[102 Pages Updated Report] Business Growth Reports has a Latest Research report on Online Ordering Systems Market into its extensive repository. This research report is a compilation of ...

Online Ordering Systems Market Potential, Growth Status, Traders, Regional Overview, Trend Analysis, Revenue Expectation and SWOT Analysis till 2030

The Readers in the section will understand how the Online Ordering Systems market scenario changed across the globe during the pandemic, post-pandemic and Russia-Ukraine War. The study is done ...

Global Online Ordering Systems Market: A Deep Dive into Industry Dynamics and Future Expectations | Industry Research Biz

Online Ordering Systems Market is expected to grow at the Same Pace in FY30, Largely Driven by Volumes, Moderating Rates and expected to reach XX million US dollars, with a CAGR of xx% by 2030.

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Competitive Analysis: - benefits your analysis after businesses compete for your main customers, Online Ordering Systems Market Share by Company Information, Description and Business Overview ...

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CUBOH, Upserve) Company Profile, Main Business Information, SWOT Analysis, Price and Gross Margin, Market Share, Retailing, Consumer & Retail industry and has 102 pages in it. "Global Online ...

2030, Online Ordering Systems Market Size | Increasing Technological Advancements by Major Market Players

Pre and Post Covid Report Is Covered | Final Report Will Add the Analysis of the Impact of Russia-Ukraine War and COVID-19 on This Industry. The market is segmented on the basis of End-user ...

Online Ordering Systems Market Size 2023 Booming Worldwide by 2031

This extensive report comprises 111 pages and features a detailed table of contents, along with figures and charts that assist thorough analysis. Moreover, the report provides a comprehensive ...

Online Ordering Systems Market Size 2023 | Data-Driven Insights with Adapting to Rapid Changes by Top Players till 2030 | No of Pages 111

online ordering systems market [ 106 Report Pages] Insights 2023 by Types (, Mobile Applications, Websites, , ) , Applications (, Restaurant, Pharmacy, Shop ...

Online Ordering Systems Market 2023 Is Set To Fly High In Upcoming Year

AFP ;John Fru Ndi, a long-time opponent of autocratic Cameroonian President Paul Biya, has died aged 81 after a long illness, his party said on Tuesday.The leader and founder of the Social Democratic Front (SDF),

John Fru Ndi, long-term opponent to Cameroon's Biya, dead at 81

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Meet the Winners of Startups in North and Central America

Recently, I gave a talk at in a panel called in front of around 100 university students, entrepreneurs, and startup founders. The panel itself was facilitated by the wonderful , Assistant Professor at UC Boulder, , and co-author of the paper . Boulder Startup Week Exit to Community, Journey of 2 Startups Nathan Schneider Hacker Noon contributor "Exit To Community: Strategies for Multi-Stakeholder Ownership in the Platform Economy" You can watch (my presentation starts at around 11:15), or read the transcript (note-taking style) below. Learn more about via Nathan's article on Hacker Noon . the talk here Exit to Community here Framing of Session: As founders, we build startups to serve communities, and as users, we value the startups that help us, so in 2019 we asked: what if communities become owners of the startups? What if founders centered users in their exit strategy? An exit strategy is how we sell and transition out of business. The two dominant options for startups are going public or getting acquired, but both exits benefit investors in ways that leave everyone else out of decisions about the startup they depend on. We decided to organize around a third option, . an Exit To Community But a bold idea leads to many questions: How can we make finance, legal structures, and community leadership work together? Is the risk of a new exit strategy really worth it? Is it possible to make everyone happy? There is no one clear E2C approach. Startups are trying to build something sustainable, and they’ve stumbled or jumped into this idea in a dozen different ways. This session is part of a long series -- 7 webinars on specific topics, a 10-week cohort -- but this is the first time we hear from startups making the road by walking, figuring out how to make an exit to the community in their own way. Presentations Collaborative Notes : Usually, I wouldn’t talk about my own education in this kind of blurb, but it feels relevant. I went to this idealistic school called United World College for 2 years at 16 and basically believed that it’s possible to change the world for the better by attending the same environment with 200 students from almost 100 different countries. Then I went to Brown, majoring in international development and education, liberal af, as they say. About Linh Dao Smooke I’m a business woman now and a lot more skeptical about the state of the world but I think in my heart of hearts I'm still that 16-year-old Vietnamese who wants to believe that capitalism is not and should not be the future of humanity. Why I think it’s relevant to mention my past: My high school friend , who I haven't spoken to in over a decade, actually co-authored the with Nathan about the 3 options startups could use to successfully funnel ownership from greedy VCs to actual stakeholders. The universe works in such a strange, mysterious way! Morshed E2C paper Now on to the presentation... What is Hacker Noon? Hacker Noon publishes stories and writes software! We are a super active community- 30 published and 60+ submitted each day- 220 years worth of reading total. We also built our own platform for publishing, from scratch- which enabled us to remove Medium software. Our software could be used for licensing one day too. We don’t require anyone to log in or go through a paywall. We are profitable as of now and will be profitable by the end of the year, all thanks to a robust community and a large editorial team and collection of writers too! The Medium Fuckup (as in, they fuck us up) Our shift in 2019, Medium made a drastic move of bringing curators/publishers in and trying to remove the middle layer – that’s us – and keep the writers, a very effective bait & switch. We were one of the first collaterals of Medium's business shift (and it's not the first or last time they did it, as we soon later found out). My husband and I were proud of doing well, but they cut off our advertising model built over the years… and we felt betrayed. Importantly… we realized all of our writers were HN writers, they identified with us, and all we needed Medium for was the software. Our options: One - not sustain HN; Two raise some money from VCs and follow whatever direction they dictate; or Three: retain who we are – our writers – and build our own tech to move off of Medium. The community rallied behind us, and we raised over $1,000,000! That was the maximum we could raise under equity crowdfunding under the 2015 JOBS Act. The idea came from one of our contributing writers! The Transition We had a 6 months runway New software, lots of bugs, but we love it - lots of pain and anguish. Now, 11% of the company is owned by around 1,300 co-investors. Lessons Learned They already know what value they get from HN, and they care the most, so they deserve to keep getting it and caring for it. 1) The people who deserve to own the platform the most are the users who use the platform. We were approached by 4 VCs and bigger companies that in the short term would have made more sense financially, but in the long term, we won’t control our own destiny. Now my husband and I control ~70%; crowdfunding control 11%; 8% are owned by a strategic partner, Coil, which helped with the second round of funding. 2) Independently owned with less money is better than owned by more money with many strings attached. If E2C grows, people will figure out how to avoid “having too many cooks in the kitchen”. We don’t defer to users entirely! 3) Listen to community concerns and feedback without losing sight of your own direction. The Future We may continue to explore an E2C… perhaps tokenizing user activity? Learn more about Exit to Community here.

