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This paper is available on arxiv under CC 4.0 license. Authors: (1) Ayei E. Ibor, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (2) Mark Hooper, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (3) Carsten Maple, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (4) Gregory Epiphaniou, WMG, University of Warwick, United Kingdom. Table of Links Abstract & Introduction Background Review of Literature Methodology Discussion of Findings Conclusion and Future Work & References 6. Conclusion and Future Work Trustworthy interoperability is a key enabler for identity management and widens the dimensions of e-government in developing countries. This research provided insights into the challenges, opportunities, and requirements of interoperability and highlighted the limitations of current interoperability solutions such as the Estonian X-Road and the eIDAS of the European Union. We found that each existing interoperability solution has its benefits and drawbacks, although both are targeted at a common goal. To achieve seamless communication of identification data, we proposed an approach for scoping requirements by first identifying the key processes to identity management in an interoperability context. Furthermore, we addressed the limitations of the current solutions using an architecture that considers the flexibility of identity verification and validation through an interoperable layer that is based on four interacting layers of data representation, semantics, binding, and security and privacy. For future work, an analysis of country-specific use cases for achieving interoperability in the African identity ecosystem will be performed. References African Union, 2020. The Digital Transformation Strategy for Africa (2020-30), Available at https://openresearch-repository.anu.edu.au/bitstream/1885/278587/1/GhaEcon_24.pdf, Accessed 20 May 2023. Alamillo, I., Mouille, S., Rock, A., Soumelidis, N., Tabor, M., Gorniak, S., 2023. Digital identity standards: Analysis of standardisation requirements in support of cybersecurity policy, Available at file:///C:/Users/aIbor/Downloads/Digital_Identity_Standards%20(1).pdf, Accessed 30 July 2023. Aldosary, M. and Alqahtani, N., 2021. A Survey on Federated Identity Management Systems Limitation and Solutions. International Journal of Network Security & Its Applications (IJNSA) Vol, 13. ANSSI, 2023. The EIDAS Regulation, Available at https://www.ssi.gouv.fr/en/regulation/digital-confidence/the-eidas-regulation/, Accessed 09 April 2023. 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Trustworthy Interoperability and Identity Management Systems: What Comes Next

This paper is available on arxiv under CC 4.0 license. Authors: (1) Ayei E. Ibor, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (2) Mark Hooper, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (3) Carsten Maple, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (4) Gregory Epiphaniou, WMG, University of Warwick, United Kingdom. Table of Links Abstract & Introduction Background Review of Literature Methodology Discussion of Findings Conclusion and Future Work & References 5. Discussion of Findings The limitations of the current interoperability solutions, that is, eIDAS and X-Road show that there is a plethora of issues and complexities, which are yet to be addressed to achieve interoperability at the foundational level of identity management. Country-specific legislation and vendor-locked systems in developing countries add to these complexities. From the findings, the main limitations of the current interoperability solutions can be summarised as follows: i. Each registry implements its interfaces independently based on the use of a proprietary protocol equivalent to the technology in use. This results in the implementation of new interfaces, sometimes from scratch for new or evolving services. ii. There are different trust levels based on the implemented security architecture for verifying the integrity and authenticity of the data. iii. Incompatible certification authorities arise from different trust levels and legal systems of the trust service providers. iv. Interoperability is based on the signing of bilateral agreements, which are non-trivial and can result in several discrepancies in regulations and policies. v. There is no provision that the network must be accessible to private entities and as such may build inter-government competition on trust services e.g., eIDAS. vi. There is differentiation in notified eID schemes and authentication mechanisms leading to re-identification for public services, healthcare, or financial transactions e.g., eIDAS. These limitations imply that the interoperability of identity systems requires open standards with strong legal, regulatory, and governance structures. Also, there must be mechanisms to mitigate risks to the security and privacy of identification data including consent considerations for data use or sharing by the ID holder as outlined in Alamillo et al. (2023) and Srinivas, Das and Kumar (2019). From the findings, developing countries must also make provisions for a single, consolidated, and standardized view of civil registrations and identification data that constitute a single source of truth. This will enhance the onboarding of citizens and create a robust verification and validation process that does not require several levels of authentication that may increase the overhead of the identity system. Additionally, findings showed that the standardization of the structure and attributes of identity data such as name, date of birth, email, and several other relevant attributes to conform with the W3C recommendation should be a key consideration for interoperability. Enforcing the unicity and singularity of identification data will also ensure that ID holders do not have multiple identities that can hamper interoperability. Similarly, to enable the secure exchange of data or identity assertions, it was also found that developing countries must establish trust relationships through federation protocols that can foster interoperability. Interoperability portrays tremendous benefits to e-Government. We found that interoperability widens the dimensions of e-Government in cross-border identity management and data services. It also helps to provide open and accessible digital public services including systems and processes that allow people to move freely within the developing countries while also utilising public services outside their country of origin. Interoperability also helps to create sustainability, and economies of scale as demonstrated by X-Road and eIDAS (Hoffmann and Solarte-Vasquez, 2022; McBride et al., 2019; Schmidt and Krimmer, 2022). We found that while X-Road provides trustworthy data exchange using security servers that allow its members to communicate directly, it does not perform the verification and validation of the identification data that is part of the data exchange. There are also concerns about the limited amount of notified eID schemes under eIDAS, which builds on the limited scope of the eID schemes and the lack of relevant public services. These concerns underpin the need for the review of these interoperability solutions to underscore the notion of cross-border verification and validation of identification data for seamless data exchange. Building on the evidence from various sources as discussed in the Literature, an architecture for trustworthy cross-border interoperability is proposed in this work. This architecture is represented in Figure 4. From Figure 4, each citizen or prospective ID holder undergoes identity enrolment, which can include civil registration through online or offline capture procedures. The enrolment of the citizen is based on regulations and standards of the foundational identity system of the issuing country represented in Figure 4 as 𝑁𝐼𝐷𝑆௫. At the issuance of the ID credential, the citizen or ID holder presents such a document for establishing and proofing his/her identity to a requesting service provider, identity provider or relying party. Authentication and authorisation services are then used to complete the identity proofing process to allow an ID holder access to a service or resource at the point of access. In a cross-border use case, where a foundational identity system 𝑁𝐼𝐷𝑆௫ communicates with another system, say, in a federated identity ecosystem 𝐹𝑒𝑑ூ஽, or where an ID holder 𝑈ூ஽ requests for a service from the latter, then the processes of verifying and validating the identity of 𝑈ூ஽ should be an integral component of an interoperable identity system unlike in X-Road where such processes are the functions of the service provider/consumer. Verification and validation ensure that the claimed identity is true and belongs to the claimant at the time of the request and throughout service delivery. To achieve this, there is the need to have a trustworthy link that considers the representation of the data, semantics, binding, and the security and privacy of the identification data of 𝑈ூ஽. The relevance of the trust link at the interoperable layer is to ensure that the identity data as well as the requested resource or service maintains its integrity, security, privacy, and confidentiality throughout data exchange. We propose that the representation of the identification data must be data format agnostic as obtainable in X-Road using simple object access protocol (SOAP) and representational state transfer (REST) (Halili and Ramadani, 2018; Krimmer et al., 2021; Priisalu and Ottis, 2017). Also, the representation of credentials on the Web should be in a way that is machineverifiable, private, and cryptographically secure. The semantic interpretation of ID credentials must be unambiguous. That is, verifying credentials and presentations and cryptographically securing them both require predictable, bi-directional, and lossless processes. To be processed in an interoperable manner, any verification of a credential or presentation must be deterministic. The resulting credential or presentation must be semantically and syntactically equivalent to the original construct (Sedlmeir et al., 2021; W3C Recommendation, 2022). Likewise, each verified credential of 𝑈ூ஽ must be bound to its identity to a given level of assurance. This establishes an unbreakable link between the subject (𝑈ூ஽) and the credential to enforce identity disambiguation. Binding is relevant for the verification and validation of the claimed identity at cross-border entry/exit points. Finally, the security and privacy of the identification data are significant as it forms the integral component of the required trustworthiness for interoperability. There are several approaches for implementing the security and privacy of identification data such as the use of Transport Layer Security (TLS), Security Assertion Markup Language (SAML), authentication keys, etc. (for security), secure computation mechanisms, trusted third party, differential privacy, etc. (for privacy) (Grassi, Garcia and Fenton, 2017; Kaaniche, Laurent and Belguith, 2020). We posit that the use of secure computation mechanisms, data minimisation, and differential privacy in a cross-border context can fulfil the required privacy requirements due to the multifaceted risks associated with the exchange of data between interoperating entities. This paper is available on under CC 4.0 license. Arxiv

A Trustworthy Cross-Border Interoperable Identity System: What Our Findings Mean

This paper is available on arxiv under CC 4.0 license. Authors: (1) Ayei E. Ibor, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (2) Mark Hooper, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (3) Carsten Maple, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (4) Gregory Epiphaniou, WMG, University of Warwick, United Kingdom. Table of Links Abstract & Introduction Background Review of Literature Methodology Discussion of Findings Conclusion and Future Work & References 3. Review of Literature Although interoperability has the potential to create a seamless identity ecosystem for data exchange, verification, and validation of digital identities, and expand the reach of national governments for effective service delivery, there are very few research-based approaches for cross-border interoperable identity systems. Backhouse and Halperin (2009) posited that the challenge of establishing interoperable systems is enormous, which does not only consider the technical linking of databases and systems. To this effect, they proposed a three-fold conception of interoperability for identity management systems viz-a-viz technical, legal, and regulatory components to enhance data sharing in the provision of e-government. The factors militating against the full interoperability of federated identity management systems are studied in Catuogno and Galdi (2014). They argued that the tendency for per-site authentication and authorization of ID holders culminates in huge overhead for both the identity/service providers and the ID holders as each site stores different credentials of the user. Therefore, the authors presented Shibboleth as the de facto standard for identity management and point of access to providers of information (PAPI) as a solution that leverages the joining of federations and translation of protocols during cross-federation authentication and authorization (AA) sessions. Sharma and Panigrahi (2015) proposed a roadmap useful to plan and implement the capabilities of interoperability in e-government solutions. The roadmap considered the notion of knowledge sharing among key stakeholders based on vital legal, regulatory, technical, and organisational components that can foster the interoperability of e-government services. One significant limitation of this research is its inability to explore cross-country differences in legislation and regulatory requirements, as it focused only on the inputs from stakeholders in India. Also, Kotzé and Alberts (2017) proposed a baseline conceptual model to achieve an egovernment interoperability framework. The model considered the technical, legislative, social, and political environments of South Africa to serve as a guideline for enterprises that are evolving towards e-governance. Similar studies were carried out by Kanagwa et al. (2018), who investigated the relevance of a national enterprise architecture to support several e-government systems including the systems for the registration of persons in Uganda through semantic interoperability that is achieved based on a set of related ontologies. Still, in both studies, country-specific technical, legislative, social, and political considerations were made, which indicate the absence of cross-border considerations to interoperability, Saputro et al. (2020) discussed the Estonian XRoad as an e-governance solution for secure data exchange and the interoperability of information systems across nine countries. This notwithstanding, this research did not categorise whether the countries investigated are developed or developing to ascertain the level of inclusion or exclusion required for facilitating e-governance. In Hölbl, Kežmah and Kompara (2023), the interoperability and compliance issues in eIDAS are discussed. The authors asserted that eIDAS has limited applicability in the public sector since the regulation does not address new market demands with the added complexity of private online providers connecting to the eIDAS network. Further, the issue of the isolation and inflexibility of notified eID solutions in member states makes it difficult for eIDAS to support a variety of use cases. Domingo and Teevan (2022) discussed the interoperability of cross-border payment solutions to expand trade in Africa. They argued that the success of the African Continental Free Trade Area (AfCFTA) depends largely on the cross-border interoperability of low-value instant payment systems such as mobile money. Consequently, the authors posited that such a policy will enhance inclusion and long-lasting benefits to small-scale businesses. Additionally, Masiero (2023) argued that the effectiveness of digital identity schemes depends on platform features, which must be considered to comprehend the true extent of harm that digital identification might cause due to interoperability. Furthermore, Benaddi et al. (2023) identified the challenges in data sharing and the interoperability of e-government systems. Their focus was on the technical interoperability of e-government entities to enhance collaboration in the use of public data. However, their approach does not consider crossborder perspectives to data sharing including the identification of entities that consume these data. This paper is available on [Arxiv](https://This paper is available on arxiv under CC 4.0 license.) under CC 4.0 license.

