The platform automates critical compliance processes, making it easier for organizations to stay aligned with evolving standards such as the NIST Cybersecurity Framework. The platform uses AI to help organizations efficiently manage risk while ensuring their security governance remains up to ...

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Gamification and hands-on learning environments are ideal for engaging, training and developing skilled professionals.

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General Assembly, a leading provider of tech talent and training solutions, today announced new cybersecurity and IT training programs designed to hel

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Nvidia's AI dominance remains strong, but Barclays warns the semiconductor race is shifting--Marvell, Broadcom, and Lumentum are rising fast.

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Recent findings from Barnum Financial Group, as reported by Investment News, show a critical insight for financial advisors: 60% of adults would buy more financial products if only they understood them better, with this figure rising to 72% among young millennials.

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Are emerging platforms bridging the gap or breaching trust? Blockchain used to be associated with innovation and originality, with a thrill of doing something meaningful that no one has done before. However, this linkage starts to fade away as all the platforms are using the same template of ‘bridging the gap’. What once was an authentic aspiration of platforms is becoming a trend that now everyone follows. When we look at the websites and press releases of new ICOs and fintech platforms, we see the same template of phrase: “We are bridging the gap between centralized/traditional and decentralized finance/market.” Don’t get me wrong: the idea to bridge the gap is quite good, it is that good, indeed, that it was copied and took on a life of its own. Bridging the gap became a catch phrase, like ‘social responsibility’ — somebody meant it, now everyone says it, but nobody really knows what it means. Using the phrase to highlight the aspirations of the projects became seen as a race for the spot under the sun, and the instruments to build sand castles are taken from other participants on the way to the finish line. Actions of such players undermine efforts and intentions of credible actors who truly want to make an impact. Nonetheless, we need to remain critical. When you see a stone making ripples on the water, find the stone, don’t go chasing the ripples. The only way that the Blockchain industry can settle in a niche of trust and truth, and not simply unfulfilled aspirations, is if we are critical about originality of platforms’ intentions and the credibility that they entail. We need to learn about those who lead and to distinguish them from those who follow. Dubious platforms that copy unique features of others are not likely to succeed in an industry that is based on a trust protocol. However, it is responsibility of the crypto community to ensure that such projects will not stop other stunning ICOs and platforms with trustworthy ideas and original blockchain implementation — the pioneers that have contributed to the hype of bridging the gap. As for the emerging players, here is my advice to you. If you want to be successful in the blockchain industry: be daring and innovative, but above all, honest. Do not bridge the gap, rather break through a ceiling, or better, build your own path. After all, there are only so many gaps that you can bridge, after a while, you are simply stepping over a paddle. For example, , has been bridging the gap since March 2018, and now, we are bridging the last mile between the traditional finances and the future realities of the Blockchain. Let’s see how many more miles there are left to bridge. Ternion

How Many Gaps Are There Left to Bridge?

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Can Culturally Responsive Computing Close the Racial and Gender Gap in STEM?