How Hacker Noon Leans on Our Community -  a #CUBoulder Talk by Linh Dao Smooke

Authors:
(1) Hamed Alimohammadzadeh, University of Southern California, Los Angeles, USA;
(2) Rohit Bernard, University of Southern California, Los Angeles, USA;
(3) Yang Chen, University of Southern California, Los Angeles, USA;
(4) Trung Phan, University of Southern California, Los Angeles, USA;
(5) Prashant Singh, University of Southern California, Los Angeles, USA;
(6) Shuqin Zhu, University of Southern California, Los Angeles, USA;
(7) Heather Culbertson, University of Southern California, Los Angeles, USA;
(8) Shahram Ghandeharizadeh, University of Southern California, Los Angeles, USA. Table of Links Abstract and Introduction Flying Light Specks User Interaction Multimedia Systems Challenges Related Work Conclusions and Current Efforts, Acknowledgments, and References ABSTRACT Today’s robotic laboratories for drones are housed in a large room. At times, they are the size of a warehouse. These spaces are typically equipped with permanent devices to localize the drones, e.g., Vicon Infrared cameras. Significant time is invested to fine-tune the localization apparatus to compute and control the position of the drones. One may use these laboratories to develop a 3D multimedia system with miniature sized drones configured with light sources. As an alternative, this brave new idea paper envisions shrinking these room-sized laboratories to the size of a cube or cuboid that sits on a desk and costs less than 10K dollars. The resulting Dronevision (DV) will be the size of a 1990s Television. In addition to light sources, its Flying Light Specks (FLSs) will be network-enabled drones with storage and processing capability to implement decentralized algorithms. The DV will include a localization technique to expedite development of 3D displays. It will act as a haptic interface for a user to interact with and manipulate the 3D virtual illuminations. It will empower an experimenter to design, implement, test, debug, and maintain software and hardware that realize novel algorithms in the comfort of their office without having to reserve a laboratory. In addition to enhancing productivity, it will improve safety of the experimenter by minimizing the likelihood of accidents. This paper introduces the concept of a DV, the research agenda one may pursue using this device, and our plans to realize one. 1 INTRODUCTION A Dronevision, DV, is a 3D display to design and implement next generation multimedia applications using Flying Light Specks, FLSs. An FLS is a miniature sized drone with one or more light sources with adjustable brightness [25–27]. A swarm of cooperating FLSs will render 3D shapes, point clouds, and animated sequences in a fixed volume, a 3D display. This Brave New Idea (BNI) envisions shrinking today’s room sized robotics laboratories to the size of a cuboid that fits on a user’s desk for multimedia content creation and rendering. Figure 1 shows a DV setup configured for visual display only. Four glass panes enhance the user’s safety by preventing rogue FLSs from coming to contact with a user and injuring them. Once the rendering of an illumination is verified to be safe, the user may remove the glass panes. The DV may illuminate the rose beyond the size of its base, see Figure 2. Moreover, a user may touch and interact with the illumination. For 3D media content, a DV is a low cost alternative to an expensive robotics laboratory shared by different research groups. It will provide an experimenter with a dedicated 3D testbed to debug and evaluate their hardware and software solutions. It will enable a content creator to design and implement diverse multimedia applications that range from healthcare to entertainment. With healthcare, a designer may develop a 3D FLS display to illuminate 3D MRI scans of a patient and their organs, empowering a physician to separate the different organs (illuminations) and examine them in real time. With entertainment, the designer may illuminate characters of multi-player games such as Minecraft and Fortnite to come alive and interact with one another on a table top. In addition to improving productivity, a DV enhances the safety of both the experimenter and the content creator. Figure 1 shows a DV displaying an illuminated rose with a falling petal; its original may be captured using an Intel RealSense depth camera[1]. The top panel of the DV consists of wireless charging stations. FLSs with a low remaining flight time fly through the square hole and land on a charging coil to charge their battery. Hundreds of FLSs may land on a charging coil and simultaneously charge their battery. The floor of the DV consists of a Hub, Tiles, Terminus, and a conveyor belt. Each rod on the side acts as an elevator that delivers fully-charged FLSs to the base of the DV, where they are stored on the black tiles for future deployment. The Hub is comparable to today’s servers and provides both wired and wireless connectivity to peripheral devices such as a depth camera and earbuds. Black and white tiles are modular pieces that can be plugged into other tiles to construct DVs of different lengths and depths. Black tiles serve both as a hangar and a dispatcher of FLSs, which are moved up and down using an elevator, and white tiles are rigid pieces that keep the floor together. Black tiles containing FLSs may be stacked below the white tiles, see Figure 1. When an empty black tile is pushed below a white tile, a black tile with FLSs is pushed onto the elevator to bring its FLSs to the floor of the DV; these FLSs are then dispatched to illuminate virtual objects. Failed FLSs