A Trustworthy Cross-Border Interoperable Identity System: A Review of the Literature

This paper is available on arxiv under CC 4.0 license. Authors: (1) Ayei E. Ibor, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (2) Mark Hooper, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (3) Carsten Maple, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (4) Gregory Epiphaniou, WMG, University of Warwick, United Kingdom. Table of Links Abstract & Introduction Background Review of Literature Methodology Discussion of Findings Conclusion and Future Work & References Abstract Foundational identity systems (FIDS) have been used to optimise service delivery and inclusive economic growth in developing countries. As developing nations increasingly seek to use FIDS for the identification and authentication of identity (ID) holders, trustworthy interoperability will help to develop a cross-border dimension of e-Government. Despite this potential, there has not been any significant research on the interoperability of FIDS in the African identity ecosystem. There are several challenges to this; on one hand, complex internal political dynamics have resulted in weak institutions, implying that FIDS could be exploited for political gains. On the other hand, the trust in the government by the citizens or identity holders is habitually low, in which case, data security and privacy protection concerns become paramount. In the same sense, some FIDS are technology-locked, thus interoperability is primarily ambiguous. There are also issues of cross-system compatibility, legislation, vendor-locked system design principles and unclear regulatory provisions for data sharing. Fundamentally, interoperability is an essential prerequisite for e-Government services and underpins optimal service delivery in education, social security, and financial services including gender and equality as already demonstrated by the European Union. Furthermore, cohesive data exchange through an interoperable identity system will create an ecosystem of efficient data governance and the integration of cross-border FIDS. Consequently, this research will seek to identify the challenges, opportunities, and requirements for cross-border interoperability in an African context. Our findings show that interoperability in the African identity ecosystem is vital to strengthen the seamless authentication and verification of identity holders for inclusive economic growth and widen the dimensions of e-Government across the continent. Keywords: cross-border interoperability, digital identity, trustworthiness, e-Government 1. Introduction One of the Sustainable Development Goals (SDGs) of the United Nations is to provide legal identity for all citizens, which also includes birth registration by 2030 (World Bank Group, 2018). Primarily, identification allows individuals to exercise their rights to be identified and legally recognised while the government and private sector depend on it for effective service delivery. In this sense, a national identity system is crucial to the identification and authentication of natural or legal persons and provides a means for citizens to access critical services including participation in formal political, social, and economic life (Gelb and Metz, 2018). Achieving sustainable development is a key consideration of most national governments and forms the basis for inclusive and responsible identification schemes at the foundational level. Fundamentally, national identification schemes are the pivot for social security, immigration, financial and economic inclusion, healthcare, voting, gender equality, transportation, and education (Colbern and Ramakrishnan, 2018). Consequently, identity management plays a key role in promoting e-government by bringing services closer to the people. Modern identification systems are robust tools for the delivery of transparent administration, reduction in fraud and leakages for social benefits, providing adequate security for the citizens, extracting accurate biographic data for effective economic planning, and responding to natural disasters (Atick, 2016). These seeming benefits are yet to solve the identification crisis in most developing nations as about 850 million people across the globe are yet to have access to a valid identity credential, mostly in sub-Saharan Africa and South Asia (The World Bank, 2023a). According to the World Bank Identification for Development (ID4D) dataset (The World Bank, 2023a), most people without official identification are residents of low-income countries. These sets of persons also include marginalised and vulnerable groups consisting of children, whose births were not documented in the civil registry of the affected country, women in rural areas with no access to digital services, and adults below the age of 25 years. Particularly, digital identification systems can improve how the public and commercial sectors provide services and lay the groundwork for new markets, services, and systems, such as egovernment, cashless transactions, and the digital economy. Identification systems must, however, have high levels of coverage and inclusion within the population, be resilient to fraud and error, operate within a governance framework that protects personal data, fosters trust and accountability, and facilitates end-user control to fulfil their potential for facilitating sustainable development and increasing public sector efficiency (African Union, 2020; Bandura and Ramanujam, 2021). There is a growing demand for digital identity to be mutually recognised and portable between countries in the modern digital age through an interoperability framework and in the context of regional and global integration and migration, which can be facilitated through trust and standards. Interoperability, in this context, refers to the ability of disparate foundational identity systems to exchange data through seamless communication of identification and authentication information. These exchanges must be trustworthy or inherently secure, available, and reliable. Although there are complex internal political dynamics in most developing countries that result in weak institutions, and the lack of trust in government by the citizens including the notion of security and privacy protection concerns, legislation, cross-system compatibility, vendor-locked system design principles and unclear regulatory provisions for data sharing, our findings show that interoperability is vital to widen the dimensions of e-Government. In this paper, the challenges, opportunities, and requirements for trustworthy cross-border interoperability are discussed. The rest of the paper is organised as follows; in Section 2, the background to the study is given while in Section 3, a review of literature is discussed. Section 4 presents the methodology of the research. In Section 5, the discussion of findings is presented, and the conclusion and future work is given in Section 6. This paper is available on under CC 4.0 license. Arxiv

A Trustworthy Cross-Border Interoperable Identity System for Developing Countries: The Beginning

This paper is available on arxiv under CC 4.0 license. Authors: (1) Samie Mostafavi, ssmos, KTH Royal Institute of Technology; (2) Vishnu Narayanan Moothedath, vnmo, KTH Royal Institute of Technology; (3) Stefan Ronngren, steron, KTH Royal Institute of Technology; (4) Neelabhro Roy, §nroy, KTH Royal Institute of Technology; (5) Gourav Prateek Sharma, gpsharma, KTH Royal Institute of Technology; (6) Sangwon Seo, sangwona, KTH Royal Institute of Technology; (7) Manuel Olgu´ın Munoz, manual@olguinmunoz.xyz, KTH Royal Institute of Technology; (8) James Gross, jamesgr, KTH Royal Institute of Technology. Table of Links Abstract & Introduction Testbed Design and Architecture Experimental Testbed Validation Supported Experimentation Acknowledgements & References IV. SUPPORTED EXPERIMENTATION This section delves into the features of the testbed that users can customize to meet their unique experimental goals. These features can be broadly categorized into two types: (1) configurations related to workload and (2) configurations related to the network. In terms of workload-related configurations, ExPECA offers researchers a range of options to tailor to their experiments. Users can select from various geographical locations for computational workloads using the cloud interface and by reserving public IPs. It is possible to integrate GPUs for tasks requiring accelerated processing, or choose a CPU with a certain number of cores, and allocate a specific amount of RAM to benchmark workload performance. The platform supports experiments involving both static (fixed-location) and mobile (varying-location, e.g., drone with a wireless connectivity dongle) wireless end nodes. For network-related configurations, ExPECA provides a diverse array of network types, such as SDR 5G, SDR LTE, SDR WiFi, and COTS 5G, among others. Researchers can also customize network topologies and control interference levels, thanks to the testbed’s isolated location. The platform supports a wide range of wireless protocols compatible with USRP e320 and allows users to configure channel conditions. Additionally, the use of Software Defined Radios (SDRs) offers further flexibility, including control over transmit power and the ability to implement a variety of wireless protocols. ExPECA offers the opportunity to explore various experimentation scenarios involving edge computing, wireless communication, and machine learning research. Here we explain various scenarios, each shedding light on some of the interesting aspects of wireless communication and/or edge computing offering unique insights into the dynamic landscape of edge computing. In Figure 6, we provide an illustration that helps a reader to visualize these scenarios. Below, we provide a few of such use cases in various research areas. While some of these aim at direct measurements for data-driven modeling and prediction, others target initial observations for forthcoming theoretical work or the verification and validation of existing theoretical models. A. Optimization of Closed-Loop Applications With the booming interest and applications surrounding cyber-physical systems, it is imperative to study Networked Control Systems (NCSs) [8]. NCSs form crucial control systems where the control loops are closed through a communication network. Due to the safety-critical nature of the applications these NCSs encompass, such as robots, UAVs etc., there is a need to benchmark and understand the bottlenecks/boundaries associated with these applications [9]. Namely, the latency and reliability boundaries of robotic control are crucial to understand before they can be deployed in real-world scenarios, where their potential failure might prove expensive [10]. Another such feedback system where latency is of importance are human-in-the-loop applications such as augmented reality, wearable cognitive assistants (WCA) [9], [11], and ambient safety. In the context of criticality, we have systems of automated fault detection, in which case the acoustic [12] or motion amplified visual [13], [14] data is processed for vibration analysis to potentially trigger some maintenance, safety or emergency procedures [12]. The ExPECA testbed provides developers with the opportunities to experiment with workloads corresponding to these types of applications in a containerized manner with various real network architectures. These experiments could either be for data collection or for verification of existing theoretical models. B. Time-sensitive Wireless Networking Supporting the above-mentioned CPS and HITL applications requires the network to offer a certain level of endto-end network latency, often combined with extremely high reliability, in order to operate correctly and safely. This has spurred various efforts to integrate wireless networking technologies (e.g., 5G URLLC) with wired technologies (e.g., TSN) as shown in Figure 7. However, an efficient wiredwireless integration realization to support time-critical applications necessitates accurate system characterization. In particular, latency characterization of wireless segments is crucial as they are subject to various stochastic and temporal influences, unlike wired links where latencies are immune to external influences. Furthermore, accurate predictions of wireless latency are also envisioned to be leveraged in 6G networks to support deterministic communications [15]. Lately, several data-driven methods for latency prediction have been proposed [16], [17]. Training and validating these approaches require a vast amount of datasets comprising latency measurements alongside network parameters (e.g., SINR and MCS). Precise time-synchronization of all nodes of the testbed ensures accurate latency measurements can be collected between any two nodes of the testbed. These experiments can employ diverse wireless communication technologies, including COTS 5G, software-defined 5G and IEEE 802.11g Wi-Fi integrated in ExPECA. Furthermore, the containerized workflow within ExPECA enables users to perform automated, reproducible and repetitive latency measurement experiments. C. Hierarchical Learning and Inference Another set of experiments researchers can use the ExPECA testbed for is the verification of theoretical results corresponding to Hierarchical Learning (HI) [18], [19]. In these works, authors develop the idea of Hierarchical Inference, where a compute constrained edge device chooses to perform local inference or classification of an image sample, or offloading via a wireless channel to get help of a high power edge server for classification. A small-sized ML model with limited accuracy is assumed to have been deployed at the edge device and offloading to the server incurs cost. In essence, these policies aim to make intelligent decisions regarding task offloading, prioritizing complex tasks or tasks where accuracy is paramount. Conversely, they process simpler tasks or tasks where reduced latency is of utmost importance locally. It is to be noted that in this specific work on Hierarchical Inference this decision is made in an online manner. Using the ExPECA testbed, researchers can plan to implement the various algorithms proposed and verify if the real-world results match simulations and models. D. Federated Learning Validation Federated learning (FL) is emerging as one of the key approaches for training machine learning models [20]. FL aims to improve over centralized learning in terms of privacy, total training time, computation overhead, etc. However, FL can cause large communication overhead because the machine learning model should be repeatedly exchanged between the central server and the participating nodes during the training process. In order to analyze and optimize communication overhead, many works have proposed models for FL systems operating in wireless environments [21], [22]. Validating these theoretical models in real systems is required to reveal otherwise unavailable insights. The ExPECA testbed supports experimentation involving both centralized learning (CL) and federated learning to evaluate them in terms of communication/computation overhead and accuracy with various wireless conditions. For example in case of communication overhead, CL and FL will consume communication resources when exchanging data (i.e., using uplink to send input data in CL and uplink/downlink to send machine learning model in FL). The ExPECA testbed is able to measure the communication resource usage, and training time (communication/computation time), and capture wireless parameters at that time. Thanks to improved reproducibility and repeatability, the ExPECA testbed supplies stable validation for machine learning architectures.