Authors:
(1) Oscar Pedreira, Universidade da Coruna, Centro de Investigacion CITIC, Laboratorio de Bases de Datos, Facultade de Informatica;
(2) Felix García, Universidad de Castilla-La Mancha, Grupo Alarcos, Escuela Superior de Informatica, Paseo de la Universidad;
(3) Mario Piattini, Universidad de Castilla-La Mancha, Grupo Alarcos, Escuela Superior de Informatica, Paseo de la Universidad;
(4) Alejandro Cortinas, Universidade da Coruna, Centro de Investigacion CITIC, Laboratorio de Bases de Datos, Facultade de Informatica;
(5) Ana Cerdeira-Pena, Universidade da Coruna, Centro de Investigacion CITIC, Laboratorio de Bases de Datos, Facultade de Informatica. Table of Links Abstract and 1 Introduction 2 Related Work 3 A Software Architecture for the Gamification of SE Environments and 3.1 Software architecture 3.2 Gamification model 4 Gamification Engine for SE Environments 4.1 System architecture and design 4.2 Integration 4.3 Other functionalities 4.4 Support of game mechanics and elements and 4.5 Player’s site 4.6 Advanced functionalities 5 Case Study: Application of the Gamification Engine in a Real Company and 5.1 Description of the organization and its tool suite 5.2 Design 5.3 Subjects and analysis units and 5.4 Field procedure and data collection 5.5 Intervention 5.6 Analysis of results from the case study 5.7 Validity threats and limitations of the case study Discussion Conclusion and Future Work, Acknowledgment, and References 2 Related Work The field of gamification is a vast research area. One of the most significant lines of research in gamification has been the evidence about its usefulness, which was initially evaluated by Hamari et al.[9] by means of a literature review. This study concluded that “gamification does work, but some caveats exist”, as most papers report positive results from gamification (with some empirical evidence), but some underlying confounding factors were also present. Gamification in web applications was analyzed in the literature review of Xu[10]. This concluded that gamification was based on superficial game mechanics(point, level, leaderboard, and badges) and that more advanced aspects should be considered, such as social interaction and mobility, by supporting the ubiquitousness of mobile devices, as well as analytics, which must be enhanced. Much research work has considered the application of gamification in SE, the goal being to improve product quality and project results by increasing people’s motivation and engagement[8]. Many software process areas have been considered in previous research work. For example, Ref. [11] presented a systematic mapping on gamification applied to requirements engineering, where they identified research studies on applying gamification to elicitation, negotiation, and prioritization of software requirements. In Ref. [12], Fernandes et al. proposed a gamified tool, iThink, for requirements management. Reference [13] presented a systematic literature review on the gamification applied to software project management processes. One of their conclusions is that most research works on this topic applied a basic point system reward system, mainly in areas related to integration, resources, and scoping. A good number of gamified tools exist for gamified software project management, such as RedCritter (http://www.redcritter.com), Jira Hero (Atlassian, https://marketplace.atlassian.com/plugins/ com.madgnome.jira.plugins.jirachievements), or Scrum Knowsy (http://www.scrumknowsy.com/), all of these with the underlying idea of rewarding users as the project progresses. Several projects, such as Master Branch (https://masterbranch.com/) and CoderWall (https://coderwall.com/), also considered software development in some way, although they are not for a particular software development organization, but rather for communities of developers. Testing has also been considered with proposals, such as HALO[14, 15]. Reference [16] published a systematic mapping on gamification applied to software testing, concluding “the increasing interest for gamification has the potential to lead to positive outcomes”. Reference [17] proposed a game called “Code Defenders”, where some developers play the role of attackers and introduce errors in the system under testing, while other developers play the role of defenders and have to write test cases that detect those mutant versions of the system. Reference [18] presented an approach for applying gamification to software process improvement, with a focus on small and medium development companies. Reference [19] studied how gamification rules, such as establishing a time limit for development tasks and developers’ personal preferences, can affect coding results, such as the working time. Reference [20] proposed a framework for the gamification of enterprise software systems, that is, instead of focusing on the engineering processes that create the product, they focus on the gamifying the system to improve aspects such as user training, acceptance, and usage. Reference [21] addressed an important aspect of gamification, the trade-off between gamification and the