will fall onto a conveyor belt, which is a garbage collector that deposits failed FLSs to a Terminus. The Terminus is an empty row on the DV floor, e.g., the row closest to the user (the back panel) in Figure 1 (2). The conveyor belt protects FLSs sitting on black tiles from falling FLSs that have failed. The Hub stops the conveyor belt before deploying one or more FLSs atop a black tile. A deployed FLS flies below the conveyor belt and past the Terminus to navigate to its coordinates in the DV’s display volume. Stopping the conveyor belt minimizes the likelihood of a failed FLS hitting and damaging a deployed FLS. Similarly, by placing the charging stations at the top of the DV, the likelihood of failed FLSs hitting and damaging a functional FLS that is in transit to a charging station is minimized. A DV will be both modular and configurable. Once an experimenter is satisfied with the safety of their setup in Figure 1, they may remove the glass panes and place the top as a back panel in front of the Terminus, enabling the DV to render illuminations that extend beyond the length and width of its base and allowing the user to physically interact with the illuminations (Figure 2). The haptic feedback provided by the illumination will include a combination of kinesthetic and tactile sensations. The kinesthetic (force) feedback will enable a user to experience the shape and hardness of an illuminated object. For example, a user may grasp a rose petal by its sides using their fingers. The user may press their fingers together to bend the rose petal to experience its stiffness. The tactile (skin-based) feedback will enable a user to experience the object’s surface properties. For example, FLSs with electrotactile actuators may provide the sharp sensation a user would expect when running their finger over a thorn on the virtual rose of Figure 2. The contributions of this paper include: • DV as an experimental 3D platform to design and implement next generation multimedia applications using FLSs (Sections 1 and 2). • Use of DV for haptic interactions (Section 3). • Two multimedia systems research challenges raised by a DV (Section 4). We present related work in Section 5 and brief conclusions and our current efforts to realize a DV in Section 6. This paper is available on arxiv under CC 4.0 license. [1] The use of a camera is for illustration purposes only. The shown rose is a mesh file consisting of 65K points created using Blender [3, 26]. Authors: (1) Hamed Alimohammadzadeh, University of Southern California, Los Angeles, USA; (2) Rohit Bernard, University of Southern California, Los Angeles, USA; (3) Yang Chen, University of Southern California, Los Angeles, USA; (4) Trung Phan, University of Southern California, Los Angeles, USA; (5) Prashant Singh, University of Southern California, Los Angeles, USA; (6) Shuqin Zhu, University of Southern California, Los Angeles, USA; (7) Heather Culbertson, University of Southern California, Los Angeles, USA; (8) Shahram Ghandeharizadeh, University of Southern California, Los Angeles, USA. Authors: Authors: (1) Hamed Alimohammadzadeh, University of Southern California, Los Angeles, USA; (2) Rohit Bernard, University of Southern California, Los Angeles, USA; (3) Yang Chen, University of Southern California, Los Angeles, USA; (4) Trung Phan, University of Southern California, Los Angeles, USA; (5) Prashant Singh, University of Southern California, Los Angeles, USA; (6) Shuqin Zhu, University of Southern California, Los Angeles, USA; (7) Heather Culbertson, University of Southern California, Los Angeles, USA; (8) Shahram Ghandeharizadeh, University of Southern California, Los Angeles, USA. Table of Links Abstract and Introduction Abstract and Introduction Flying Light Specks Flying Light Specks User Interaction User Interaction Multimedia Systems Challenges Multimedia Systems Challenges Related Work Related Work Conclusions and Current Efforts, Acknowledgments, and References Conclusions and Current Efforts, Acknowledgments, and References ABSTRACT Today’s robotic laboratories for drones are housed in a large room. At times, they are the size of a warehouse. These spaces are typically equipped with permanent devices to localize the drones, e.g., Vicon Infrared cameras. Significant time is invested to fine-tune the localization apparatus to compute and control the position of the drones. One may use these laboratories to develop a 3D multimedia system with miniature sized drones configured with light sources. As an alternative, this brave new idea paper envisions shrinking these room-sized laboratories to the size of a cube or cuboid that sits on a desk and costs less than 10K dollars. The resulting Dronevision (DV) will be the size of a 1990s Television. In addition to light sources, its Flying Light Specks (FLSs) will be network-enabled drones with storage and processing capability to implement decentralized algorithms. The DV will include a localization technique to expedite development of 3D displays. It will act as a haptic interface for a user to interact with and manipulate the 3D virtual illuminations. It will empower an experimenter to design, implement, test, debug, and maintain software and hardware that realize novel algorithms in the comfort of their office without having to reserve a laboratory. In addition to enhancing productivity, it will improve safety of the experimenter by minimizing the likelihood of accidents. This paper introduces the concept of a DV, the research agenda one may pursue using this device, and our plans to realize one. 1 INTRODUCTION A Dronevision, DV, is a 3D display to design and implement next