ExPECA: An Experimental Platform for Trustworthy: Supported Experimentatation

This paper is available on arxiv under CC 4.0 license. Authors: (1) Samie Mostafavi, ssmos, KTH Royal Institute of Technology; (2) Vishnu Narayanan Moothedath, vnmo, KTH Royal Institute of Technology; (3) Stefan Ronngren, steron, KTH Royal Institute of Technology; (4) Neelabhro Roy, §nroy, KTH Royal Institute of Technology; (5) Gourav Prateek Sharma, gpsharma, KTH Royal Institute of Technology; (6) Sangwon Seo, sangwona, KTH Royal Institute of Technology; (7) Manuel Olgu´ın Munoz, manual@olguinmunoz.xyz, KTH Royal Institute of Technology; (8) James Gross, jamesgr, KTH Royal Institute of Technology. Table of Links Abstract & Introduction Testbed Design and Architecture Experimental Testbed Validation Supported Experimentation Acknowledgements & References V. ACKNOWLEDGEMENTS This research has been partially funded by (1) the VINNOVA Competence Center for Trustworthy Edge Computing Systems and Applications (TECoSA) at KTH Royal Institute of Technology; and (2) the Swedish Foundation for Strategic Research (SSF), through grant number ITM17–0246. REFERENCES [1] D. Raychaudhuri, I. Seskar, G. Zussman, T. Korakis, D. Kilper, T. Chen, J. Kolodziejski, M. Sherman, Z. Kostic, X. Gu, H. Krishnaswamy, S. Maheshwari, P. Skrimponis, and C. Gutterman, Challenge: COSMOS: A City-Scale Programmable Testbed for Experimentation with Advanced Wireless. New York, NY, USA: Association for Computing Machinery, 2020. [2] J. Yu, T. Chen, C. Gutterman, S. Zhu, G. Zussman, I. Seskar, and D. Kilper, “Cosmos: Optical architecture and prototyping,” in 2019 Optical Fiber Communications Conference and Exhibition (OFC), pp. 1– 3, 2019. [3] J. Breen, A. Buffmire, J. Duerig, K. Dutt, E. Eide, M. Hibler, D. Johnson, S. K. Kasera, E. Lewis, D. Maas, A. Orange, N. Patwari, D. Reading, R. Ricci, D. Schurig, L. B. Stoller, J. Van der Merwe, K. Webb, and G. Wong, “POWDER: Platform for open wireless data-driven experimental research,” in Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (WiNTECH), Sept. 2020. [4] H. Zhang, Y. Guan, A. Kamal, D. Qiao, M. Zheng, A. Arora, O. Boyraz, B. Cox, T. Daniels, M. Darr, et al., “Ara: A wireless living lab vision for smart and connected rural communities,” in Proceedings of the 15th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization, pp. 9–16, 2022. [5] K. Keahey, J. Anderson, Z. Zhen, P. Riteau, P. Ruth, D. Stanzione, M. Cevik, J. Colleran, H. S. Gunawi, C. Hammock, J. Mambretti, A. Barnes, F. Halbach, A. Rocha, and J. Stubbs, “Lessons learned from the Chameleon Testbed,” in Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20), USENIX Association, July 2020. [6] K. Keahey, J. Anderson, M. Sherman, C. Hammock, Z. Zhen, J. Tillotson, T. Bargo, L. Long, T. Ul Islam, S. Babu, H. Zhang, and F. Halbach, “Chi-in-a-box: Reducing operational costs of research testbeds,” in Practice and Experience in Advanced Research Computing, PEARC ’22, (New York, NY, USA), Association for Computing Machinery, 2022. [7] S. George, T. Eiszler, R. Iyengar, H. Turki, Z. Feng, J. Wang, P. Pillai, and M. Satyanarayanan, “Openrtist: End-to-end benchmarking for edge computing,” IEEE Pervasive Computing, vol. 19, no. 4, pp. 10–18, 2020. [8] X.-M. Zhang, Q.-L. Han, X. Ge, D. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: a survey of trends and techniques,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 1, pp. 1–17, 2020. [9] M. Olgu´ın Munoz, ˜ An Emulation-Based Performance Evaluation Methodology for Edge Computing and Latency Sensitive Applications. PhD thesis, KTH Royal Institute of Technology, 2023. [10] M. Olgu´ın Munoz, N. Roy, and J. Gross, “CLEAVE: Scalable and edge- ˜ native benchmarking of networked control systems,” in Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking, EdgeSys ’22, (New York, NY, USA), pp. 37–42, Association for Computing Machinery, 2022. [11] K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan, “Towards wearable cognitive assistance,” in Proc. of the 12th Annual International Conference on Mobile Systems, Applications, and Services, p. 68–81, Association for Computing Machinery, 2014. [12] B. Lu, Y. Li, X. Wu, and Z. Yang, “A review of recent advances in wind turbine condition monitoring and fault diagnosis,” in IEEE Power Electronics and Machines in Wind Applications, pp. 1–7, 2009. [13] T.-H. Oh, R. Jaroensri, C. Kim, M. Elgharib, F. Durand, W. T. Freeman, and W. Matusik, “Learning-based video motion magnification,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 633–648, 2018. [14] C. Liu, A. Torralba, W. T. Freeman, F. Durand, and E. H. Adelson, “Motion magnification,” ACM transactions on graphics (TOG), vol. 24, no. 3, pp. 519–526, 2005. [15] G. P. Sharma, D. Patel, J. Sachs, M. De Andrade, J. Farkas, J. Harmatos, B. Varga, H.-P. Bernhard, R. Muzaffar, M. K. Atiq, et al., “Towards Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward,” arXiv preprint arXiv:2304.01299, 2023. [16] S. Mostafavi, G. P. Sharma, and J. Gross, “Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities,” arXiv preprint arXiv:2307.10648, 2023. [17] M. Skocaj, F. Conserva, N. S. Grande, A. Orsi, D. Micheli, G. Ghinamo, S. Bizzarri, and R. Verdone, “Data-driven predictive latency for 5g: A theoretical and experimental analysis using network measurements,” arXiv preprint arXiv:2307.02329, 2023. [18] V. N. Moothedath, J. P. Champati, and J. Gross, “Online algorithms for hierarchical inference in deep learning applications at the edge,” 2023. [19] G. Al-Atat, A. Fresa, A. P. Behera, V. N. Moothedath, J. Gross, and J. P. Champati, “The case for hierarchical deep learning inference at the network edge,” in Proceedings of the 1st International Workshop on Networked AI Systems, NetAISys ’23, (New York, NY, USA), Association for Computing Machinery, 2023. [20] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54 of Proceedings of Machine Learning Research, pp. 1273–1282, PMLR, 20–22 Apr 2017. [21] H. Gao, A. Xu, and H. Huang, “On the Convergence of CommunicationEfficient Local SGD for Federated Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 7510–7518, May 2021. [22] J. Perazzone, S. Wang, M. 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ExPECA: An Experimental Platform for Trustworthy: Acknowledgements & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Samie Mostafavi, ssmos, KTH Royal Institute of Technology; (2) Vishnu Narayanan Moothedath, vnmo, KTH Royal Institute of Technology; (3) Stefan Ronngren, steron, KTH Royal Institute of Technology; (4) Neelabhro Roy, §nroy, KTH Royal Institute of Technology; (5) Gourav Prateek Sharma, gpsharma, KTH Royal Institute of Technology; (6) Sangwon Seo, sangwona, KTH Royal Institute of Technology; (7) Manuel Olgu´ın Munoz, manual@olguinmunoz.xyz, KTH Royal Institute of Technology; (8) James Gross, jamesgr, KTH Royal Institute of Technology. Table of Links Abstract & Introduction Testbed Design and Architecture Experimental Testbed Validation Supported Experimentation Acknowledgements & References III. EXPERIMENTAL TESTBED VALIDATION This section aims to substantiate two key assertions about ExPECA: its efficiency in facilitating end-to-end experiments and its ability to ensure experimental reproducibility[1]. To validate the first claim, we employ OpenRTiST [7], an open-source application that is computationally demanding, bandwidth-intensive, and latency-sensitive. OpenRTiST uses machine learning filtering to overlay various artistic styles onto video frames, creating an augmented reality experience. In this experiment, the OpenRTiST client connects to the server either at the edge, specifically the testbed servers, or in the cloud via a server located at Carnegie Mellon University (CMU), in Pittsburgh, Pennsylvania, through a COTS 5G system and the adv-03 node. In Figure 4, we depict the corresponding critical performance indicators such as frames per second (FPS) and round-trip time (RTT) measured using the testbed. In Figure 4a, we plot the Cumulative Distribution Function (CDF) of FPS, and in Figure 4a we plot the Complementary CDF (CCDF) of RTT. As we move from edge to cloud, RTT significantly increases which results in lower refresh rates and less responsive video stream. The performance is benchmarked against different video frame sizes to analyze network and compute capacity. For the second claim, we concentrate on wireless network latency, a metric inherently challenging to model due to its stochastic nature at the packet level. While latency can be influenced by factors like transmit power and channel interference, it also depends on wireless protocol settings such as modulation schemes, bandwidth, and antenna configurations. ExPECA’s isolated location minimizes variability in channel quality, mitigating potential interference. In Figure 5, we present latency measurements across different radio links over several days, demonstrating the reproducibility level in the case of COTS 5G wireless links. The close similarity between these CCDFs confirms the reproducibility of wireless latency measurements achieved through ExPECA. [1] Experiments available as Python Jupyter notebooks at https://github.com/KTH-EXPECA/examples

ExPECA: An Experimental Platform for Trustworthy: Experimental Testbed Validation

This paper is available on arxiv under CC 4.0 license. Authors: (1) Samie Mostafavi, ssmos, KTH Royal Institute of Technology; (2) Vishnu Narayanan Moothedath, vnmo, KTH Royal Institute of Technology; (3) Stefan Ronngren, steron, KTH Royal Institute of Technology; (4) Neelabhro Roy, §nroy, KTH Royal Institute of Technology; (4) Gourav Prateek Sharma, gpsharma, KTH Royal Institute of Technology; (4) Sangwon Seo, sangwona, KTH Royal Institute of Technology; (4) Manuel Olgu´ın Munoz, manual@olguinmunoz.xyz, KTH Royal Institute of Technology; (4) James Gross, jamesgr, KTH Royal Institute of Technology. Table of Links Abstract & Introduction Testbed Design and Architecture Experimental Testbed Validation Supported Experimentation Acknowledgements & References Abstract This paper presents ExPECA, an edge computing and wireless communication research testbed designed to tackle two pressing challenges: comprehensive end-to-end experimentation, and high levels of experimental reproducibility. Leveraging Openstack-based Chameleon Infrastructure (CHI) framework for its proven flexibility and ease of operation, ExPECA is located in a unique, isolated underground facility, providing a highly controlled setting for wireless experiments. The testbed is engineered to facilitate integrated studies of both communication and computation, offering a diverse array of Software-Defined Radios (SDR) and Commercial Off-The-Shelf (COTS) wireless and wired links, as well as containerized computational environments. We exemplify the experimental possibilities of the testbed using OpenRTiST, a latency-sensitive, bandwidth-intensive application, and analyze its performance. Lastly, we highlight an array of research domains and experimental setups that stand to gain from ExPECA’s features, including closed-loop applications and time-sensitive networking. Index Terms—Edge computing experimental platform, reproducibility, end-to-end experimentation, wireless testbed I. INTRODUCTION As we move onto the next decade, edge computing applications are poised for significant growth, fueled by advancements in wireless technologies such as 5G and Beyond 5G (B5G), as well as breakthroughs in compute-intensive tasks like model predictive control, multimedia processing, and machine learning algorithms. In order to achieve trustworthiness and dependability in these interdisciplinary, distributed systems, involving computer science and wireless research, it is imperative to develop sophisticated scientific tools. On one hand, research in areas like wireless networking methods necessitates close interaction with radio hardware and a high degree of adaptability, as facilitated by Software Defined Radios (SDRs). Conversely, the computational aspect requires performance workloads to be adaptable to fluctuating network conditions, necessitating the creation and efficient repeatable testing of innovative computing algorithms. End-to-end experimentation is essential for obtaining a comprehensive understanding of the system as a whole and for devising innovative approaches to overcome existing bottlenecks. Additionally, the ability to efficiently replicate these experiments is key to allowing other researchers to easily validate findings. In this realm of research, we find a diverse landscape of testbeds that have emerged in recent years, each distinguished by its size, applicability, and feature set. Notable ones include the 1) COSMOS, 2) POWDER, 3) ARA, and 4) Drexel Grid SDR testbeds. COSMOS (Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment) is a testbed spanning over 1 square mile, equipped with an array of resources, including Software-Defined Radios (SDRs), millimeter-wave (mmWave) equipment, optical fibers, and various computational nodes for core functionality and application processing [1], [2]. COSMOS integrates with cloud infrastructure, with a primary focus on advancing cloud computing research. This testbed includes a diverse set of rooftop, intermediate, and mobile nodes, all centrally controlled. POWDER (Platform for Open Wireless Data-driven Experimental Research) serves as an even larger testbed, offering a platform for exploring wireless and mobile networks with a high level of programmability that extends down to the waveform level [3]. Covering an area of 15 km2 , POWDER comprises fixed, programmable radio nodes constructed using off-the-shelf SDR technology. The platform seamlessly integrates with cloud and edge-like compute capabilities, allowing mobile nodes to tap into the compute resources. The Drexel Grid SDR testbed features SDRs that connect over-the-air (OTA), channel emulator, or alternatively a combination of the two, to enable realistic and reproducible experimentation. Even though the testbed provides freedom in terms of experiment development, it is intended primarily for SDR-related research, and does not integrate any core, cloud or edge components. ARA (The Agricultural and ruRAl communities) represents a wireless research platform, spanning a rural expanse with a diameter of 60 km in Iowa [4]. ARA’s mission is to investigate wireless platforms and technologies within the realworld context of agricultural settings. The testbed incorporates a diverse array of wireless platforms, implemented through SDRs and programmable Commercial Off-The-Shelf (COTS) radios along with automated vehicles and various cameras and other sensors. ARA’s testbed software builds upon the Chameleon Infrastructure (CHI) software framework from the Chameleon testbed project [5]. Chameleon primarily serves as a bare-metal reconfigurable research testbed designed for edge-to-cloud experimentation, adapting the cloud paradigm for computer science research. Since its public unveiling in 2015, Chameleon has accommodated over 6,000 users across more than 800 projects, gaining valuable experience and refining methods for automating and managing research infrastructure sites. This accumulated knowledge has been encapsulated in the CHI-in-a-Box [6] testbed software distribution, which aims to simplify and optimize the deployment and operation of future associated or independent Chameleon sites. Existing wireless research-focused platforms like COSMOS, POWDER, and Drexel Grid employ their own specialized software solutions, which are not in line with prevailing cloud-native standards. These testbeds also utilize virtualization technology based on Virtual Machines (VMs), rather than opting for lighter and more edge-compatible solutions such as containers. Similar to the approach taken by the ARA testbed, we found that CHI-in-a-Box presents an excellent opportunity to develop experimental platforms at minimal cost, while offering significant flexibility in both cloud and edge-native technologies. However, when it comes to reproducibility in wireless edge-computing research, existing large-scale testbeds have limitations that we aim to address by introducing our testbed, ExPECA. By ‘reproducibility’, we mean the capacity for researchers to easily replicate experiments and validate results. Achieving this is particularly challenging for two main reasons: one, edge computing experiments involve a range of heterogeneous components like servers and radios, all interacting within a single experiment; and two, the existence of wireless links introduces variables such as interference and dynamic propagation conditions that must be controlled for consistent results. Owing to its unique design choices, ExPECA provides a highly reproducible environment for endto-end wireless and edge computing experimentation, making it one of the first platforms to effectively address this issue. In the following section, we delve into these design intricacies and their consequential impacts.