participants’ privacy. A more general systematic mapping on gamification in software engineering[8], covering all works addressing the application of gamification to software engineering in any of its areas, found that the adoption of gamification in SE is going more slowly than in other domains, such as marketing, education, health, or banking. This systematic mapping identified two main gaps in the research on gamification in software engineering. One of them is that there is an evident lack of methodological support for the application of gamification elements in software engineering organizations, an issue which was addressed in our previous work[8]. Another important problem in the adoption of gamification in software engineering organizations is the lack of generic architectures and tools for this purpose. Most previous research on gamification in SE has worked with no software support at all, or with custom-developed gamified tools. We believe that this is a very significant impediment. The adoption of software development environments and tools in a real SE company is no small undertaking, and it is by no means cheap. It is not very probable that one of those tools would be changed for another one just because the latter is gamified, since it is highly unlikely that the new tool would provide the same functionality level and set of features as one of the existing, widely-used tools. The software architecture for gamification proposed in this paper aims to overcome the second set of weaknesses identified in the systematic mapping, by supporting the gamification of an existing SE environment, without replacing any of its current work tools, and centralizing all the gamification logic and additional functionalities, which are described in the next section. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Oscar Pedreira, Universidade da Coruna, Centro de Investigacion CITIC, Laboratorio de Bases de Datos, Facultade de Informatica; (2) Felix García, Universidad de Castilla-La Mancha, Grupo Alarcos, Escuela Superior de Informatica, Paseo de la Universidad; (3) Mario Piattini, Universidad de Castilla-La Mancha, Grupo Alarcos, Escuela Superior de Informatica, Paseo de la Universidad; (4) Alejandro Cortinas, Universidade da Coruna, Centro de Investigacion CITIC, Laboratorio de Bases de Datos, Facultade de Informatica; (5) Ana Cerdeira-Pena, Universidade da Coruna, Centro de Investigacion CITIC, Laboratorio de Bases de Datos, Facultade de Informatica. Authors: Authors: (1) Oscar Pedreira, Universidade da Coruna, Centro de Investigacion CITIC, Laboratorio de Bases de Datos, Facultade de Informatica; (2) Felix García, Universidad de Castilla-La Mancha, Grupo Alarcos, Escuela Superior de Informatica, Paseo de la Universidad; (3) Mario Piattini, Universidad de Castilla-La Mancha, Grupo Alarcos, Escuela Superior de Informatica, Paseo de la Universidad; (4) Alejandro Cortinas, Universidade da Coruna, Centro de Investigacion CITIC, Laboratorio de Bases de Datos, Facultade de Informatica; (5) Ana Cerdeira-Pena, Universidade da Coruna, Centro de Investigacion CITIC, Laboratorio de Bases de Datos, Facultade de Informatica. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 A Software Architecture for the Gamification of SE Environments and 3.1 Software architecture 3 A Software Architecture for the Gamification of SE Environments and 3.1 Software architecture 3.2 Gamification model 3.2 Gamification model 4 Gamification Engine for SE Environments 4 Gamification Engine for SE Environments 4.1 System architecture and design 4.1 System architecture and design 4.2 Integration 4.2 Integration 4.3 Other functionalities 4.3 Other functionalities 4.4 Support of game mechanics and elements and 4.5 Player’s site 4.4 Support of game mechanics and elements and 4.5 Player’s site 4.6 Advanced functionalities 4.6 Advanced functionalities 5 Case Study: Application of the Gamification Engine in a Real Company and 5.1 Description of the organization and its tool suite 5 Case Study: Application of the Gamification Engine in a Real Company and 5.1 Description of the organization and its tool suite 5.2 Design 5.2 Design 5.3 Subjects and analysis units and 5.4 Field procedure and data collection 5.3 Subjects and analysis units and 5.4 Field procedure and data collection 5.5 Intervention 5.5 Intervention 5.6 Analysis of results from the case study 5.6 Analysis of results from the case study 5.7 Validity threats and limitations of the case study 5.7 Validity threats and limitations of the case study Discussion Discussion Conclusion and Future Work, Acknowledgment, and References Conclusion and Future Work, Acknowledgment, and References 2 Related Work The field of gamification is a vast research area. One of the most significant lines of research in gamification has been the evidence about its usefulness, which was initially evaluated by Hamari et al.