Data-intensive Technologies Research Articles

The political economy of digital government: How Silicon Valley firms drove conversion to data science and artificial intelligence in public management

IMPACT The relationships between digital governments and private digital technology corporations shape investments that endure for years, even decades. Yet recent changes have been under-researched in public management and accountability studies. Until the 2010s systems integrators (SIs) were dominant, producing proprietary legacy computer systems that still shape government technologies today. Since that time, governments have become increasingly reliant on the online tools and cloud technologies developed by Silicon Valley (SV) firms and platform corporations. To keep digital investments in line with business and civil society progress, governments have had to catch up and innovate with the latest generation of data-intensive technologies, such as artificial intelligence (AI), and to adopt some SV organizational culture and practices in government. However, previous long time-lags in public sector responses highlight many lessons for today’s digital government change pioneers. In particular, the article illustrates the importance for digital managers of accelerating change in government organizational cultures, and developing the expertise to manage contractual diversity.

Triple-Disk Recovery RDP Coding: A Robust Solution for Data Storage in RAID Configurations

Data-intensive technologies require reliable and efficient storage solutions to ensure the integrity and availability of vast amounts of generated data. Redundant Arrays of Independent Disks (RAID) technology has emerged as a popular choice, offering fault tolerance and improved performance. Among RAID configurations, RAID-6 stands out for its ability to tolerate up to two disk failures. This is achieved through algorithms like Row-Diagonal Parity (RDP) coding, which introduces redundancy and enables data recovery. In this paper, we propose an innovative approach, namely Triple-Disk Recovery RDP (TDR-RDP), to enhance reliability based on RDP coding by accommodating three disk failures. We review the RDP algorithm, introduce the TDR-RDP coding scheme, elaborate on encoding and decoding processes, and address diverse types of triple disk failures along with their recovery strategies. Furthermore, we evaluate and compare the computing efficiency of TDR-RDP with the RDP algorithm, showcasing its practical feasibility. Overall, the TDR-RDP algorithm offers a compelling solution to bolster data storage reliability in RAID configurations, surpassing traditional RDP coding by ensuring resilience against triple disk failures and augmenting data-intensive technologies.

THREE MORAL CHALLENGES OF SURVEILLANCE CAPITALISM IN THE METAVERSE

2021 has been called the first year of the metaverse, which is an independent virtual digital world that is both imitative and transcendent to the real physical world. Many tech giants claim that this technological innovation will bring huge opportunities and dividends to society, but some critics believe that it will also pose challenges to the current social ethics. The important moral challenges may stem from the data issue posed by the metaverse, which is believed to build an unequal relationship between users and service providers due to data-intensive technologies such as VR. This paper argues that the unequal relationships in data have caused the typical consequences of what Zuboff calls surveillance capitalism and posed three major moral challenges to our society, including alienation, exploitation, and domination. According to the Marxist account, alienation and exploitation arise from the existence of digital labor and the monopoly of means of production in the metaverse, while by referring to the Republican account of liberty, the emergence of domination can be attributed to the inequality of data possession.

AI ethics as subordinated innovation network

AI ethics is proposed, by the Big Tech companies which lead AI research and development, as the cure for diverse social problems posed by the commercialization of data-intensive technologies. It aims to reconcile capitalist AI production with ethics. However, AI ethics is itself now the subject of wide criticism; most notably, it is accused of being no more than “ethics washing” a cynical means of dissimulation for Big Tech, while it continues its business operations unchanged. This paper aims to critically assess, and go beyond the ethics washing thesis. I argue that AI ethics is indeed ethics washing, but not only that. It has a more significant economic function for Big Tech. To make this argument I draw on the theory of intellectual monopoly capital. I argue that ethics washing is better understood as a subordinated innovation network: a dispersed network of contributors beyond Big Tech’s formal employment whose research is indirectly planned by Big Tech, which also appropriates its results. These results are not intended to render AI more ethical, but rather to advance the business processes of data-intensive capital. Because the parameters of AI ethics are indirectly set in advance by Big tech, the ostensible goal that AI ethics sets for itself—to resolve the contradiction between business and ethics—is in fact insoluble. I demonstrate this via an analysis of the latest trend in AI ethics: the operationalization of ethical principles.

Printable Epsilon-Type Structure Transistor Arrays with Highly Reliable Physical Unclonable Functions.