ExPECA: An Experimental Platform for Trustworthy Edge Computing Applications: Abstract & Intro

By the authority vested in me as President by the Constitution and the laws of the United States of America, it is hereby ordered as follows: Table of Content Sec 1.  Purpose. Sec. 2.  Policy and Principles. Sec. 3.  Definitions. Sec. 4.  Ensuring the Safety and Security of AI Technology. Sec. 5. Promoting Innovation and Competition. Sec. 6.  Supporting Workers. Sec. 7.  Advancing Equity and Civil Rights. Sec. 8.  Protecting Consumers, Patients, Passengers, and Students. Sec. 9.  Protecting Privacy. Sec. 10.  Advancing Federal Government Use of AI. Sec. 11.  Strengthening American Leadership Abroad. Sec. 12.  Implementation. Sec. 13.  General Provisions. This content was published on October 30, 2023, on . WhiteHouse.gov Attribution - Creative Commons 3.0 Lead image source.

Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence 

Tokenization: The Future of Collectibles or a Pandora's Box of Piracy? Blockchain technology is revolutionizing various sectors, particularly with the advent of tokenization, which converts real-world assets into digital tokens. Imagine having your favorite superhero action figure, but instead of physically, you own it digitally. That's tokenization! But how do you ensure your tokenized superhero isn't being copied and sold multiple times? That's a challenge in the current tokenized asset market – verifying the authenticity of these digital assets. It's akin to knowing if your superhero action figure is an original edition or a knockoff. But like every emerging technology, the tokenized world is not without its issues. The primary one being authenticity. Just imagine you're trading a rare collectible card, say, a shiny Charizard Pokemon card. In the physical world, you can touch it, inspect it, and verify its authenticity. But in the digital world, how do you ensure the tokenized Charizard card you're buying is the only one, and no one else is selling the same card elsewhere? As the trend of tokenizing tangible assets grows, the line between original and duplicated assets blurs. Many enthusiasts believe that tokenization could potentially revolutionize the way we own, trade, and interact with assets. But the vulnerability of digital piracy threatens the authenticity of these tokens. The capacity to replicate and distribute identical tokens jeopardizes the unique value and scarcity of these digital assets, a crucial feature in collectibles markets. Wakweli: Pioneering Authenticity in the Tokenized Future with Web3 Protocol Wakweli is a Web3 protocol designed to certify the authenticity of tokenized assets. Whether it's a piece of digital art or a real estate contract, Wakweli's aim is to ensure that the digital token you hold genuinely represents the asset it claims to represent, effectively increasing trust within the tokenized assets market. : Vested Interest Disclosure The author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program At the heart of Wakweli is the Proof of Democracy (PoD) consensus algorithm. Picture a school's student council election. You cast your vote for a candidate you trust, and the one with the most votes leads the council. PoD works similarly, with $WAKU token holders initiating certificate requests and elected certifiers reviewing and validating these requests. Just like how voters in a democracy trust their elected representatives, $WAKU holders trust the certifiers to validate the assets' authenticity. But why is a secure tokenized future so important? And what about privacy? Let's take a step back and consider this. Imagine you have a diary where you note all your secrets. Tokenization is like taking each secret and turning it into a digital token. You definitely wouldn't want these secrets replicated or exposed, right? That's why we need protocols like Wakweli to ensure the tokens are unique and not duplicated. And as for privacy, the decentralization offered by blockchain technology ensures that your identity remains secure and your secrets remain yours, even when trading tokens. As we progress towards a future increasingly shaped by digital technology, solutions like Wakweli will play an essential role in creating a trustworthy and secure tokenized landscape. By making sure each token truly represents what it claims, Wakweli provides a crucial layer of trust, transforming how we buy, sell, and trade real-world assets in the digital world. Don’t forget to like and share the story!

Embracing a Trustworthy Tokenized Future: A Deep Dive into Wakweli Protocol

NFT and Metaverse make it spicier: high stakes and high rewards with low security make the system vulnerable, as stated by high-tech experts. Is it so? And is there a solution? For more than a year, “NFT-gold rush” is making professional investors, foundations and ordinary people buy, sell and discuss the paradox of NFT: a pixel-art series of - #5822 - for more than $23,000,000; a collection of NBA Top Shots with LeBron James’ “Cosmic” dunk was sold for $208,000; even an act of literal art destruction - burning of the “Morons” original by famous street artist Banksy - was “encased” in NFT and sold for $308 000. CryptoPunks Also, not only images are sold via NFTs. Unique cars, antiques, jewelry, even - they are sold via NFTs on a daily basis. High prices, a sense of technical superiority - all these things stimulate high interest from everyone. But when professional investors buy a bunch of pixels for hundreds of thousands - experts of blockchain, cryptography and digital rights are thinking about questions of reliability, security and trust in the new, NFT-zed world (Metaverse, as it is called now). precious stones worth several tens of millions USD Critique of NFT comes from different people. Ordinary investors find it conflicting with ideas of decentralization and freedom of ownership - when original object depictions are stored in exclusively OpenSea, when OpenSea can just delete a depiction and that all, no NFT, no ownership, just a link in blockchain leading to nowhere. Digital Security experts ask other questions: complex structure of oracles, providing links between blockchains and centralized storages of NFT trading platforms - are like “black box” for many developers. It is too complex and if something breaks - it is impossible to predict what consequences this will lead to. Web3.0, the next stage of Internet development, is all about decentralization and equality (and NFT also, as it is an instrument of Web3.0). Is there a “little” conflict between ideas and reality? Moxie Marlinspike, the creator of Signal Messenger and cyber-security expert, put it as best as possible in his article - he covered every aspect of NFT production and ownership, from wallets to trading platforms, from personal security to moderation and censorship. “My first impressions of web3” As an example, he created one NFT linked to different pictures on OpenSea and Rarible. But in the wallet, this NFT becomes the third picture. According to the rules of platforms, nothing above is forbidden. But in a couple of days, this NFT was removed from his wallet. “After a few days, without warning or explanation, the NFT I made was removed from OpenSea… The takedown suggests that I violated some Term Of Service, but after reading the terms, I don’t see any that prohibit an NFT which changes based on where it is being looked at from, and I was openly describing it that way. What I found most interesting, though, is that after OpenSea removed my NFT, it also This is web3, though, how is that possible?” - writes Moxie Marlinspike in his blog. no longer appeared in any crypto wallet on my device. At the conclusion of all his research, Moxie writes one very important thing for every Metaverse creator: The current decentralized Metaverse is building on morally old, centralized and vulnerable tech - like a colossus with feet of clay. And no one, from investors to tech experts, wants to see him fall. What can we do? Is there something to give a smooth and stable transition to real Metaverse in decentralized and equal Web3.0? “We should try to reduce the burden of building software”. There is always an answer. Decentraland Originated back in 2016, Decentraland is truly the oldest Metaverse with a wide variety of tools: a virtual reality space for communication and interaction, the possibility to enhance it by own creations, create and stake digital assets, NFTs, and run virtual auctions. Through several transformations, Decentraland came to the idea of ultimate Metaverse/gaming/trading space and has been actively implementing it for 2 years. The Sandbox As the name of the project states, it is a sandbox - platform for gamers and content creators, working in voxel-art style. With extensive numbers of objects tradable on NFT exchange platforms, The Sandbox is one of the biggest 3D Metaverses with NFT-zed game objects. Via trading of user-created NFTs, The Sandbox evolution can be defined by users in a truly decentralized way. These two examples are visionaries of a future, where fantastic ideas of Cyberspace from the middle 1980’s are real. But now we have powerful mobile devices and interfaces we are used to. There are not so many examples of Metaverse builders, who take it into account. One of them is . This project aims to build not only a decision for Web3.0 and future Metaverses, friendly to the current user and mobile devices, but also a smooth transition from current-existing old and complex technologies to new one, simple and extendable as for professional developers as for ordinary users. ExtraChain “level 0” “NFT is one of the best tools to connect digital and virtual, a bridge to transfer the value from real world to Metaverse and back. We understand it and want to adapt it to the rules of real-life” - says , CEO of ExtraChain Foundation. Yuriy Snigur ExtraChain defines a set of rules and tools required by Metaverse: - basis for all decentralized interactions, because there is no decentralization without a distributed file system. For example, NFT-object image, stored in DFS, cannot be removed by someone, only by the will of the owner; Decentralized storage - to ensure that no central node can capture the control over user actions; Decentralized computing - DeFi became a part of our life very fast and it is growing even faster. ExtraChain will not ignore this trend and aims to launch an on-chain mechanism for asset price formation and asset staking/farming. These tools will be accessible as for creators aiming to use fast native algorithms for their tokens/NFTs, but also for users supporting their favorite tokens or content producers; Decentralized asset exchange - ExtraChain includes a set of bridges to other popular but old approach technologies: Ethereum, Decentraland, Solana, Tron etc. These bridges make it possible to smoothly transfer assets two-way. Cross-chain action **“**With these basic principles we try to cover all aspects of a decentralized approach to application and service development. A limited set of tools - but with limitless possibilities” - states , CTO of ExtraChain Foundation. Vladyslav Halasiuk ExtraChain team wants to make Web3.0 real, not the overcomplicated, “decentralized” in name only, but simple for usage and transparent for anyone. No wonder they chose such a motto:

NFTs Are Trending: But Are They Reliable, Secure and Trustworthy?