[9] by means of a literature review. This study concluded that “gamification does work, but some caveats exist”, as most papers report positive results from gamification (with some empirical evidence), but some underlying confounding factors were also present. Gamification in web applications was analyzed in the literature review of Xu[10]. This concluded that gamification was based on superficial game mechanics(point, level, leaderboard, and badges) and that more advanced aspects should be considered, such as social interaction and mobility, by supporting the ubiquitousness of mobile devices, as well as analytics, which must be enhanced. Much research work has considered the application of gamification in SE, the goal being to improve product quality and project results by increasing people’s motivation and engagement[8]. Many software process areas have been considered in previous research work. For example, Ref. [11] presented a systematic mapping on gamification applied to requirements engineering, where they identified research studies on applying gamification to elicitation, negotiation, and prioritization of software requirements. In Ref. [12], Fernandes et al. proposed a gamified tool, iThink, for requirements management. Reference [13] presented a systematic literature review on the gamification applied to software project management processes. One of their conclusions is that most research works on this topic applied a basic point system reward system, mainly in areas related to integration, resources, and scoping. A good number of gamified tools exist for gamified software project management, such as RedCritter (http://www.redcritter.com), Jira Hero (Atlassian, https://marketplace.atlassian.com/plugins/ com.madgnome.jira.plugins.jirachievements), or Scrum Knowsy (http://www.scrumknowsy.com/), all of these with the underlying idea of rewarding users as the project progresses. Several projects, such as Master Branch (https://masterbranch.com/) and CoderWall (https://coderwall.com/), also considered software development in some way, although they are not for a particular software development organization, but rather for communities of developers. Testing has also been considered with proposals, such as HALO[14, 15]. Reference [16] published a systematic mapping on gamification applied to software testing, concluding “the increasing interest for gamification has the potential to lead to positive outcomes”. Reference [17] proposed a game called “Code Defenders”, where some developers play the role of attackers and introduce errors in the system under testing, while other developers play the role of defenders and have to write test cases that detect those mutant versions of the system. Reference [18] presented an approach for applying gamification to software process improvement, with a focus on small and medium development companies. Reference [19] studied how gamification rules, such as establishing a time limit for development tasks and developers’ personal preferences, can affect coding results, such as the working time. Reference [20] proposed a framework for the gamification of enterprise software systems, that is, instead of focusing on the engineering processes that create the product, they focus on the gamifying the system to improve aspects such as user training, acceptance, and usage. Reference [21] addressed an important aspect of gamification, the trade-off between gamification and the participants’ privacy. A more general systematic mapping on gamification in software engineering[8], covering all works addressing the application of gamification to software engineering in any of its areas, found that the adoption of gamification in SE is going more slowly than in other domains, such as marketing, education, health, or banking. This systematic mapping identified two main gaps in the research on gamification in software engineering. One of them is that there is an evident lack of methodological support for the application of gamification elements in software engineering organizations, an issue which was addressed in our previous work[8]. Another important problem in the adoption of gamification in software engineering organizations is the lack of generic architectures and tools for this purpose. Most previous research on gamification in SE has worked with no software support at all, or with custom-developed gamified tools. We believe that this is a very significant impediment. The adoption of software development environments and tools in a real SE company is no small undertaking, and it is by no means cheap. It is not very probable that one of those tools would be changed for another one just because the latter is gamified, since it is highly unlikely that the new tool would provide the same functionality level and set of features as one of the existing, widely-used tools. The software architecture for gamification proposed in this paper aims to overcome the second set of weaknesses identified in the systematic mapping, by supporting the gamification of an existing SE environment, without replacing any of its current work tools, and centralizing all the gamification logic and additional functionalities, which are described in the next section. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Current Trends and Research Gaps in Gamification for Software Engineering

