary, this study expands understanding of how communicators can evolve and lead in an increasingly data-driven world.

Balancing Privacy and Competition: Evaluating the Competitive Effects of India’s Data Protection Bill

Abstract Data protection and competition law are perhaps two of the most enmeshed legal arenas in the contemporary data-driven world. This essay ingresses into this intersection, appraising the competitive implications of India’s Data Protection Bill, 2022 through key arenas of conflict such as jurisdictional tussles, data portability, the remedy of mandatory data sharing, and compliance costs. Although the intersection of privacy and competition law has been discussed in Indian scholarship, an analysis of the competitive implications of the Data Protection Bill has been absent. This is precisely the gap that this piece aims to fill, by appraising the host of new implications of the Digital Personal Data Protection Bill (DPDPB) on Indian competition and providing a framework to evaluate various competition concerns such as jurisdiction, compliance, and data sharing of any subsequent data protection bill in India. It does this by first analyzing the extent to which competitive concerns can be addressed in the Indian data protection system. With this premise, it secondly considers the question of jurisdictional primacy between the Competition Commission and the Data Protection Board, arguing for the primacy of the Competition Commission. Thirdly, it discusses the questions of data portability, mandatory data sharing and compliance costs in the DPDBP which could affect India’s competition, critically discussing the trade-offs and suggesting potential solutions. The goal of the piece is to critically examine the implications of the 2022 version of the bill to suggest ways forward for its future iterations in India.