generation multimedia applications using Flying Light Specks, FLSs. An FLS is a miniature sized drone with one or more light sources with adjustable brightness [25–27]. A swarm of cooperating FLSs will render 3D shapes, point clouds, and animated sequences in a fixed volume, a 3D display. This Brave New Idea (BNI) envisions shrinking today’s room sized robotics laboratories to the size of a cuboid that fits on a user’s desk for multimedia content creation and rendering. Figure 1 shows a DV setup configured for visual display only. Four glass panes enhance the user’s safety by preventing rogue FLSs from coming to contact with a user and injuring them. Once the rendering of an illumination is verified to be safe, the user may remove the glass panes. The DV may illuminate the rose beyond the size of its base, see Figure 2. Moreover, a user may touch and interact with the illumination. For 3D media content, a DV is a low cost alternative to an expensive robotics laboratory shared by different research groups. It will provide an experimenter with a dedicated 3D testbed to debug and evaluate their hardware and software solutions. It will enable a content creator to design and implement diverse multimedia applications that range from healthcare to entertainment. With healthcare, a designer may develop a 3D FLS display to illuminate 3D MRI scans of a patient and their organs, empowering a physician to separate the different organs (illuminations) and examine them in real time. With entertainment, the designer may illuminate characters of multi-player games such as Minecraft and Fortnite to come alive and interact with one another on a table top. In addition to improving productivity, a DV enhances the safety of both the experimenter and the content creator. Figure 1 shows a DV displaying an illuminated rose with a falling petal; its original may be captured using an Intel RealSense depth camera[1]. The top panel of the DV consists of wireless charging stations. FLSs with a low remaining flight time fly through the square hole and land on a charging coil to charge their battery. Hundreds of FLSs may land on a charging coil and simultaneously charge their battery. The floor of the DV consists of a Hub, Tiles, Terminus, and a conveyor belt. Each rod on the side acts as an elevator that delivers fully-charged FLSs to the base of the DV, where they are stored on the black tiles for future deployment. The Hub is comparable to today’s servers and provides both wired and wireless connectivity to peripheral devices such as a depth camera and earbuds. Black and white tiles are modular pieces that can be plugged into other tiles to construct DVs of different lengths and depths. Black tiles serve both as a hangar and a dispatcher of FLSs, which are moved up and down using an elevator, and white tiles are rigid pieces that keep the floor together. Black tiles containing FLSs may be stacked below the white tiles, see Figure 1. When an empty black tile is pushed below a white tile, a black tile with FLSs is pushed onto the elevator to bring its FLSs to the floor of the DV; these FLSs are then dispatched to illuminate virtual objects. Failed FLSs will fall onto a conveyor belt, which is a garbage collector that deposits failed FLSs to a Terminus. The Terminus is an empty row on the DV floor, e.g., the row closest to the user (the back panel) in Figure 1 (2). The conveyor belt protects FLSs sitting on black tiles from falling FLSs that have failed. The Hub stops the conveyor belt before deploying one or more FLSs atop a black tile. A deployed FLS flies below the conveyor belt and past the Terminus to navigate to its coordinates in the DV’s display volume. Stopping the conveyor belt minimizes the likelihood of a failed FLS hitting and damaging a deployed FLS. Similarly, by placing the charging stations at the top of the DV, the likelihood of failed FLSs hitting and damaging a functional FLS that is in transit to a charging station is minimized. A DV will be both modular and configurable. Once an experimenter is satisfied with the safety of their setup in Figure 1, they may remove the glass panes and place the top as a back panel in front of the Terminus, enabling the DV to render illuminations that extend beyond the length and width of its base and allowing the user to physically interact with the illuminations (Figure 2). The haptic feedback provided by the illumination will include a combination of kinesthetic and tactile sensations. The kinesthetic (force) feedback will enable a user to experience the shape and hardness of an illuminated object. For example, a user may grasp a rose petal by its sides using their fingers. The user may press their fingers together to bend the rose petal to experience its stiffness. The tactile (skin-based) feedback will enable a user to experience the object’s surface properties. For example, FLSs with electrotactile actuators may provide the sharp sensation a user would expect when running their finger over a thorn on the virtual rose of Figure 2. The contributions of this paper include: contributions • DV as an experimental 3D platform to design and implement next generation multimedia applications using FLSs (Sections 1 and 2). • Use of DV for haptic interactions (Section 3). • Two multimedia systems research challenges raised by a DV (Section 4). We present related work in Section 5 and brief conclusions and our current efforts to realize a DV in Section 6. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [1] The use of a camera is for illustration purposes only. The shown rose is a mesh file consisting of 65K points created using Blender [3, 26].