Printed electronics promises to drive the future data-intensive technologies, with its potential to fabricate novel devices over a large area with low cost on nontraditional substrates. In these emerging technologies, there exists a large digital information flow, which requires secure communication and authentication. Physical unclonable functions (PUFs) offer a promising built-in hardware-security system comparable to biometrical data, which can be constructed by device-specific intrinsic variations in the additive manufacturing process of active devices. However, printed PUFs typically exploit the inherent variation in layer thickness and roughness of active devices. The current in devices with enough significant changes to increase the robustness to external environment noise is still a challenge. Here, printable epsilon-type-structure indium tin oxide transistor arrays are demonstrated to construct high-reliability PUFs by modifying the coffee-ring structure. The epsilon-type structure improves the printing scalability, film quality, and device reliability. Furthermore, the print-induced uncertainty along the channel thickness and length can lead to changes in the carrier concentration. Notably, the randomly distributed printing droplets in a small area significantly increase this uncertainty. As a result, the PUFs exhibit near-ideal uniformity, uniqueness, randomness, and reliability. Additionally, the PUFs are resilient against machine-learning-based attacks with a prediction accuracy of only 55% without postprocessing.

SMT: Software-Defined Memory Tiering for Heterogeneous Computing Systems With CXL Memory Expander

The rapid development of data-intensive technologies has driven an increasing demand for new architectural solutions with scalable, composable, and coherent computing environments. Compute Express Link (CXL), an open-standard interconnect protocol, overcomes architectural limitations by efficiently expanding memory capacity and bandwidth. In this work, we propose a power-efficient and cost-effective solution consisting of CXL-attached memory hardware and a software suite. The memory module hardware integrated with double-data-rate (DDR) dynamic random access memory and a CXL controller expands bandwidth by dozens of gigabytes per second and increases memory capacity by a few terabytes. Our software suite, scalable memory development kit, inherits and expands the traditional memory management architecture of existing Linux systems. We proved the functionality of the proposed solution by integrating renowned data centers and computing-intensive applications. The proposed CXL solution improved throughput for in-memory database and artificial intelligence applications by 1.5-fold and 1.99-fold, respectively, compared with the conventional DDR-only memory system.

Coca-Cola Curses: Hate Speech in a Post-Colonial Context

Hate speech is a contextual phenomenon. What offends or inflames in one context may differ from what incites violence in a different time, place, and cultural landscape. Theories of hate speech, especially Susan Benesch’s concept of “dangerous speech” (hateful speech that incites violence), have focused on the factors that cut across these paradigms. However, the existing scholarship is narrowly focused on situations of mass violence or societal unrest in America or Europe. This paper discusses how online hate speech may operate differently in a postcolonial context. While hate speech impacts all societies, the global South—Africa in particular—has been sorely understudied. I posit that in postcolonial circumstances, the interaction of multiple cultural contexts and social meanings form concurrent layers of interpretation that are often inaccessible to outsiders. This study expands the concept of online harms by examining the political, social, and cultural dimensions of data-intensive technologies. The paper’s theories are informed by fieldwork that local partners and I conducted in Kasese, Uganda in 2019–2020, focusing on social unrest and lethal violence in the region following the 2016 elections. The research, completed with assistance from the Berkeley Human Rights Clinic, included examining the background and circumstances of the conflict; investigating social media’s role in the conflict; designing a curriculum around hate speech and disinformation for Ugandan audiences; creating a community-sourced lexicon of hateful terms; and incorporating community-based feedback on proposed strategies for mitigating hate speech and disinformation. I begin this with a literature review of legal theory around hate speech, with a particular focus on Africa, and then turn to the legal context around hate speech and social media use in Uganda, examining how the social media landscape fueled past conflicts. Then I explain my Kasese fieldwork and the study’s methodology, before describing initial results. I follow with a discussion of applications to industry, specifically how hate speech is defined and treated by Meta’s Facebook, the dominant social media provider in Kasese. It progresses to a discussion of the implications of the study results and legal and policy recommendations for technology companies stemming from these findings. Importantly, I apply the research findings to expand existing scholarship by proposing a new sixth “hallmark of dangerous speech” to augment Benesch’s paradigm. Adding “calls for geographic exclusion” as a new qualifier for dangerous speech stems from the particular characteristics embodied by postcolonial hate speech. Examples from the Kasese study illustrate how this phenomenon upends platforms’ expectations of hate speech—which may not consider “Coca-Cola bottle” to be an epithet. The application of this new hallmark will create a more inclusive understanding of hate speech in localized contexts. This paper’s conclusions and questions may challenge platforms that must address hate speech and content moderation at a global scope and scale. It will examine the prevalence and role of social media platforms in Africa, and how these platforms have provided resources and engagement with civil society in these regions.