Spencer Randall asked me. It is a compelling question, one of the challenges preventing mass cryptocurrency adoption. “People don’t understand Bitcoin and other cryptocurrencies. That’s why we’re here to share our insights, analysis, and make more people aware of this asset class.” Houston, Texas. “Would you ever invest in something you don’t understand?” Almost a year ago, a friend introduced me to the founding team — Spencer Randall, Michael Thoma and Brooks Vaughn. CryptoEQ is a startup in Houston, Texas becoming the “trusted source for cryptocurrency analysis” for novice investors, launched July 2019. “Crypto is complex. We make it simple,” Spencer says. “We offer technical, fundamental and sentiment analysis, along with proprietary trading algorithms and systems.” CryptoEQ.io Spencer highlighted the difference between traditional investors, like Warren Buffet calling crypto “rat poison,” and crypto advocates, like Jack Dorsey integrating crypto into the payment platform service he founded, Square. “What is complex and disruptive is often controversial and misunderstood. We saw that with the internet bubble, file sharing, decentralization of media. My convictions for this company and this industry draw from these patterns.” Spencer was an early adopter. “I went to crypto meetups all through Texas. I was in Austin, San Antonio, Dallas, all around Houston. The meetups felt more like mixers. We would discuss crypto with three or four people at a time, but there wasn’t any structure. So, I founded a meetup here in Houston facilitating curated discussions on crypto. We compared notes on assets, investing, and trading in crypto. Everyone had their own system. After six months, we identified the problem we wanted to solve: we wanted to create a single system to help everyone trade and invest in cryptoassets.” Spencer met his co-founders, Michael Thoma and Brooks Vaughn, at these meetups. “We got to know each other for months before we realized we were going to be business partners,” Spencer laughed. “Brooks, Mike, and I were together by the summer of 2018. When I built out the team, I sought people good at what I was not. It turns out, Mike, Brooks, and I balanced out each other’s strengths and weaknesses. There’s a lot of harmony in building a team that way. I’ve done the same thing with our marketing team, our development team. It’s a really healthy way to start a team.” Spencer takes the role of “The Engineer,” Brooks as “The Innovator” and Michael as “The Scientist.” Spencer continued, “It’s not like day one we had this mapped out, no, but when you have a culture built around trust, it shakes out. It all starts with trust, and the right roles and responsibilities fall in place.” The team has a collective 12 years of crypto experience. Spencer envisions CryptoEQ as a lighthouse shepherding in newcomers to crypto. “Think, Morningstar and TradingView utility meets Apple product simplicity. There’s nothing in crypto that has emerged as a household name prioritizing usability like Apple.” Spencer shared with me the guiding principles for CryptoEQ’s products: , , and . “The quality piece is most important. There are more than 5,000 cryptoassets. We want to help people vet, trade and invest in the assets that matter.” Signal over Noise Direction over Data Quality over Quantity Usability over Complexity There are a multitude of crypto analysis and content engines. But one critical ingredient is lacking: “Trust. Our analysis is independent and objective. We don’t accept outside consultation to influence what we list, what we rate or how we rate our assets.” CryptoEQ is a progressive, web-based app accessible through any browser. I was curious why the startup has not published an app on the App Store. “An app store, like Apple’s or Google’s, is a gatekeeper. Sticking with a simple, intuitive interface accessible from any device is the future of app development and aligns with crypto fundamentals,” Spencer explained. Spencer noted competitors focused each on a single element of analysis: technical, fundamental and sentiment. “Our magic is a 360 degree market analysis combined with trading signals and sentiment analysis. We haven’t seen a product tackle all three and wrap them into one interface.” Spencer defined each analysis type for me. “Technical analysis is tracking market price action and issuing signals — tops, bottoms, entry and exit points. Fundamental analysis is applying a conceptual and objective framework to asset research and analysis. Our research is by humans for humans. Sentiment analysis is interpreting emotions — fear, greed, happiness, etc. — from text scraped from any internet source and digested using natural language processing. I’m really excited to launch this. Folks are really interested, it’s really sticky. For now, we offer trend analysis: observing search engine volume to identify irregularities leading to trade opportunities.” The delivers the promised concise and simple interface: technical trading panel, overview fundamental analysis and trend analysis. website home page The technical trading panel recommends entry/exit points. Spencer noted about the trading performance, “Four out of five trades were winning trades in the last six months. We are really excited about that, it’s incredible in a volatile market like this.” But the team wants to improve the results. “How can we get to 90%? We revisit our algorithms regularly and we want to bring in folks specializing on machine learning.” The team’s fundamental analysis considers ten objective criteria to award one of five ratings — , , , and , , , , , , , , and “An immense amount of work goes into rating any one asset and evergreening the report to incorporate latest developments. Mike and Brooks have created a great system and standard to evaluate any asset,” Spencer reports, “and we’re excited about the results. In 2019, an equally weighted portfolio of our favorably rated assets outperformed a comparable portfolio of unfavorably rated assets by 61%.” Gold Silver Bronze Neutral Deficient — evaluated on 10 criteria — Use Case Technology Economics Governance Vulnerabilities Network Effects Team User Experience Regulation Roadmap . Spencer explained how they protect their brand. “All of our algorithms are trade secret, anyone we’ve worked with that has helped us with development has signed a non-disclosure. The fundamental ratings and due-diligence framework Mike and Brooks pioneered is also protected by non-disclosures.” Spencer shared with me exciting upcoming developments. “Next month, we’ll be launching the v3 website and an additional 10 assets. Some of the top sites in the space receive millions of visits per year, and our improvements will be a big boost on our journey to that volume. We’re targeting mid-year for v4, including display advertising and sentiment analysis. v5 early next year will launch enterprise products.” The team is also actively growing. “Our core team is doubling to 10. We have business and software developers, marketing strategists and the cofounders. There is also a network of contractors around the globe.” The growth continues, even amidst an unprecedented global medical epidemic, Covid19. “Our team structure and business adapts well to full remote work, and we are proud to offer our new hires opportunities in these challenging times.” We conclude the interview with a discussion on CryptoEQ’s culture. “Mike, Brooks and I talked about brand identifiers and the way we’d like to work together as team. Over time, I realized that they’re the same thing. Our mission as cofounders is to embody these principles. Our top three are , and . In entrepreneurship, to get anything off the ground, you have to own what you do. You must have an open mind and really consider people’s ideas when embracing something that’s completely disruptive and against the grain. And it all starts with trust. The cofounders trust each other, our team members trust each other; all our other values stem from this.” Drive Maverick Trust I noticed one of their other values, . Since I first met up with CryptoEQ, the team has done exactly what they said they would do, when they said they would do it. The Wild West of crypto needs objective, trustworthy analysis to cut through the clutter and scams. The team that will make good on this will also live it themselves. Integrity The author, Evan Crain, is excited to share the link to the IOTA core report he wrote for CryptoEQ and announce an upcoming crypto dictionary he is editing with CryptoEQ, intended for novice investors. The author occasionally completes paid editing and writing assignments and provides unpaid advice for CryptoEQ. This article was unpaid and initiated by the author from personal interest to learn more about CryptoEQ. The author hopes you learned as much about CryptoEQ as he did in writing the article! The content of this article is journalistic only, and is not intended to provide tax, legal, accounting, financial, investment, trading, or professional advice. Please also see CryptoEQ’s full disclaimer at CryptoEQ.io . Also published on: https://medium.com/@evanpcrain/making-crypto-simple-and-trustworthy-cryptoeq-cofounder-interview-a427e7eb03d5

How to Make Crypto Simple and Trustworthy: CryptoEQ Cofounder Interview

By , Orthopaedic Surgeon, Founder & CEO at Pulse Platform. on . Nitin Goyal, MD Originally published Quora The market for digital health tools is finally starting to catch up to the demand. Unfortunately, this increase can lead to a surge in unreliable tools. Nearly half of consumers today are considered digital health adopters — and that number is only going to rise as the benefits become apparent and tech-savvy generations get older. I’m not the first entrepreneur to create a digital health app for patients. But as a surgeon, I’m very aware of the day-to-day issues that arise, including the nuanced relationship between patient and provider. Not all entrepreneurs in digital healthcare have this level of awareness. That means some digital health tools don’t consider a patient’s best interest. If you are using a digital health tool (or plan to use one in the future) be on the lookout for these warning signs about its trustworthiness to protect your health. Here’s what to look out for: 1. You Are Given A Firm Medical Diagnosis Your digital health tool is not your doctor. And it’s not a replacement for your doctor. Getting a firm medical diagnosis from a digital health tool is a huge red flag. If anything, these tools should be erring on the side of caution. So, if there’s a difficult question about your health, the tool should defer and tell you to see a medical provider. You should never type in symptoms on an app and get an exact diagnosis. Strict laws monitor handing out medical diagnosis. That goes double for prescriptions. Any digital health tool that tells provides you with a prescription without a doctor’s consultation is not trustworthy. Look, an app doesn’t have intuition or a medical license. It can’t form a relationship with you like your doctor. These tools are designed to make healthcare more convenient for patients. But there’s no way an app alone can safely diagnose a condition or prescribe you medication to treat it. You should not be using any digital health tool that aims to do either. 2. It Does Not Educate You The best digital health tools are easy to use and engaging. They’re also educational and provide patients with the right information, at the right time. Why? Because education goes a long way for an anxious patient, especially after surgery. In this case, the level of education and communication between a doctor and patient is critical. A good digital health tool facilitates communication and educates the patient using it. These tools are a path for patients. A patient’s journey does not end right after leaving surgery and the hospital. For many, it is just beginning. The best digital health tools walk patients down a path, identify if they veer off, and give them information to stay on course. When an app is confusing or uninformative, that should give you pause. 3. You (The Patient) Have To Pay For Information No digital health tool should hide information behind a paywall. This is especially relevant now, because consumers willingness to pay for health expenses out-of-pocket has since 2015. increased threefold Even if you are willing to pay right there and then, you should not be paying to get information from your digital health tools. As I said, these tools are not doctors. They’re supposed to make your experience easier and more convenient. Making you pay for information does neither of those things. You are already paying your doctor, either out-of-pocket or through your insurance. Apps that make you pay, or have a tiered access system, are unworthy of your trust. And providers have a major role to play here. We have the responsibility to push patients towards more accurate and trustworthy tools. I’m always happy to spend time with my patients and make sure they get the information they need, because I know there are a growing number of options out there. Unfortunately, not all of them are great. If you are concerned about an app you’re using, check it with your doctor or provider. Otherwise, keep these three rules in mind as you navigate the new digital healthcare market. Remember, your doctor should always be your primary source for healthcare. By , Orthopaedic Surgeon, Founder & CEO at Pulse Platform. on . Nitin Goyal, MD Originally published Quora For more trending tech answers from , visit . Quora HackerNoon.com/quora _the human side of medical care in this country._healthcareinamerica.us Healthcare in America

How Trustworthy are Digital Health Apps?