Dronevision: An Experimental 3D Testbed for Flying Light Specks

Authors:
(1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu;
(2) Eric Bier, Palo Alto Research Center, USA bier@parc.com;
(3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com;
(4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov;
(5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 2 Related Work Floor plans are crucial for many applications. Software approaches to floor plan creation depend on data availability and data format. Our work builds on previous research on data collection and floor plan computation. Data collection. Indoor environments can be captured in many formats, including RGBD images, point clouds, and triangle meshes. Zhang et al. [29] uses panoramic RGBD images as input and reconstructs geometry using structure grammars, while [21] uses a 3D scan to extract plane primitives and generates models using heuristics. A single image is used in some deep learning methods [8,11,13,14,30] to generate cuboid-based layouts for a single room. Detailed semi-constrained floor plan computations for a complete house require processing a 3D scan of the house [15]; the complete scan increases accuracy, but increases computing requirements and time. Pintore and Gobbetti [23] proposed a technique to create floor plans and 3D models using an Android device camera, leveraging sensor data and statistical techniques. Chen et al. [7] introduced an augmented reality system utilizing the Microsoft Hololens for indoor layout assessment, addressing intuitive evaluation and efficiency challenges. In our approach, we begin with a triangle mesh from a HoloLens 2, using its Spatial Mapping software [18], which has been surveyed by Weinmann et al. [27]. Floor plan computations Early methods [1,3,22,28] relied on image processing techniques, such as histograms and plane fitting, to create floor plans from 3D data. While [22] creates a floor plan by detecting vertical planes in a 3D point cloud, [3] uses planar structure extraction to create floor plans. These techniques rely on heuristics and were prone to failure due to noise in the data. There has been much progress in floor plan computation using graphical models [4,9,12]. Such models [10] are also used to recover layouts and floor plans from crowd-sourced images and location data. One interactive tool [16] creates desirable floorplans by conforming to design constraints. Pintore et al. [24] characterizes several available input sources (including the triangle meshes that we use) and output models and discusses the main elements of the reconstruction pipeline. It also identifies several systems for producing floor plans, including FloorNet [15], and Floor-SP [6]. Monszpart et al. [19] introduced an algorithm that exploits the observation that distant walls are generally parallel to identify dominant wall directions using k-means. Our approach also utilizes k-means, but does so to identify walls in all directions, not just the dominant ones. Cai et al. [5] uses geometric priors, including point density, indoor area recognition, and normal information, to reconstruct floorplans from raw point clouds. In contrast to Arikan et al. [2], which employed a greedy algorithm to find plane normal directions and fit planes to points with help from user interaction, our approach is automatic. It also differs from the work of [20], which focuses on removing clutter and partitioning the interior into a 3D cell complex; our method specifically divides the building into separate walls. Our work is related to [22] and [26]. In [22], floor plan generation starts with a laser range data point cloud, followed by floor and ceiling detection using a height histogram. The remaining points are projected onto a ground plane, where a density histogram and Hough transform are applied to generate the line segments that form a floor plan. In projecting to 2D, their method risks losing information that may be useful for creating 3D models or detailed floor plans. Similarly, [26] utilizes a histogram-based approach to detect ceilings and floors. Their method involves identifying taller wall segments to create a 2D histogram, and then employing heuristics based on histogram point density to compute the floor plan. Our approach differs from [22] and [26] by aligning the mesh with global coordinate axes and not relying on laser data or a point cloud. Working primarily with 3D data throughout the pipeline, it benefits from enhanced information and generates both a 3D model and a floor plan. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Authors: Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Abstract and Intro Related Work Related Work Methodology Methodology Experiments Experiments Conclusion & Future work and References Conclusion & Future work and References 2 Related Work Floor plans are crucial for many applications. Software approaches to floor plan creation depend on data availability and data format. Our work builds on previous research on data collection and floor plan computation. Data collection. Indoor environments can be captured in many formats, including RGBD images, point clouds, and triangle meshes. Zhang et al. [29] uses panoramic RGBD images as input and reconstructs geometry using structure grammars, while [21] uses a 3D scan to extract plane primitives and generates models using heuristics. A single image is used in some deep learning methods [8,11,13,14,30] to generate cuboid-based layouts for a single room. Detailed semi-constrained floor plan computations for a complete house require processing a 3D scan of the house [15]; the complete scan increases accuracy, but increases computing requirements and time. Pintore and Gobbetti [23] proposed a technique to create floor plans and 3D models using an Android device camera, leveraging sensor data and statistical techniques. Chen et al. [7] introduced an augmented reality system utilizing the Microsoft Hololens for indoor layout assessment, addressing intuitive evaluation and efficiency challenges. In our approach, we begin with a triangle mesh from a HoloLens 2, using its Spatial Mapping software [18], which has been surveyed by Weinmann et al. [27]. Data collection. Floor plan computations Early methods [1,3,22,28] relied on image processing techniques, such as histograms and plane fitting, to create floor plans from 3D data. While [22] creates a floor plan by detecting vertical planes in a 3D point cloud, [3] uses planar structure extraction to create floor plans. These techniques rely on heuristics and were prone to failure due to noise in the data. Floor plan computations There has been much progress in floor plan computation using graphical models [4,9,12]. Such models [10] are also used to recover layouts and floor plans from crowd-sourced images and location data. One interactive tool [16] creates desirable floorplans by conforming to design constraints. Pintore et al. [24] characterizes several available input sources (including the triangle meshes that we use) and output models and discusses the main elements of the reconstruction pipeline. It also identifies several systems for producing floor plans, including FloorNet [15], and Floor-SP [6]. Monszpart et al. [19] introduced an algorithm that exploits the observation that distant walls are generally parallel to identify dominant wall directions using k-means. Our approach also utilizes k-means, but does so to identify walls in all directions, not just the dominant ones. Cai et al. [5] uses geometric priors, including point density, indoor area recognition, and normal information, to reconstruct floorplans from raw point clouds. In contrast to Arikan et al. [2], which employed a greedy algorithm to find plane normal directions and fit planes to points with help from user interaction, our approach is automatic. It also differs from the work of [20], which focuses on removing clutter and partitioning the interior into a 3D cell complex; our method specifically divides the building into separate walls. Our work is related to [22] and [26]. In [22], floor plan generation starts with a laser range data point cloud, followed by floor and ceiling detection using a height histogram. The remaining points are projected onto a ground plane, where a density histogram and Hough transform are applied to generate the line segments that form a floor plan. In projecting to 2D, their method risks losing information that may be useful for creating 3D models or detailed floor plans. Similarly, [26] utilizes a histogram-based approach to detect ceilings and floors. Their method involves identifying taller wall segments to create a 2D histogram, and then employing heuristics based on histogram point density to compute the floor plan. Our approach differs from [22] and [26] by aligning the mesh with global coordinate axes and not relying on laser data or a point cloud. Working primarily with 3D data throughout the pipeline, it benefits from enhanced information and generates both a 3D model and a floor plan. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Related Work