Quantitative Framework for Bench-to-Bedside Cancer Research.

Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical-protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets.

Plasma fireball-mediated ion implantation for nonvolatile memory application

Extraordinary performance of nonvolatile memories based on resistive switching is expected to fulfill the requirements of next-generation data-intensive technologies. However, due to the separate growth of functional layer and electrodes in the device, resistive random access memories still suffer from their implementation at a commercial scale. Herein, depending upon the polarity, a single device is used to grow Ti thin film followed by plasma fireball-mediated oxygen ion implantation to transform the top surface of Ti film into a functional TiOx layer to fabricate the device. The formation of TiOx layer at the near-surface region and oxygen-concentration gradient with depth in the implanted film is demonstrated. The current–voltage characteristics of the device analyzed at the nanoscale show forming-free bipolar resistive switching, which is further confirmed by the twofold erase-write process. At 5 × 1016 ions cm−2 fluence, resistive switching occurs at higher voltages, while at 5 × 1017 ions cm−2 fluence, it occurs at lower voltages. As an application, the rapid fabrication of periodic arrays of squared memory cells of different sizes is demonstrated. We propose a simple and cost-effective one-step technique to fabricate resistive switching-based nanoscale nonvolatile memory devices, which can be utilized for the production of high-density commercial devices at a large scale.

Human Rights in the Digital Era: The Right to Privacy at Stake

The respect for fundamental human rights, including the right to privacy, is always invoked in opposition to the monitoring and social control techniques, but how rigorous and holistic analysis of the translation of these rights in a largely globalized and computerized world? Internet and new technologies: what remain of one’s privacy? However, data-intensive technologies are helping to create a digital environment in which individuals, governments and commercial enterprises are increasingly able to keep up, to analyze, predict, and even manipulate people’s behavior to an unprecedented degree. If effective safeguards are not applied, these technological developments entail very significant risks for human dignity, autonomy, and privacy, as well as for the exercise of human rights in general. The right to privacy is one of the fundamental pillars of human rights which finds its legal basis in international texts, inter-alia, the 1948 Universal Declaration of Human Rights, the 1966 International Covenant on Civil and Political Rights (ICCPR), and the 1950 European convention of human rights (ECHR). In accordance with the instructions of the relevant resolutions of the United Nations General Assembly and the Human Rights Council, have organized expert consultations and published reports to explore issues that threaten the right to privacy and other human rights in the digital age. The United Nations General Assembly adopted on December 16, 2020, a resolution in which it “reaffirms fundamental of the right to privacy and renews international commitments to ending all abuses and violation of this right worldwide.” Keywords : Human rights – Right to Privacy - Digital technology – ICCPR – ECHR – Cyberspace DOI: 10.7176/JAAS/80-08 Publication date: August 31 st 2022

Data Privacy Concerns Using mHealth Apps and Smart Speakers: Comparative Interview Study Among Mature Adults.

BackgroundNew technologies such as mobile health (mHealth) apps and smart speakers make intensive use of sensitive personal data. Users are typically aware of this and express concerns about their data privacy. However, many people use these technologies although they think their data are not well protected. This raises specific concerns for sensitive health data.ObjectiveThis study aimed to contribute to a better understanding of data privacy concerns of mature adults using new technologies and provide insights into their data privacy expectations and associated risks and the corresponding actions of users in 2 different data contexts: mHealth apps and smart speakers.MethodsThis exploratory research adopted a qualitative approach, engaging with 20 mature adults (aged >45 years). In a 6-month test period, 10 (50%) participants used a smart speaker and 10 (50%) participants used an mHealth app. In interviews conducted before and after the test period, we assessed the influence of data privacy concerns on technology acceptance, use behavior, and continued use intention.ResultsOur results show that although participants are generally aware of the need to protect their data privacy, they accept the risk of misuse of their private data when using the technology. Surprisingly, the most frequently stated risk was not the misuse of personal health data but the fear of receiving more personalized advertisements. Similarly, surprisingly, our results indicate that participants value recorded verbal data higher than personal health data.ConclusionsOlder adults are initially concerned about risks to their data privacy associated with using data-intensive technologies, but those concerns diminish fairly quickly, culminating in resignation. We find that participants do not differentiate between risky behaviors, depending on the type of private data used by different technologies.