Table of Links Abstract and 1 Introduction 2 Related Work 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 5 The DeepSea System 5.1 Map View
5.2 Core View 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.3 Limitations 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 4.2 Workflow and Data To quantify the distribution of microbial activity in deep sea sediments, microbial ecologists, geochemists and geologists first collect biological and geological samples (often sediment, rock or pore water) from the deep ocean. These samples are then used to examine the geochemical, sedimentological, and biological characteristics of the environment. The sampled cores and associated parameters are both sparse and multidimensional (i.e., cores with a fixed location in 3D, sampled at time points ranging from days to decades). Collecting samples begins with an oceanographic research expedition, where scientists use various human- or robot-operated tools to map the study area and execute sampling dives. For the examples presented in this paper, AUVs (Autonomous Underwater Vehicles) equipped with multibeam sonar collected 1 m lateral resolution bathymetry and acoustic backscatter covering an ≈ 30 km2 area including multiple hydrothermal vent fields. With this context mapping completed, a Low Altitude Survey System (LASS) fielded on an ROV (Remotely Operated Vehicle) then mapped one vent field at much higher resolution, yielding 5 cm resolution bathymetry from a multibeam sonar (Fig. 2A), 1 cm resolution bathymetry from LIDAR, and 5 cm photomosaics from a color stereo camera rig (Fig. 2B) [37]. With these maps, scientists then planned the exploration and execution of sampling dives with ROVs or HOVs (Human Occupied Vehicles) to collect sediment samples from the ocean floor and bring them back to the ship for downstream processing. The samples DeepSee currently visualizes are sediment push cores (PC), hollow polycarbonate tubes 7 cm diameter and 30 cm tall, usually sectioned shipboard into 1 cm or 3 cm tall horizons. There are two types of data attributes: (1) core-level (Table 1, left) and sample-level (Table 1, right). For core-level analysis, there can be more than a dozen separate measurements conducted for a single horizon with a core; considering a core can have between 7-12 horizons, there are over 84 identification data points to interpret per core. For sample-level analysis, each horizon is processed for a wide variety of geochemical parameters, including sulfide, sulfate, iron, and other chemicals associated with metabolism or the state of the environment. Each horizon is also preserved and sequenced for a quantitative measure of how many microbial community members (taxa, e.g., ANME archaea or sulfate-reducing Desulfobacterota) live there. This typically results in over 500 discrete taxonomic, geochemical, or physical parameters per core, used to identify spatial trends in distributions (Sect. 4.3). Finally, cores are often collected in pairs or clusters for different experiments and assigned a core fate descriptor, e.g., “Geochem” or “Live”, which differentiates what type of metadata and parameters are associated with the core. In DeepSee, these data were stratified into three hierarchical levels and visualized in different ways: Region-level data. Region-level data (Fig. 2) are predominantly map-format, including low-altitude survey system (LASS) multi beam amplitude and bathymetry (5 cm) [31], AUV bathymetry (1 m lateral resolution), photo mosaics (5 cm resolution), and LIDAR bathymetry (2 cm). Together, these data types reveal the geological landscape (bathymetry) and surface biological and ephemeral “soft” features (photo mosaics and high-resolution LIDAR). Core-level data. Core-level data (Table 1, left) are unique x,y points within a methane seep where subsurface sediments were sampled. Cores comprise a minimum of 11 parameters stored in a CSV file, including core IDs (e.g., dive number, core number, GPS location), measurements (e.g., temperature, sulfate, sulfide), and associated data (e.g., core type, habitat description). Sample-level data. Sample-level data (Table 1, right) is typically parameters subdivided within cores into horizons of 2-3 cm each. DeepSee treats each horizon (row) as a sample belonging to a core within a seep. Recent cruises have returned cores with up to 257 depth horizons described in CSV format by 21 physicochemical parameters (e.g. pH), 12 geochemical measurements (e.g. methane concentration), and over 500 biological parameters (microbial identity and abundances). This paper is available on arxiv under CC BY 4.0 DEED license. [3] The example data is provided by Speth et al. [45]. Authors:
(1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu);
(2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu);
(3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu);
(4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com);
(5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu);
(6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu);
(7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu);
(8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu);
(9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org);
(10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org);
(11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org);
(12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu);