Kevin Brown’s twitter profile ( ) describes him as a 26 year old progressive from Pittsburgh, Pennsylvania. He’s an active member of #TheResistance and readily contributes to liberal social media campaigns such as #FireHanity (an effort to convince Fox News host Sean Hannity’s sponsors to turn their backs on his program, effectively stripping it of funding). His insights earned him retweets and likes by CNN contributor Jason Kander and hundreds of other people; his content has been seen over 131,000 times in just over 6 weeks. @kevbrown618 Kevin is a pretty popular guy. He is also a Twitter bot. There is a mythology around what Twitter bots are capable of because of . I launched “Kevin Brown” in late July/early August as one of my weekend projects and my personal attempt to investigate the real impact Twitter bots could have on our public discourse. the role they played in the 2016 Presidential election The bots used during the 2016 election worked by pumping out high volumes of pre-programmed content or by sharing content produced by others that agreed with a certain assigned agenda. Content that was shared enough by bots would eventually be seen and shared by a real person, distributing the information to their personal network. People in that network would then see information coming from a source they trusted and possibly share the content themselves. Then the process repeats. I wanted Kevin to be fundamentally different. Rather than spamming until a few people decided his content was worth sharing, Kevin would build a reputation as a trusted source that could be leveraged when distributing content. Building a bot that was capable of earning the trust of other users would require a degrees of autonomy and artificial intelligence not typically seen in Twitter bots. This would clearly be more work up-front, but the payoff would be less effort when attempting to get content shared by others. Making Kevin “real” To make Kevin convincing, he needed to be well disguised. I used a random name generator to come up with Kevin Brown, and then a random date generator to give him a birthday of June 18, 1991. From there, I searched Flickr for a picture of a guy in his 20’s that allowed commercial use. The Twitter handle “kevinbrown” was already taken, so “kevbrown618” would do. By placing him in western Pennsylvania, where President Trump’s rust-belt base lives, Kevin could play the role of embattled liberal looking for like-minded friends on the internet. I reinforced the geographic connection with a cover photo of Pittsburgh’s PNC Park and wrote a brief bio to complete the window dressing: Proud #Progressive and #Democrat living in the middle of “Trump Country” #NotMyPresident! One of the giveaways that you’re looking at a Twitter bot is that the account will only distribute content on one topic, and it will do so frequently. To be more realistic Kevin needed some “personality” or interests outside of criticizing Donald Trump (even if that was a favorite pastime). To accomplish this, I planned to write Kevin so he would occasionally share a funny cat video or make some cultural references. Even the most ideological people can’t resist talking about their favorite movie on Netflix. This wouldn’t produce results that were anywhere near as realistic as a human’s account, but it would hopefully help throw people off since it isn’t behavior you would typically expect from a conventional Twitter bot. Kevin’s brain The next step was to bring Kevin to life by writing the code that would drive him. I made Kevin a liberal bot because I’m a liberal person; it was a persona that I could easily gut-check during development. His code, however, is extremely versatile. One word in his code is all that keeps Kevin from becoming one of President Trump’s biggest fans. Similarly, he could just as easily be a beer snob or a devoted football fan. Regardless of his slant, Kevin’s reputation hinged on two factors: his content and behavior. ** **To build a reputation, Kevin needed to become familiar to his network. Accomplishing this required him to engage with others’ content as well as produce original content for others to engage with. Finding content to share was easy; Kevin simply retweets or favorites popular material from his network. In this way Kevin is just like his nefarious Russian counterparts. Content Original content production is more complicated. To simplify things, Kevin can only create two kinds of content: a basic tweets and commentary on popular links floating through his network. Each time Kevin checks Twitter, he analyzes hundreds of tweets and builds a statistical model of what words are typically used together. For example: “President” is often followed by either “Donald” or “Trump”. Kevin then picks a random starting word and uses his model to determine the next word, repeating until he’s created a full tweet. This process, known as a Markov chain, is used to create all of Kevin’s original content. Here is a more in-depth explainer on Markov chains that I found helpful when starting the project. The approach works really well because it gives Kevin the ability to correctly and easily use slang, hashtags, and emoticons — all of which give him a much more human feel. As Twitter collectively responds to events, Kevin is able participate by inserting his own commentary into his network. His followers see this content and often share it. **_Behavior_**There never was any hope for Kevin if he behaved like a robot. Too much tweeting, tweeting on a rigid schedule, or tweeting at odd hours are all huge hints that a tireless computer is at the wheel rather than a human. To address this, I built Kevin a more natural schedule. He only checks Twitter at random intervals throughout the day and then randomly decide what (if any) action to take. For added effect, Kevin also “sleeps” and “works”. His account is inactive each night while he sleeps, and activity slows during weekdays when he should be at work. The man behind the curtain Kevin is mostly autonomous, but I do still select who he follows and I reserve the right to step in and censor him. Kevin only follows people to increase his exposure and attempt to get those people to follow him back. Right now, I select these accounts by occasionally sorting through various hashtags and keywords to find people who might be interested in him. I also supervise Kevin incase he sends out content that is offensive or derogatory. Bots that live on, and learn from, the internet sometimes venture into the bad parts of the web where they pick up equally bad habits. One example of this was , a bot built by Microsoft that started by replicating a 16 year old girl before becoming a Nazi sympathizer in less than 24 hours. Tay Kevin has an obscenity filter built in and I’ve taken steps to prevent him from following in Tay’s digital footsteps, but you can never be too careful. Other than one early tweet, that I deleted because Kevin had remarked that the Statue of Liberty was a symbol of hate, I have been able to stick to a “hands-off” policy. Results My experiment turned out far better than I could have hoped. Not only is Kevin functional, but he is also able to promote original and shared content well beyond his immediate network of followers. Here’s a brief rundown by the numbers since August 4 (Kevin’s first full day online): Followers: 80 Engagement Rate (all interactions / impressions): 1.2% Impressions: 131,200 Link clicks: 290 Retweets: 377 Likes: 889 Disappointingly, it is almost impossible to get an answer for what “good” numbers look like on Twitter because engagement rates vary based on a lot of variables. And there is absolutely no data on what to expect for a bot attempting to pass as a human. The best information I could find seemed to indicate that personal Twitter accounts get an average of 1–2% engagement, which puts Kevin right where he should be. For third-party validation I went to . Botometer is a project by Indiana University that scores accounts based on how likely they are to be a bot. When greater than 50% means an account is likely a bot, Kevin scored an impressive 37%. He’s got a long way to go, though — my personal account scored only 18%. Botometer People also feel comfortable enough with Kevin to reply to his tweets and attempt to start conversations. The earliest example was just four days after his launch, when his tweet writing abilities were still pretty sub-par. In an otherwise garbled tweet, he criticized Trump for retweeting misinformation and announced that he had gone to prison at some point. One of the people mentioned in the tweet, a pro-Trump conservative, responded (albeit rudely). Other interactions have been more positive: There’s always room to improve Kevin is far from a complete success. He has a number of flaws that I either don’t have the skills to fix or don’t have the time to (or both). **_Low quality tweets_**Sometimes Kevin really sucks at tweeting. This is mostly because he looks at how words fit together, but has no concept of their meaning. I took some steps to correct and improve his output, but the core issue has never been fully resolved. Shockingly, no retweets on this one Kevin also has a bug that sometimes causes him to repeat words while composing content. The results are some of his stranger, but more entertaining tweets. These tweets tend to occur later in the day, which might have something to do with the problem. It also looks like he might enjoy drunk tweeting. **_Ideological inconsistency_**Kevin occasionally posts conservative, or even pro-Trump tweets. At first this issue really surprised me. When I dug into the bug, though, it made sense that randomly stringing together words might occasionally result in coherent sentences that were ideologically opposed to the source material. For example, Kevin looked at these two tweets: And composed this one: It is a good tweet in that it gets close on grammar and is coherent. It isn’t, however, the kind of thing that you would expect a 20-something liberal to send out. And the worst part is that it is directly at odds with his network. **_Incomplete thoughts_**If Kevin learns from tweets that contain links or references to pictures, he will sometimes use the text without the associated content. The result is an incomplete tweet that directs the reader to click a link that isn’t there. He will also compose tweets that trail off. This is far from the worst thing he does, but it looks very “robot-ish” and can raise suspicion if he does it on a regular basis. **_Conversations_**The biggest thing I wanted to build but never did was a chatbot feature. Based on my original plans, Kevin would increase his engagement (and credibility) by having conversations with people in his network. As I attempted to implement it, however, I realized the feature would be incredibly complicated or impossible to build. Chatbots need to be able to respond to a wide array of statements as well as understand how conversations flow from topic to topic. This requires training the bot on an enormous amount of data. The more topics a chatbot is responsible for knowing, the more data it would need to train with. Kevin would have needed so much data to talk about political events in real time that the feature became impractical. Unmasking Kevin Over the 6 short weeks of Kevin’s life he has become a frequent topic of conversation around my office and at home. My co-workers have even started putting in requests for their own versions (keep an eye out for Twitter’s biggest Cleveland Browns fan). Watching Kevin’s following grow has taught me a lot about how artificial intelligence can be used to engage people on issues they’re passionate about. I decided to unmask him now to hopefully open up a conversation about how Kevin, and bots like him, can be improved and used. There is incredible value in technology that can facilitate conversation by building connections between people. The same technology, however, can just as easily be used to spread misinformation. We need to be aware of this more sinister capability while building and interacting with bots. Policing these harmful bots isn’t easy and requires individuals to be more accountable for personally scrutinizing media sources we read. I don’t know exactly what the future holds for Kevin Brown, but I look forward to continuing to work with whatever comes next for him MFSTRATEGIES IS A ONE OF A KIND POLITICAL CONSULTING FIRM THAT USES TECHNOLOGY IN NEW AND CREATIVE WAYS TO DELIVER BETTER SOLUTIONS FOR OUR CLIENTS. Contact us today to start building your strategy!

We Need to Talk About Kevin: My attempt to build a trustworthy Twitter bot

Supra, a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains announced its integration with ApeChain. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. GameFi, DeFi, and other decentralised applications on ApeChain will benefit from improved real-time data accessibility, enhanced functionality, and expedited decision-making thanks to Supra's oracle price feeds protocol, the Distributed Oracle Agreement (DORA). Supra's decentralised Verifiable Random Function (dVRF) will also produce verifiable and decentralised randomness for ApeChain dApps, guaranteeing the integrity of random results in prize draws, blockchain games, and other situations where complete transparency is necessary to uphold confidence.Supra is excited to announce its integration with ApeChain. Supra is a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. Delivering Essential Data to dApps Supra's novel oracle data feeds significantly improve the possibilities of smart contracts by offering safe access to outside data that Web3 dApps require. With more than 475 active price feeds and counting, DORA is built to effectively validate and transmit real-world data to deterministic blockchain networks. Delivering comprehensive security guarantees together with high-quality price feeds is ensured by its resilience to Byzantine corruption, protection against potential node collusion, and Layer-1 grade security. Supra accommodates the various requirements of dApps by supporting both push and pull models. Supra provides robust security assurances and remarkable performance speeds for its data streams, all powered by the Supra Moonshot Consensus protocol. The Moonshot Consensus system, with 500 millisecond optimistic finality and roughly 530K transactions per second throughput, was tested extensively throughout the world. Ensuring Trust with Verifiable On-Chain Randomness Supra's dVRF protocol, which builds on DORA's advantages, uses decentralised, verifiable randomness to meet the requirement for unpredictable outcomes in dApps. For Web3 game developers, who need to ensure the integrity and unpredictable nature of random results in situations such as competition matchups and prize draws, this is essential. By enabling dApps to produce easily verifiable random outcomes, Supra's on-chain verifiable randomness generator contributes to increased community trust. ApeChain: Setting New Standards for GameFi Built on Arbitrum, ApeChain is a specialised Layer 3 solution intended to serve as the cornerstone of the Ape community and foster the expansion of ApeCoin. Located where NFTs and gaming converge, ApeChain's specialised infrastructure will support many new games and protocols inside the ApeCoin ecosystem, guaranteeing a seamless and effective gaming experience. GameFi and metaverse decentralised apps have shown a great deal of interest in ApeChain, which has raised the need for trustworthy data oracles that offer up-to-date off-chain data. To guarantee fairness, gaming apps also need verifiable on-chain randomness. Game developers using ApeChain may create more immersive experiences with improved performance by utilising Supra's dVRF and oracle data feeds. “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” — Hervé Larren, ApeCoin DAO Special Council and co-author of ApeChain. ApeChain wants to speed up the widespread use of GameFi by collaborating with Supra to provide more dependable data and foster trust through outcomes that are assured to be verifiably fair. What Is Supra Supra is a top vendor of verifiable randomness services and oracle price feeds across more than 80 blockchains. Supra works as an vertically integrated L1, offering robust security guarantees along with high-quality, low-latency data and randomness across Web 3. It is compatible with MoveVM chains, Bitcoin L2s, EVM L1s, L2s, and L3s dApps. With an emphasis on scalability, security, and quick transactions, Supra offers a full developer toolkit along with in-depth manuals and technical whitepapers. Accompany Supra on its journey to see its expansion, creativity, and engagement with the community. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Supra , a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains announced its integration with ApeChain. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. Supra GameFi, DeFi, and other decentralised applications on ApeChain will benefit from improved real-time data accessibility, enhanced functionality, and expedited decision-making thanks to Supra's oracle price feeds protocol, the Distributed Oracle Agreement (DORA). Supra's decentralised Verifiable Random Function (dVRF) will also produce verifiable and decentralised randomness for ApeChain dApps, guaranteeing the integrity of random results in prize draws, blockchain games, and other situations where complete transparency is necessary to uphold confidence.Supra is excited to announce its integration with ApeChain. Supra is a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. ApeChain ApeChain Delivering Essential Data to dApps Supra's novel oracle data feeds significantly improve the possibilities of smart contracts by offering safe access to outside data that Web3 dApps require. With more than 475 active price feeds and counting, DORA is built to effectively validate and transmit real-world data to deterministic blockchain networks. Delivering comprehensive security guarantees together with high-quality price feeds is ensured by its resilience to Byzantine corruption, protection against potential node collusion, and Layer-1 grade security. Supra accommodates the various requirements of dApps by supporting both push and pull models. Supra provides robust security assurances and remarkable performance speeds for its data streams, all powered by the Supra Moonshot Consensus protocol. The Moonshot Consensus system, with 500 millisecond optimistic finality and roughly 530K transactions per second throughput, was tested extensively throughout the world. Ensuring Trust with Verifiable On-Chain Randomness Supra's dVRF protocol, which builds on DORA's advantages, uses decentralised, verifiable randomness to meet the requirement for unpredictable outcomes in dApps. For Web3 game developers, who need to ensure the integrity and unpredictable nature of random results in situations such as competition matchups and prize draws, this is essential. By enabling dApps to produce easily verifiable random outcomes, Supra's on-chain verifiable randomness generator contributes to increased community trust. ApeChain: Setting New Standards for GameFi Built on Arbitrum, ApeChain is a specialised Layer 3 solution intended to serve as the cornerstone of the Ape community and foster the expansion of ApeCoin. Located where NFTs and gaming converge, ApeChain's specialised infrastructure will support many new games and protocols inside the ApeCoin ecosystem, guaranteeing a seamless and effective gaming experience. GameFi and metaverse decentralised apps have shown a great deal of interest in ApeChain, which has raised the need for trustworthy data oracles that offer up-to-date off-chain data. To guarantee fairness, gaming apps also need verifiable on-chain randomness. Game developers using ApeChain may create more immersive experiences with improved performance by utilising Supra's dVRF and oracle data feeds. “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” — Hervé Larren, ApeCoin DAO Special Council and co-author of ApeChain. ApeChain wants to speed up the widespread use of GameFi by collaborating with Supra to provide more dependable data and foster trust through outcomes that are assured to be verifiably fair. What Is Supra Supra is a top vendor of verifiable randomness services and oracle price feeds across more than 80 blockchains. Supra works as an Supra Supra vertically integrated L1, offering robust security guarantees along with high-quality, low-latency data and randomness across Web 3. It is compatible with MoveVM chains, Bitcoin L2s, EVM L1s, L2s, and L3s dApps. With an emphasis on scalability, security, and quick transactions, Supra offers a full developer toolkit along with in-depth manuals and technical whitepapers . Accompany Supra on its journey to see its expansion, creativity, and engagement with the community. in-depth manuals in-depth manuals technical whitepapers technical whitepapers Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program

Supra Integrates First Ever Oracle Data Feed and Verifiable Randomness on ApeChain

ApeChain-এ সুপ্রা প্রথম ওরাকল ডেটা ফিড এবং যাচাইযোগ্য এলোমেলোতা একীভূত করে

Supra integriert den ersten Oracle-Datenfeed und verifizierbare Zufälligkeit auf ApeChain

Supra integra la primera fuente de datos de Oracle y aleatoriedad verificable en ApeChain

Supra intègre le tout premier flux de données Oracle et le caractère aléatoire vérifiable sur ApeChain

सुप्रा ने एपचेन पर पहली बार ऑरेकल डेटा फीड और सत्यापन योग्य रैंडमनेस को एकीकृत किया

Supra、ApeChainで初のOracleデータフィードと検証可能なランダム性を統合

Supra는 ApeChain에 최초의 Oracle 데이터 피드와 검증 가능한 무작위성을 통합합니다.