Data-Induced Rationality and Unitary Spaces in Interfirm Collaboration

The real-time data transfer between collaborating companies allows them to represent and control activities across firm boundaries, providing new ways to organize collaborative efforts. We conducted an inductive multiple case study of five long-term relationships to examine the effects of data-intensive technologies on the organization and management of collaborative relationships in industrial companies. Our analysis shows how the delegation of digital activities into specialized digital units fostered data-driven mindsets and data-driven interactions that jointly formed a holistic data-induced rationality for managing the relationship. Together, the compartmentalization of digital collaboration and the data-induced rationalities turned these units into “unitary spaces,” organizational enclosures where structural tensions and competing demands were temporarily suspended to foster single-minded pursuit of collaborative short-term benefits for the partner company. Funding: The authors are grateful for the financial support from the Academy of Finland [Grant 274536].

Tradeoffs all the way down: Ethical abduction as a decision-making process for data-intensive technology development

Ample scholarship demonstrates that data-intensive technologies have the capacity to cause serious harm and that their developers are obliged to address ethics in their work. This ethnographic paper tells the story of data scientists attempting to instantiate a carefully considered ethical vision into a data infrastructure while balancing competing priorities, negotiating divergent interests, and wrestling with contrasting values. I use their story to develop the concept of “ethical abduction,” which I characterize as an exemplary process by which actors can intentionally and systematically address ethical issues that arise during their day-to-day actions by making decisions with consideration for a foundational ethical worldview. It entails tacking back and forth between divergent but complementary ways of thinking: between establishing ideals and making decisions given practical constraints; between understanding historical context and anticipating future consequences; between acknowledging structural dependencies and accepting responsibility for moral agency.

Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products

Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven Integrated Computational Materials Engineering (ICME), Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. In the introduction of the DT design paradigm in the ICME era, the simulation aspects of DT and the data and design infrastructures are discussed. Referring to the simulation and theoretical factors of DTs, high-throughput simulation and automation and artificial intelligence-assisted multiscale atomistic modeling are detailed through several cases studies. With respect to data and data mining technologies, entropy and its application for attribute selection in decision trees are discussed to emphasize knowledge-based modeling, simulation and data analysis in machine learning coherently. Guided by the perspectives and case studies of the digital thread, we present our recent work on the design, manufacturing and product service via big data-assisted DTs for smart design and manufacturing by integrating some of these advanced concepts and technologies. It is believed that big data-assisted DTs for smart design and manufacturing effectively support better products with the application of novel materials by reducing the time and cost of materials design and deployment.

Implementation and Application of Artificial Intelligence in Selected Public Services

Data-intensive technologies, such as artificial intelligence, imply huge opportunities for transforming the delivery of healthcare and social services, improving people’s quality of life and working in the health and welfare system. The aim of this paper is to present examples of the implementation of artificial intelligence techniques in healthcare and social services and to sketch the trends and challenges in the adoption of artificial intelligence techniques, with an emphasis on the public sector and selected public services. Analysis is based on a realistic assessment of current artificial intelligence technologies and their anticipated development. Besides the benefits and potential opportunities for healthcare and social services, there are also challenges for governments. Understanding the huge potential of artificial intelligence as well as its limitations will be a key step forward, but it is essential to avoid the trap of an overestimation of artificial intelligence potential.

Nanophotonics-enabled optical data storage in the age of machine learning

The growing data availability has accelerated the rise of data-driven and data-intensive technologies, such as machine learning, a subclass of artificial intelligence technology. Because the volume of data is expanding rapidly, new and improved data storage methods are necessary. Advances in nanophotonics have enabled the creation of disruptive optical data storage techniques and media capable of storing petabytes of data on a single optical disk. However, the needs for high-capacity, long-term, robust, and reliable optical data storage necessitate breakthrough advances in existing optical devices to enable future developments of artificial intelligence technology. Machine learning, which employs computer algorithms capable of self-improvement via experience and data usage, has proven an unrivaled tool to detect and forecast data patterns and decode and extract information from images. Furthermore, machine learning has been combined with physical and chemical sciences to build new fundamental principles and media. The integration of nanophotonics-enabled optical data storage with emerging machine learning technologies promises new methods for high-resolution, accurate, fast, and robust optical data writing and reading, as well as the discovery, design, and optimization of nanomaterials and nanostructures with new functionalities for next-generation nanophotonics-enabled optical data storage. In this Perspective, we review advances in nanophotonics-enabled optical data storage and discuss the role of machine learning in next-generation nanophotonics-enabled optical data storage.