(13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu);
(14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu);
(15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu);
(16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov);
(17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Methodology 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 4.3 Design Challenges and User Tasks 5 The DeepSea System 5 The DeepSea System 5.1 Map View 5.2 Core View 5.1 Map View 5.2 Core View 5.3 Interpolation View and 5.4 Implementation 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.2 Expert Interviews 7.3 Limitations 7.3 Limitations 7.4 Lessons Learned 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 8 Conclusions and Future Work, Acknowledgments, and References 4.2 Workflow and Data To quantify the distribution of microbial activity in deep sea sediments, microbial ecologists, geochemists and geologists first collect biological and geological samples (often sediment, rock or pore water) from the deep ocean. These samples are then used to examine the geochemical, sedimentological, and biological characteristics of the environment. The sampled cores and associated parameters are both sparse and multidimensional (i.e., cores with a fixed location in 3D, sampled at time points ranging from days to decades). Collecting samples begins with an oceanographic research expedition , where scientists use various human- or robot-operated tools to map the study area and execute sampling dives. For the examples presented in this paper, AUVs (Autonomous Underwater Vehicles) equipped with multibeam sonar collected 1 m lateral resolution bathymetry and acoustic backscatter covering an ≈ 30 km2 area including multiple hydrothermal vent fields. With this context mapping completed, a Low Altitude Survey System (LASS) fielded on an ROV (Remotely Operated Vehicle) then mapped one vent field at much higher resolution, yielding 5 cm resolution bathymetry from a multibeam sonar (Fig. 2A), 1 cm resolution bathymetry from LIDAR, and 5 cm photomosaics from a color stereo camera rig (Fig. 2B) [37]. With these maps, scientists then planned the exploration and execution of sampling dives with ROVs or HOVs (Human Occupied Vehicles) to collect sediment samples from the ocean floor and bring them back to the ship for downstream processing. The samples DeepSee currently visualizes are sediment push cores (PC) , hollow polycarbonate tubes 7 cm diameter and 30 cm tall, usually expedition push cores (PC) sectioned shipboard into 1 cm or 3 cm tall horizons . There are two types of data attributes: (1) core-level (Table 1, left) and sample-level (Table 1, right). For core-level analysis, there can be more than a dozen separate measurements conducted for a single horizon with a core; considering a core can have between 7-12 horizons, there are over 84 identification data points to interpret per core. For sample-level analysis, each horizon is processed for a wide variety of geochemical parameters , including sulfide, sulfate, iron, and other chemicals associated with metabolism or the state of the environment. Each horizon is also preserved and sequenced for a quantitative measure of how many microbial community members ( taxa , e.g., ANME archaea or sulfate-reducing Desulfobacterota) live there. This typically results in over 500 discrete taxonomic, geochemical, or physical parameters per core, used to identify spatial trends in distributions (Sect. 4.3). Finally, cores are often collected in pairs or clusters for different experiments and assigned a core fate descriptor, e.g., “Geochem” or “Live”, which differentiates what type of metadata and parameters are associated with the core. In DeepSee, these data were stratified into three hierarchical levels and visualized in different ways: horizons parameters chemicals taxa core fate Region-level data. Region-level data (Fig. 2) are predominantly map-format, including low-altitude survey system (LASS) multi beam amplitude and bathymetry (5 cm) [31], AUV bathymetry (1 m lateral resolution), photo mosaics (5 cm resolution), and LIDAR bathymetry (2 cm). Together, these data types reveal the geological landscape (bathymetry) and surface biological and ephemeral “soft” features (photo mosaics and high-resolution LIDAR). Region-level data. Core-level data. Core-level data (Table 1, left) are unique x,y points within a methane seep where subsurface sediments were sampled. Cores comprise a minimum of 11 parameters stored in a CSV file, including core IDs (e.g., dive number, core number, GPS location), measurements (e.g., temperature, sulfate, sulfide), and associated data (e.g., core type, habitat description). Core-level data. Sample-level data. Sample-level data (Table 1, right) is typically parameters subdivided within cores into horizons of 2-3 cm each. DeepSee treats each horizon (row) as a sample belonging to a core within a seep. Recent cruises have returned cores with up to 257 depth horizons described in CSV format by 21 physicochemical parameters (e.g. pH), 12 geochemical measurements (e.g. methane concentration), and over 500 biological parameters (microbial identity and abundances). Sample-level data. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [3] The example data is provided by Speth et al. [45]. Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Authors: Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu).