Let's learn about via these 33 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Image Recognition /Learn Repo 1. Using Image Classification for Fitness and Dieting Apps Body management is desired yet hard. Knowing how much the daily take-in is can also be a challenge. Read on to use ML tech to overcome these challenges. 2. How Computer Vision Turns Images Into Arrays How Images are turned into arrays in Computer Vision 3. Universal Data Tool Introduction: Weekly Update 2 If you haven’t heard of the Universal Data Tool, it’s an open-source web or desktop program to collaborate, build and edit text, image, video, and audio datasets with labels and annotations. 4. An Intro to AI Image Recognition and Image Generation The ability of artificial intelligence to recognize and generate images or even videos is assisting businesses to enhance the speed and quality of their work. 5. How AI Is Helping To Diagnose Cancer Read how artificial intelligence is used in digital pathology for cancer diagnosis and reveal MobiDev’s research on WSI processing using machine learning models 6. This Is How Easy it Is to Read and Display Images Working with images is more and more popularized; we are going to take dive into this field, first with the basics and starting with an open CV library. 7. Small Object Detection in Computer Vision: The Patch-Based Approach How to carry out small object detection with Computer Vision - An example of finding lost people in a forest. 8. 10 Best Image Classification Datasets for ML Projects To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. These datasets vary in scope and magnitude and can suit a variety of use cases. Furthermore, the datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others. 9. What Makes "Good" Fashion Image Tagging Fashion image tagging is infamously tedious for eCommerce. But, how can AI help create accurate tags--and go a step beyond in understanding fashion information? 10. How to Build Neural Network that Recognizes People Wearing Masks CDC officially recommends wearing face masks (even though not everyone complies). Meanwhile, governments in European countries like Spain, Ukraine, or certain regions in Italy require everyone, big or small, to wear masks all the time, when shopping, walking a dog, or plainly going outside. Breaking the requirements could result in a hefty fine. 11. Image Annotation Business Models [Reviewed] In the rise of robotics, computer vision and image processing cameras, image annotation comes as the first step to get the right AI training data for Deep Learning models. Whether you build an app to allow users to snap fashion items at the store as a new omni-channel sales or use machine vision installed at edge device at the industrial facility to monitor anomalies: it starts with training massive image data sets. 12. What's The Best Image Labeling Tool for Object Detection? An image labeling or annotation tool is used to label the images for bounding box object detection and segmentation. It is the process of highlighting the images by humans. They have to be readable for machines. With the help of the image labeling tools, the objects in the image could be labeled for a specific purpose. The process of object labeling makes it easy for people to understand what is in the image. The labeling tool helps the people to mark the items in an image. There are several image labeling tools for object detection, and some of them use varied techniques for detection of the object, like a semantic, bounding box, key-point, cuboid, semantic and many more. In this article, we will talk about image labeling and the best image labeling tools. 13. Hacking Your Mind with AI: The Case of Addictive TikTok Discover how the AI algorithms used in TikTok make you addicted to the app.  Find out how companies use them to improve their marketing efforts! 14. ICDAR 2021 Competition: Detecting Tables Using Image Recognition To participate go to: https://competitions.codalab.org/competitions/26979 15. Unwrapping Wine Labels - How We Trained A Neural Network To Do It In the previous article, it was described a six-point method to unwrap wine labels. Finding anchor points were performed with Hough transform. It gave fair results for good labels, but for many real cases it was quite unstable, and the efforts to tune it didn’t help much. It became clear at some point, Hough transform itself wasn’t capable of handling the variety of label forms, so the next step was training a neural network. 16. Experiencing Seattle's Amazon Go Grocery Capitol Hill, Seattle. That’s the location of the most futuristic supermarket on the planet, the Amazon Go Grocery store. Today it opened its doors to the general public for the first time, and this was my experience. 17. The “Magic of Tidying for Photos” App — Introduction This post shares about the idea of applying Kon Marie organising method to your digital photos, and I developed an IOS app  with AppleML and PhotoKits to solve the problem. I share my reflection on lean product development strategy and my journey of making the app end-to-end using a minimum, iterable tech stack and strategy. 