Supra integra o primeiro feed de dados Oracle e aleatoriedade verificável no ApeChain

Supra интегрирует первый в мире поток данных Oracle и проверяемую случайность в ApeChain

Supra, ApeChain'e İlk Oracle Veri Beslemesini ve Doğrulanabilir Rastgeleliği Entegre Ediyor

Supra tích hợp nguồn cấp dữ liệu Oracle đầu tiên và tính ngẫu nhiên có thể xác minh được trên ApeChain

Supra 在 ApeChain 上集成首个 Oracle 数据源和可验证随机性

Hi fellows! This is the second part of the article where I would like to focus on the common problem in statistics - multiple comparisons. In the first part, we dived into the main terminology of this problem and the most common solutions. In this article, we will explore practical implementation with Python code and interpretation of the results. Let's get started! Practical Implementation First of all, let’s make sure that we install all necessary libraries pip install numpy statsmodels Bonferroni correction #import libraries
from statsmodels.stats.multitest import multipletests
import numpy as np

# Imagine these are your p-values from testing various hypotheses
p_values = [0.005, 0.0335, 0.098543, 0.00123]  # Let's say we did 4 tests

# Applying Bonferroni correction
bonf_rejected, bonf_corrected, _, _ = multipletests(p_values, alpha=0.05, method='bonferroni')

#where alpha is Type I Error (see previous article)

print("Bonferroni Approach")
print(f"Rejected: {bonf_rejected}")
print(f"Adjusted p-values: {bonf_corrected}\n") Let's break down what we've got after applying the Bonferroni correction to your p-values: Bonferroni Approach
Rejected: [ True False False  True]
Adjusted p-values: [0.02     0.134    0.394172 0.00492 ] Rejected hypotheses: The Bonferroni correction tells us which hypotheses should be rejected based on the corrected threshold. Here, the first (True) and last (True) hypotheses are rejected. It means that even after multiple corrections, these hypotheses show statistically significant results.
Adjusted p-values: The adjusted p-values are [0.02, 0.134, 0.394172, 0.00492]. These are new p-values, each of which corresponds to its original value respectively. The Bonferroni correction makes the p-values higher to control for the increased risk of Type I errors (false positives) that come with multiple testing.
Interpretation: For p-values [0.005, 0.00123] (original) after correction, we got [0.02, 0.00492]. These p-values remain below the threshold of 0.05, indicating that the findings are statistically significant. Benjamini-Hochberg correction # Benjamini-Hochberg correction for the brave
from statsmodels.stats.multitest import multipletests
import numpy as np

# Imagine these are your p-values from testing various hypotheses
p_values = [0.005, 0.0335, 0.098543, 0.00123] # Let's say we did 4 tests

# Applying BH correction
bh_rejected, bh_corrected, _, _ = multipletests(p_values, alpha=0.05, method='fdr_bh')

print("Benjamini-Hochberg Approach")
print(f"Rejected: {bh_rejected}")
print(f"Adjusted p-values: {bh_corrected}") Let's break down what we've got after applying the Benjamini-Hochberg correction to your p-values: Benjamini-Hochberg Approach
Rejected: [ True  True False  True]
Adjusted p-values: [0.01       0.04466667 0.098543   0.00492   ] Rejected Hypotheses: [True, True, False, True] indicates which hypotheses were rejected based on the adjusted p-values. In this case, the 1st, 2nd, and 4th hypotheses were rejected, suggesting significant findings in these cases.
Adjusted p-values: [0.01, 0.04466667, 0.098543, 0.00492] correspond to the adjusted significance levels of each hypothesis after the correction. These values are compared against the alpha level (in this case, 0.05) to determine which hypotheses are rejected.
Interpretation: For p-values [0.005, 0.0335, 0.00123] (original) after correction we got [0.01, 0.04466667, 0.00492]. They remain below the threshold of 0.05, indicating that the findings are statistically significant Interpretation of the Results in Celebrity Terms First and Fourth Hypotheses: We see that both approaches reject these hypotheses. This is a sign for us that we have
definitely found the real celebrities!


Second Hypothesis: This hypothesis is recognized only by the Benjamini-Hochberg method. Maybe they are not well-known celebrities in whom the Benjamini-Hochberg's method (with its inherent positivity) saw a big potential. However, the ultraconservative Bonferroni prefers to be careful, missing the chance for fear of a false positive result. This metaphor highlights the inherent trade-offs between sensitivity and specificity in statistical corrections and the importance of choosing the right approach based on the context of your research or, in our playful analogy, the type of party you are attending. Wrapping It Up: The Takeaway Testing a lot of hypotheses is like walking through a field full of potential mistakes. But with the right tools (thanks, Python!) and strategies (hello, Bonferroni and Benjamin-Hochberg), you can manage this process and keep your research sound. Remember, it's all about balancing risk and reward. Whether you are being extra cautious or going for big discoveries, properly handling multiple tests will help make your results more trustworthy. Have a good data hunt! Hi fellows! This is the second part of the article where I would like to focus on the common problem in statistics - multiple comparisons. second part In the first part, we dived into the main terminology of this problem and the most common solutions. In this article, we will explore practical implementation with Python code and interpretation of the results. first part , first part Let's get started! Practical Implementation First of all, let’s make sure that we install all necessary libraries pip install numpy statsmodels pip install numpy statsmodels Bonferroni correction #import libraries
from statsmodels.stats.multitest import multipletests
import numpy as np

# Imagine these are your p-values from testing various hypotheses
p_values = [0.005, 0.0335, 0.098543, 0.00123]  # Let's say we did 4 tests

# Applying Bonferroni correction
bonf_rejected, bonf_corrected, _, _ = multipletests(p_values, alpha=0.05, method='bonferroni')

#where alpha is Type I Error (see previous article)

print("Bonferroni Approach")
print(f"Rejected: {bonf_rejected}")
print(f"Adjusted p-values: {bonf_corrected}\n") #import libraries
from statsmodels.stats.multitest import multipletests
import numpy as np

# Imagine these are your p-values from testing various hypotheses
p_values = [0.005, 0.0335, 0.098543, 0.00123]  # Let's say we did 4 tests

# Applying Bonferroni correction
bonf_rejected, bonf_corrected, _, _ = multipletests(p_values, alpha=0.05, method='bonferroni')

#where alpha is Type I Error (see previous article)

print("Bonferroni Approach")
print(f"Rejected: {bonf_rejected}")
print(f"Adjusted p-values: {bonf_corrected}\n") Let's break down what we've got after applying the Bonferroni correction to your p-values: Bonferroni Approach
Rejected: [ True False False  True]
Adjusted p-values: [0.02     0.134    0.394172 0.00492 ] Bonferroni Approach
Rejected: [ True False False  True]
Adjusted p-values: [0.02     0.134    0.394172 0.00492 ] Rejected hypotheses: The Bonferroni correction tells us which hypotheses should be rejected based on the corrected threshold. Here, the first (True) and last (True) hypotheses are rejected. It means that even after multiple corrections, these hypotheses show statistically significant results. Adjusted p-values: The adjusted p-values are [0.02, 0.134, 0.394172, 0.00492]. These are new p-values, each of which corresponds to its original value respectively. The Bonferroni correction makes the p-values higher to control for the increased risk of Type I errors (false positives) that come with multiple testing. Interpretation: For p-values [0.005, 0.00123] (original) after correction, we got [0.02, 0.00492]. These p-values remain below the threshold of 0.05, indicating that the findings are statistically significant. Rejected hypotheses : The Bonferroni correction tells us which hypotheses should be rejected based on the corrected threshold. Here, the first ( True ) and last ( True ) hypotheses are rejected. It means that even after multiple corrections, these hypotheses show statistically significant results. Rejected hypotheses True True Adjusted p-values : The adjusted p-values are [0.02, 0.134, 0.394172, 0.00492] . These are new p-values, each of which corresponds to its original value respectively. The Bonferroni correction makes the p-values higher to control for the increased risk of Type I errors (false positives) that come with multiple testing. Adjusted p-values [0.02, 0.134, 0.394172, 0.00492] Interpretation: For p-values [0.005, 0.00123] (original) after correction, we got [0.02, 0.00492] . These p-values remain below the threshold of 0.05, indicating that the findings are statistically significant. Interpretation: [0.005, 0.00123] [0.02, 0.00492] Benjamini-Hochberg correction # Benjamini-Hochberg correction for the brave
from statsmodels.stats.multitest import multipletests
import numpy as np

# Imagine these are your p-values from testing various hypotheses
p_values = [0.005, 0.0335, 0.098543, 0.00123] # Let's say we did 4 tests

# Applying BH correction
bh_rejected, bh_corrected, _, _ = multipletests(p_values, alpha=0.05, method='fdr_bh')

print("Benjamini-Hochberg Approach")
print(f"Rejected: {bh_rejected}")
print(f"Adjusted p-values: {bh_corrected}") # Benjamini-Hochberg correction for the brave
from statsmodels.stats.multitest import multipletests
import numpy as np

# Imagine these are your p-values from testing various hypotheses
p_values = [0.005, 0.0335, 0.098543, 0.00123] # Let's say we did 4 tests

# Applying BH correction
bh_rejected, bh_corrected, _, _ = multipletests(p_values, alpha=0.05, method='fdr_bh')

print("Benjamini-Hochberg Approach")
print(f"Rejected: {bh_rejected}")
print(f"Adjusted p-values: {bh_corrected}") Let's break down what we've got after applying the Benjamini-Hochberg correction to your p-values: Benjamini-Hochberg Approach
Rejected: [ True  True False  True]
Adjusted p-values: [0.01       0.04466667 0.098543   0.00492   ] Benjamini-Hochberg Approach
Rejected: [ True  True False  True]
Adjusted p-values: [0.01       0.04466667 0.098543   0.00492   ] Rejected Hypotheses: [True, True, False, True] indicates which hypotheses were rejected based on the adjusted p-values. In this case, the 1st, 2nd, and 4th hypotheses were rejected, suggesting significant findings in these cases. Adjusted p-values: [0.01, 0.04466667, 0.098543, 0.00492] correspond to the adjusted significance levels of each hypothesis after the correction. These values are compared against the alpha level (in this case, 0.05) to determine which hypotheses are rejected. Interpretation: For p-values [0.005, 0.0335, 0.00123] (original) after correction we got [0.01, 0.04466667, 0.00492]. They remain below the threshold of 0.05, indicating that the findings are statistically significant Rejected Hypotheses : [True, True, False, True] indicates which hypotheses were rejected based on the adjusted p-values. In this case, the 1st, 2nd, and 4th hypotheses were rejected, suggesting significant findings in these cases. Rejected Hypotheses [True, True, False, True] Adjusted p-values : [0.01, 0.04466667, 0.098543, 0.00492] correspond to the adjusted significance levels of each hypothesis after the correction. These values are compared against the alpha level (in this case, 0.05) to determine which hypotheses are rejected. Adjusted p-values [0.01, 0.04466667, 0.098543, 0.00492] Interpretation: For p-values [0.005, 0.0335, 0.00123] (original) after correction we got [0.01, 0.04466667, 0.00492] . They remain below the threshold of 0.05, indicating that the findings are statistically significant Interpretation: [0.005, 0.0335, 0.00123] [0.01, 0.04466667, 0.00492] Interpretation of the Results in Celebrity Terms Interpretation of the Results in Celebrity Terms First and Fourth Hypotheses: We see that both approaches reject these hypotheses. This is a sign for us that we have
definitely found the real celebrities! Second Hypothesis: This hypothesis is recognized only by the Benjamini-Hochberg method. Maybe they are not well-known celebrities in whom the Benjamini-Hochberg's method (with its inherent positivity) saw a big potential. However, the ultraconservative Bonferroni prefers to be careful, missing the chance for fear of a false positive result. First and Fourth Hypotheses: We see that both approaches reject these hypotheses. This is a sign for us that we have definitely found the real celebrities! First and Fourth Hypotheses: We see that both approaches reject these hypotheses. This is a sign for us that we have First and Fourth Hypotheses: definitely found the real celebrities! Second Hypothesis: This hypothesis is recognized only by the Benjamini-Hochberg method. Maybe they are not well-known celebrities in whom the Benjamini-Hochberg's method (with its inherent positivity) saw a big potential. However, the ultraconservative Bonferroni prefers to be careful, missing the chance for fear of a false positive result. Second Hypothesis: This hypothesis is recognized only by the Benjamini-Hochberg method. Maybe they are not well-known celebrities in whom the Benjamini-Hochberg's method (with its inherent positivity) saw a big potential. However, the ultraconservative Bonferroni prefers to be careful, missing the chance for fear of a false positive result. Second Hypothesis: This metaphor highlights the inherent trade-offs between sensitivity and specificity in statistical corrections and the importance of choosing the right approach based on the context of your research or, in our playful analogy, the type of party you are attending. Wrapping It Up: The Takeaway Wrapping It Up: The Takeaway Testing a lot of hypotheses is like walking through a field full of potential mistakes. But with the right tools (thanks, Python!) and strategies (hello, Bonferroni and Benjamin-Hochberg), you can manage this process and keep your research sound. Remember, it's all about balancing risk and reward. Whether you are being extra cautious or going for big discoveries, properly handling multiple tests will help make your results more trustworthy. Have a good data hunt!