Report of Task Group 201 of the American Association of Physicists in Medicine: Quality management of external beam therapy data transfer.

With the advancement of data-intensive technologies, such as image-guided radiation therapy (IGRT) and intensity-modulated radiation therapy (IMRT), the amount and complexity of data to be transferred between clinical subsystems have increased beyond the reach of manual checking. As a result, unintended treatment deviations (e.g., dose errors) may occur if the treatment system is not closely monitored by a comprehensive data transfer quality management program (QM). This report summarizes the findings and recommendations from the task group (TG) on quality assurance (QA) of external beam treatment data transfer (TG-201), with the aim to assist medical physicists in designing their own data transfer QM. As a background, a section of this report describes various models of data flow (distributed data repositories and single data base systems) and general data test characteristics (data integrity, interpretation, and consistency). Recommended tests are suggested based on the collective experience of TG-201 members. These tests are for the acceptance of, commissioning of, and upgrades to subsystems that store and/or modify clinical treatment data. As treatment complexity continues to evolve, we will need to do and know more about ensuring the quality of data transfers. The report concludes with the recommendation to move toward data transfer open standards compatibility and to develop tools that automate data transfer QA.

Scope and Integration of Computational Intelligence in Traditional Power Sector

An electrical power system is an infinite vast complex network of sophisticated equipment and confederate control to ensure a sustainable energy supply. Ever-increasing power demand in the recent decade has made it difficult to maintain its viability. A rapid transformation in the internal architecture of power system infrastructure is the need of the hour to continue the fictitious lifeline. With the passage of time, electricity has become one of the most crucial elements with almost no substitute. Cutting edge energy-efficient technologies and modern generation computational tools can trigger its growth to maximize its potential, which may entirely transform the power sector scenario and make it future-ready. It is predicted that the adoption of artificial intelligence mutually with data science must have a remarkable outcome to incorporate and develop automation and move towards a smart grid by slashing energy consumption, lowering prices, enhance transparency, gear up efficiency and boost clean green renewable sources globally. AI can further improve the planning, operation, and intelligent control of power systems. Data-intensive technologies can be introduced in diverse dimensions of the electricity value chain following an authentic road map which may considerably reduce the conventional challenges and create significant value. In this work, the authors have attempted to study, investigate, and explore the Power System's present outline in context to India and summarize future possibilities, difficulties, and specific outcome in a systematic, logical manner. This novel work shall benefit distinct researchers and dynamic academicians to get a fundamental idea and strengthen their existing knowledge over the subject. Keyword : Energy, power system, AI, data science, cloud computing, automation, management

Artificial intelligence to support the integration of variable renewable energy sources to the power system

The power sector is increasingly relying on variable renewable energy sources (VRE) whose share in energy production is expected to further increase. A key challenge for adopting these energy sources is their high integration costs. Artificial intelligence (AI) solutions and data-intensive technologies are already used in different parts of the electricity value chain and, due to the growing complexity and data generation potential of the future smart grid, have the potential to create significant value in the system. However, different uncertainties or lack of understanding about its impact often hinder the commitment of decision makers to invest in AI and data intensive technologies, also in the energy sector. While previous work has outlined a number of ways AI solutions can be used in the power sector, the goal of this article is to consider the value creation potential of AI in terms of managing VRE integration costs. We use an economic model of variable renewable integration cost from the literature to present a systematic review of how AI can decrease substantial integration costs. We review a number of use cases and discuss challenges estimating the value creation of AI solutions in the power sector.

Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records.

Electronic health record (EHR) data are widely used to perform early diagnoses and create treatment plans, which are key areas of research. We aimed to increase the efficiency of iteratively applying data-intensive technology and verifying the results for complex and big EHR data. We used a system entailing sequence mining, interpretable deep learning models, and visualization on data extracted from the MIMIC-IIIdatabase for a group of patients diagnosed with heart disease. The results of sequence mining corresponded to specific pathways of interest to medical staff and were used to select patient groups that underwent these pathways. An interactive Sankey diagram representing these pathways and a heat map visually representing the weight of each variable were developed for temporal and quantitative illustration. We applied the proposed system to predict unplanned cardiac surgery using clinical pathways determined by sequence pattern mining to select cardiac surgery from complex EHRs to label subject groups and deep learning models. The proposed system aids in the selection of pathway-based patient groups, simplification of labeling, and exploratory the interpretation of the modeling results. The proposed system can help medical staff explore various pathways that patients have undergone and further facilitate the testing of various clinical hypotheses using big data in the medical domain.