18. Creating an Image Recognizer on Golang Telegram Bot In this article, we will go over a project on image recognition using Go. We will also create a Telegram bot, through which we can send images for recognition. 19. Major Image Recognition And Annotation Trends Image recognition and annotation technologies are evolving. New techniques that allow you to solve a wide variety of tasks quickly appear. We are happy to present five major trends in image recognition and annotation. 20. Facial Recognition On Drone A facial recognition demonstration using Keras, Tensorflow, python and the drone Tello, from DJI. 21. Are Your Photos Exposing You? You may be accidentally sharing personal information in your photos 22. Clearview AI Fined in UK for Illegally Storing Facial Images In this thread, our community discusses Clearview's endeavor and the backlash that followed. 23. Can I Avoid Facial Recognition At The Airport? Technically, yes, but it is not always easy 24. High-Resolution Photorealistic Image Translation in Real Time You can apply any design, lighting, or graphics style to your 4K image in real-time using this new machine learning-based approach 25. Image Analysis using AWS Rekognition via Lambda Function In this blog, I am going to show you how we can use rekognition for image analysis using lambda function.we will be going to perform label detection and object detection for an image so basically we are performing image analysis in this blog. 26. Using Sparse R-CNN As A Detection Model Today, we are going to discuss a method proposed by researchers from four institutions one of which is ByteDance AI Lab (known for their TikTok App). 27. How Artificial Intelligence Can Improve Modern Radiology Artificial Intelligence in Radiology - Advantages, Use Cases & Trends. Read about the challenges radiology AI is facing on the way to deployment 28. Use Cases and Roll-Out Tips for Image Recognition in Retail Heavily shattered by the pandemic, the retail sector is on the lookout for innovation. 29. Image Analysis using AWS Rekognition via AWS CLI In this blog, I am going to show you how we can use rekognition for image analysis using AWS CLI. So you can test the image analysis on your local machine. We will be going to perform label detection and object detection for an image so basically we are performing image analysis in this blog by using AWS CLI. 30. How To Identify Trees with Deep Learning Idea / inspiration 31. How Synthetic Data is Accelerating Computer Vision In the spring of 1993, a Harvard statistics professor named Donald Rubin sat down to write a paper. Rubin’s paper would go on to change the way that artificial intelligence is researched and practiced, but its stated goal was more modest: analyze data from the 1990 U.S. census, while preserving the anonymity of its respondents. 32. What is Human Pose Estimation? Part of the broader artificial intelligence and computer vision realms, human pose estimation (HPE) technology has been gradually making its presence seen in all kinds of software apps and hardware solutions. Still, human pose estimation seemed to be stuck at the edge, failing to cross into mainstream adoption. 33. Manipulate Images Using Text Commands via this AI Manipulate Real Images With Text - An AI For Creative Artists! StyleCLIP Explained Thank you for checking out the 33 most read stories about Image Recognition on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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Let's learn about via these 76 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Vc /Learn Repo 1. Valuing a Startup with Negative EBITDA Learn how to value a startup with negative EBITDA a startup valuation tool that stands for Earnings Before Interest, Taxes, Depreciation, and Amortization. 2. What Will the Next Era of Marketplaces Look Like? 3. Li Jin: My Path to Venture Capital 4. Community-as-a-Service Concept: How You Can Monetize Access 5. Airtable: 26 Firms Offering Non-Dilutive Funding Options to Founders Raise money without giving up a piece of your company 6. Advice for Founders During Uncertain Times Having been in the VC business for 22 years now, we have had the misfortune of living through at least two major downturns. While each has its own unique characteristics, there are a few lessons that may be relevant in responding to the latest crisis. 7. Consider a Loan Instead of VC Money “If you’re starting a business and you take out a loan, you’re a moron,” said firebrand entrepreneur Mark Cuban in an interview with Bloomberg. 8. Being A Creator: Definition, Economy, Tools and More 9. Crypto VC Behavior Reveals Market Trends for 2023 and Beyond Crypto VCs scaled down funding significantly in 2022 due to the macro uncertainty and systemic risks such as Luna's collpase and the recent FTX debacle. 10. 10 First Principles from Investing Legends for Better Decision Making Now We're living in crazy times. Coronavirus, lockdowns, recession and riots. It's enough to make even the most seasoned investor wonder what to do next. Not to mention what it all does to an average person. 