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Blockchain ukax kawkhantix qullqichasiw tradicional ukax jan walt’ayaski uka chiqanakanw ch’amanchasi, Fundación Stellar Development ukax siwa

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La tecnología blockchain cierra brechas donde las finanzas tradicionales fallan, afirma Stellar Development Foundation

ステラ開発財団によると、ブロックチェーンは従来の金融がうまく機能しない部分を補う

ბლოკჩეინი ახიდებს ხარვეზებს, სადაც ტრადიციული ფინანსები ვერ ხერხდება, ამბობს Stellar Development Foundation

Blockchain Салттуу Каржы ийгиликсиз болгон жерде боштуктарды толтурат, дейт Stellar Development Foundation

ብሎክቸይን ባህላዊ ፋይናንስ ኣብ ዝፈሽለሉ ክፍተታት ይድልድል ይብል ስቴላር ዲቨሎፕመንት ፋውንዴሽን

“Stellar” gaznasynyň aýtmagyna görä, adaty maliýe şowsuz bolan “Blockchain” köprüleri

Blockchain Yi Bula Swivandla Laha Timali ta Ndhavuko ti Hlulekaka, Ku vula Stellar Development Foundation

Блокчейн усуває прогалини там, де традиційне фінансування зазнає невдачі, каже Stellar Development Foundation

恒星发展基金会称区块链弥补了传统金融的不足

A recent report, Furthering Digital Assets 2024: Pioneering Insurance Solutions for the Web3 Era, highlights a substantial coverage gap in digital asset insurance, revealing that only 3% of digital assets are currently insured. This gap leaves billions at risk, with an estimated $19 billion in losses from fraud and security breaches since 2011. The report emphasizes significant incidents that illustrate the vulnerability in the sector. These include a $650 million breach at Ronin in March 2022 and a $614 million loss from PolyNetwork in August 2021. As investments in digital assets increase, so does the call for comprehensive risk management solutions, particularly from institutional stakeholders. With more than 90% of crypto hedge funds expressing a desire for mandatory insurance on exchange-based assets and around 40% of institutional investors now holding cryptocurrency, the demand for tailored insurance products is clear. Further Ventures, the report’s creator, points to a growing interest from institutions seeking ways to protect their digital assets through robust insurance policies. The report also sheds light on recent regulatory responses. The Hong Kong Monetary Authority (HKMA), for example, has set mandates for digital asset custodians, requiring 50% insurance coverage on cold storage and 100% on hot wallets. Despite these initiatives, high premiums remain a challenge, with average rates around 0.5%-5% for custody insurance and 5-10% for slashing events and Directors & Officers (D&O) policies. According to the report, addressing the insurance gap in the digital assets industry will likely require innovation in policy structure, more accessible premium rates, and a regulatory environment that supports the development of effective, comprehensive solutions. As the sector evolves, insurance options may play a critical role in fostering institutional confidence and broader adoption of digital assets. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. A recent report, Furthering Digital Assets 2024: Pioneering Insurance Solutions for the Web3 Era , highlights a substantial coverage gap in digital asset insurance, revealing that only 3% of digital assets are currently insured. This gap leaves billions at risk, with an estimated $19 billion in losses from fraud and security breaches since 2011. Furthering Digital Assets 2024: Pioneering Insurance Solutions for the Web3 Era Furthering Digital Assets 2024: Pioneering Insurance Solutions for the Web3 Era Furthering Digital Assets 2024: Pioneering Insurance Solutions for the Web3 Era The report emphasizes significant incidents that illustrate the vulnerability in the sector. These include a $650 million breach at Ronin in March 2022 and a $614 million loss from PolyNetwork in August 2021. As investments in digital assets increase, so does the call for comprehensive risk management solutions, particularly from institutional stakeholders. With more than 90% of crypto hedge funds expressing a desire for mandatory insurance on exchange-based assets and around 40% of institutional investors now holding cryptocurrency, the demand for tailored insurance products is clear. Further Ventures, the report’s creator, points to a growing interest from institutions seeking ways to protect their digital assets through robust insurance policies. The report also sheds light on recent regulatory responses. The Hong Kong Monetary Authority (HKMA), for example, has set mandates for digital asset custodians, requiring 50% insurance coverage on cold storage and 100% on hot wallets. Despite these initiatives, high premiums remain a challenge, with average rates around 0.5%-5% for custody insurance and 5-10% for slashing events and Directors & Officers (D&O) policies. According to the report, addressing the insurance gap in the digital assets industry will likely require innovation in policy structure, more accessible premium rates, and a regulatory environment that supports the development of effective, comprehensive solutions. As the sector evolves, insurance options may play a critical role in fostering institutional confidence and broader adoption of digital assets. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. Business Blogging

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