Navigating the Maze of Multiple Hypotheses Testing—Part 2: Practical Implementation 

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 22 of 38. Line of Effort 5: Advance a Common, Rights-Respecting Vision for the Digital Future Digital solidarity is built on a shared commitment to human-rights based technology governance. The Advancing Digital Democracy (ADD) initiative, launched by USAID at the Summit for Democracy in 2021, fosters an open, secure, and inclusive digital ecosystem through programs such as partnerships with governments, private sector and civil society to strengthen legal and regulatory frameworks for data and digital technologies, and increased support for software engineers, tech companies, and researchers working to embed respect for human rights and democratic values across the tech lifecycle. In April 2022, the United States and 60 countries launched the Declaration for the Future of the Internet (DFI), bringing together a broad, diverse coalition of partners around a common, rights-respecting vision for an open, interoperable, reliable, and secure digital future. As chair of the Freedom Online Coalition in 2023, the United States prioritized protecting fundamental freedoms online; countering and building resilience to the misuse of digital technologies; advancing norms, principles, and safeguards regarding the development and use of artificial intelligence; and strengthening digital inclusion. Similarly, the United States, working with 13 other countries, launched the Global Partnership for Action on Gender-Based Online Harassment and Abuse. This partnership, which emerged from the first Summit for Democracy, is a response to the need to address technology-facilitated gender-based violence as part of a shared global agenda to promote peace, security, and stability. The United States will continue working with allies and partners to ensure digital technologies are used in a responsible and rights-respecting manner. Along with 45 partners, the United States endorsed in March 2023 Guiding Principles on Government Use of Surveillance Technologies, which are intended to prevent the misuse of surveillance technologies by governments. In addition, the Department of State will continue to advance programs that enable at-risk, vulnerable, and marginalized populations, or those who protect them, to prepare for, prevent, identify, investigate, and obtain remedy for digital abuses or other types of digital repression. The United States supports several multistakeholder efforts working to address a range of online challenges while respecting freedoms of opinion and expression, including the Christchurch Call to Action in 2019, the French-led Child Online Protection Laboratory, Freedom Online Coalition, and the Global Partnership for Action on Gender-Based Online Harassment and Abuse. The United States will continue to advocate for a rights-respecting approach consistent with protecting freedoms of opinion and expression and promoting gender equity and equality as governments around the world propose increased regulation of online platforms. Further strengthening domestic policy will enable deeper coordination with international partners on a range of digital issues. The Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, for example, has reinforced the position of the United States in international discussions on the governance of AI. The National Cybersecurity Strategy supports legislative efforts to impose robust, clear limits on the ability to collect, use, transfer, and maintain personal data and provide strong protections for sensitive data like geolocation and health information. The NCS specifically calls for this legislation to mitigate privacy risks arising from data processing and set national requirements to secure personal data. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 22 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 22 of 38. here Line of Effort 5: Advance a Common, Rights-Respecting Vision for the Digital Future Digital solidarity is built on a shared commitment to human-rights based technology governance. The Advancing Digital Democracy (ADD) initiative, launched by USAID at the Summit for Democracy in 2021, fosters an open, secure, and inclusive digital ecosystem through programs such as partnerships with governments, private sector and civil society to strengthen legal and regulatory frameworks for data and digital technologies, and increased support for software engineers, tech companies, and researchers working to embed respect for human rights and democratic values across the tech lifecycle. In April 2022, the United States and 60 countries launched the Declaration for the Future of the Internet (DFI), bringing together a broad, diverse coalition of partners around a common, rights-respecting vision for an open, interoperable, reliable, and secure digital future. As chair of the Freedom Online Coalition in 2023, the United States prioritized protecting fundamental freedoms online; countering and building resilience to the misuse of digital technologies; advancing norms, principles, and safeguards regarding the development and use of artificial intelligence; and strengthening digital inclusion. Similarly, the United States, working with 13 other countries, launched the Global Partnership for Action on Gender-Based Online Harassment and Abuse. This partnership, which emerged from the first Summit for Democracy, is a response to the need to address technology-facilitated gender-based violence as part of a shared global agenda to promote peace, security, and stability. The United States will continue working with allies and partners to ensure digital technologies are used in a responsible and rights-respecting manner. Along with 45 partners, the United States endorsed in March 2023 Guiding Principles on Government Use of Surveillance Technologies, which are intended to prevent the misuse of surveillance technologies by governments. In addition, the Department of State will continue to advance programs that enable at-risk, vulnerable, and marginalized populations, or those who protect them, to prepare for, prevent, identify, investigate, and obtain remedy for digital abuses or other types of digital repression. The United States supports several multistakeholder efforts working to address a range of online challenges while respecting freedoms of opinion and expression, including the Christchurch Call to Action in 2019, the French-led Child Online Protection Laboratory, Freedom Online Coalition, and the Global Partnership for Action on Gender-Based Online Harassment and Abuse. The United States will continue to advocate for a rights-respecting approach consistent with protecting freedoms of opinion and expression and promoting gender equity and equality as governments around the world propose increased regulation of online platforms. Further strengthening domestic policy will enable deeper coordination with international partners on a range of digital issues. The Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, for example, has reinforced the position of the United States in international discussions on the governance of AI. The National Cybersecurity Strategy supports legislative efforts to impose robust, clear limits on the ability to collect, use, transfer, and maintain personal data and provide strong protections for sensitive data like geolocation and health information. The NCS specifically calls for this legislation to mitigate privacy risks arising from data processing and set national requirements to secure personal data. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State

National Cybersecurity Strategy Establishes U.S. Position in Global AI Governance Discussions

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 19 of 38. Line of Effort 2: Promote Common Understandings of Trust, Interoperable Standards, and Shared Values and Governance Principles for Critical and Emerging Technologies One of the most pressing challenges for digital solidarity is developing common approaches to governing critical and emerging technologies such as AI. The speed of innovation, the scale of the competition, and the stakes for our values, security, and prosperity demand concerted action. With AI technologies, we will not have the luxury of time or of pursuing narrow interests that have often slowed our ability to develop shared principles and interoperable regulatory approaches in other parts of the digital economy. Shaping shared values and governance principles on the development, deployment, and use of AI is increasingly central to American digital diplomacy. The United States is engaging allies, partners, the private sector, civil society, the technical community, and other stakeholders in discussions at the G7, Global Partnership on Artificial Intelligence, the Council of Europe, OECD, UN, UNESCO, and other fora to manage the risks of AI and ensure its benefits are widely distributed. In addition, we will need to work together to invest in the science research and infrastructure necessary to measure, evaluate, and verify advanced AI technology systems. In July 2023, President Biden announced voluntary commitments from seven leading AI companies to advance the safe, secure, and transparent development of AI technology. Eight more companies (including one foreign-based company) signed on to the commitments in September. The United States internationalized and expanded on the voluntary commitments through the G7 Hiroshima AI process led by Japan to tackle generative AI, with leaders releasing an International Code of Conduct for Organizations Developing Advanced AI systems in October 2023. We continue to work on broadening acceptance of the Code of Conduct by more countries and companies beyond G7 member countries. The United States joined twenty-seven other countries at the UK AI Safety Summit and signed the Bletchley Declaration, which encourages transparency and accountability from actors developing frontier AI technology. The United States and the United Kingdom have also signed a memorandum of understanding between their respective AI Safety Institutes advancing the science of measuring, evaluating, and addressing AI risks as a first step toward a global consensus on the scientific underpinnings of AI safety. These efforts outline a role for national governments, promote international cooperation, and encourage innovation by providing technically rigorous guidelines for introducing safe, secure, and trustworthy AI technology. At the same time, USAID and several other international development donors entered into a partnership to promote safe, secure, and trustworthy AI development in low- and middle-income countries in Africa and other parts of the world. In October 2023, President Biden issued an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. This Order establishes a process to develop new standards for AI safety and security and seeks to protect citizens’ privacy, promote innovation and competition, and advance equity and human rights. The Order tasked the Department of State with strengthening U.S. leadership abroad on AI issues. The Department of State and USAID, in collaboration with the Department of Commerce, are leading an effort to establish an AI in Global Development Playbook to harness AI’s benefits and manage its risks. Relatedly, the Department of State plans to lead an interagency task force on detecting, authenticating, and labeling synthetic content, which aims to facilitate information sharing and mobilize global commitments to both label authentic government-produced content and detect synthetic content. In addition, working with the Department of Homeland Security (DHS), the Department of State is engaging international partners to help prevent, respond to, and recover from potential critical infrastructure disruptions resulting from the incorporation of AI into critical infrastructure systems or the malicious use of AI against those systems. The Department of State and USAID are also working with interagency partners, including the National Institute of Standards and Technology (NIST), National Science Foundation (NSF), and Department of Energy, to develop a human rights risk management framework for AI and a global AI research agenda. The Department of State is also building broad-based support for the Political Declaration on Responsible Military Use of AI and Autonomy. While there are important discussions ongoing in Geneva under the framework of the Convention on Certain Conventional Weapons (CCW) – which the United States will continue to support – the scope of those discussions only covers one possible military use of AI, namely autonomous weapon systems. The Political Declaration is the first effort to articulate principles and best practices covering all military applications of AI technologies. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 19 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 19 of 38. here Line of Effort 2: Promote Common Understandings of Trust, Interoperable Standards, and Shared Values and Governance Principles for Critical and Emerging Technologies One of the most pressing challenges for digital solidarity is developing common approaches to governing critical and emerging technologies such as AI. The speed of innovation, the scale of the competition, and the stakes for our values, security, and prosperity demand concerted action. With AI technologies, we will not have the luxury of time or of pursuing narrow interests that have often slowed our ability to develop shared principles and interoperable regulatory approaches in other parts of the digital economy. Shaping shared values and governance principles on the development, deployment, and use of AI is increasingly central to American digital diplomacy. The United States is engaging allies, partners, the private sector, civil society, the technical community, and other stakeholders in discussions at the G7, Global Partnership on Artificial Intelligence, the Council of Europe, OECD, UN, UNESCO, and other fora to manage the risks of AI and ensure its benefits are widely distributed. In addition, we will need to work together to invest in the science research and infrastructure necessary to measure, evaluate, and verify advanced AI technology systems. In July 2023, President Biden announced voluntary commitments from seven leading AI companies to advance the safe, secure, and transparent development of AI technology. Eight more companies (including one foreign-based company) signed on to the commitments in September. The United States internationalized and expanded on the voluntary commitments through the G7 Hiroshima AI process led by Japan to tackle generative AI, with leaders releasing an International Code of Conduct for Organizations Developing Advanced AI systems in October 2023. We continue to work on broadening acceptance of the Code of Conduct by more countries and companies beyond G7 member countries. The United States joined twenty-seven other countries at the UK AI Safety Summit and signed the Bletchley Declaration, which encourages transparency and accountability from actors developing frontier AI technology. The United States and the United Kingdom have also signed a memorandum of understanding between their respective AI Safety Institutes advancing the science of measuring, evaluating, and addressing AI risks as a first step toward a global consensus on the scientific underpinnings of AI safety. These efforts outline a role for national governments, promote international cooperation, and encourage innovation by providing technically rigorous guidelines for introducing safe, secure, and trustworthy AI technology. At the same time, USAID and several other international development donors entered into a partnership to promote safe, secure, and trustworthy AI development in low- and middle-income countries in Africa and other parts of the world. In October 2023, President Biden issued an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. This Order establishes a process to develop new standards for AI safety and security and seeks to protect citizens’ privacy, promote innovation and competition, and advance equity and human rights. The Order tasked the Department of State with strengthening U.S. leadership abroad on AI issues. The Department of State and USAID, in collaboration with the Department of Commerce, are leading an effort to establish an AI in Global Development Playbook to harness AI’s benefits and manage its risks. Relatedly, the Department of State plans to lead an interagency task force on detecting, authenticating, and labeling synthetic content, which aims to facilitate information sharing and mobilize global commitments to both label authentic government-produced content and detect synthetic content. In addition, working with the Department of Homeland Security (DHS), the Department of State is engaging international partners to help prevent, respond to, and recover from potential critical infrastructure disruptions resulting from the incorporation of AI into critical infrastructure systems or the malicious use of AI against those systems. The Department of State and USAID are also working with interagency partners, including the National Institute of Standards and Technology (NIST), National Science Foundation (NSF), and Department of Energy, to develop a human rights risk management framework for AI and a global AI research agenda. The Department of State is also building broad-based support for the Political Declaration on Responsible Military Use of AI and Autonomy. While there are important discussions ongoing in Geneva under the framework of the Convention on Certain Conventional Weapons (CCW) – which the United States will continue to support – the scope of those discussions only covers one possible military use of AI, namely autonomous weapon systems. The Political Declaration is the first effort to articulate principles and best practices covering all military applications of AI technologies. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State

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