11. Invest in People, Not Companies: Identity-Centric Investment Model According to Paul A. Gompers and Anna Kovner’s research, serial entrepreneurs are the key driving forces in the modern business world. Those who succeeded once have significantly higher chances of succeeding in their next venture by 30% when compared to the first-timers. That makes the past performance of an entrepreneur is a strong indicator of a future success.[1] 12. What Will VC Investment Look Like in the Year Ahead? The crises of 2022, which came in the form of geopolitical instability, economic recession, and inflation, have taken a toll on the venture capital investment. 13. What'll Happen to Crypto After Black Monday 2020? Bitcoin was explicitly called “digital gold” back in 2011, and a 2010 InfoWorld article says: 14. Aves Lair Announces Three Startups Comprising Inaugural Accelerator Cohort New York, NY, (December 14, 2020) – Aves Lair, a New York-based venture capital and frontier technology accelerator, announced today the debut of three innovative startups in its Winter 2020 cohort. 15. Pecan.ai Raises 11 Million to Bring Machine Learning to Business Analysts Pecan.ai has just come out of stealth, raising an $11M Series A, to enable business analysts to build machine learning models automatically. Dell Capital led the round, joined by S capital and bringing the total funding of the company to $15M. 16. Bootstrapping A Marketplace: "Fake It Till You Make It" Strategy 17. Passion Economy: The "Influencer Stack" and Chinese Influencer Incubators [Tweetstorm] 18. How 100 True Fans Can Change Everything More than a decade ago, Wired editor Kevin Kelly wrote an essay called “1,000 True Fans,” predicting that the internet would allow large swaths of people to make a living off their creations, whether an artist, musician, author, or entrepreneur. Rather than pursuing widespread celebrity, he argued, creators only needed to engage a modest base of “true fans”—those who will “buy anything you produce”—to the tune of $100 per fan, per year (for a total annual income of $100,000). By embracing online networks, he believed creators could bypass traditional gatekeepers and middlemen, get paid directly by a smaller base of fans, and live comfortably off the spoils. 19. What is the Biggest Risk For Passion Economy Startups? Creators want a direct relationship with their own users, which is against the platforms’ DNA. E.g. FB restricts ability for large group admins to message members. Agree that platforms adding in direct monetization
features is a big risk though to new startups in the Passion Economy.
Here’s some tactics for startups to hedge against incumbents: 20. Future of Technology in Modern Education: Hallway Conversation 21. Why Do Investors Say "No"? All startups hunt for capital, and in order to attract it from investors or funds they need, first of all, to contact them. 22. Passion Economy: Not All Communities Are Created Equal 23. Why Hardware Will Be Sexy to Mainstream VC (in 5 Slides) If you're a software engineer looking to become a founder, consider hardware. There's a shift coming. 24. How I Managed a Fund that Gave No Money, Took No Equity and Had an Exit I managed a fund that gave no money, took no equity, and had an exit. Here's that story. 25. Passion Economy: Future of Social Products [Tweetstorm] I like this article by Sam Lessin on the future of social, but think there’s a big omission. Namely, one of the major opportunities in social is to first
focus on helping creators monetize and own their audiences, then layering in social elements on top. 26. Private Equity in Your Pocket: How Digitization Changed VC Investment Market With further simplification of access to the VC market, people will start massively investing in new technologies and shape the image of tomorrow. 27. An insight into Asia's Tech Startup Scene With the boom in technology, tech startups are booming all over the world, especially in the Asian region. In recent years, it has been noticed that startups are growing the track record of success stories in the business world. 28. Investment Trend in Generative AI Shows No Signs of Slowing Down Generative AI refers to the use of machine learning to create new content. It has the potential to revolutionize industries 29. The European Tech Champions Initiative Aims to Fend Off Non-EU Investors The European Tech Champions Initiative (ETCI) aims to fend off the threat of non-European investors acquiring such businesses as they become more established. 30. Meet the Writer: HackerNoon's Contributor Eran Elhanani, Co-Founder of BullPerks and GamesPad My name is Eran Elhanani, and I’m the Co-Founder of BullPerks, a decentralized VC and multichain launchpad, currently ranking as the #2 IDO investment platform. 31. Pros and Cons of Running a Managed Marketplace 32. Naming Your Startup: 6 Considerations for First Time Founders Naming your startup can be a tricky and time-consuming process. This article breaks down some tips and stories  from VC Partners and CEOs to get you started. 33. What Are Crypto Venture Capitalists (VCs) Investing in This Bearish Market? The value of a cryptocurrency can be influenced by various factors, including news about the cryptocurrency and its underlying technology, regulations etc 34. 5 Things That Make Ecommerce Startups Work 35. Notion : A Product That Users Love, and VCs Can't Invest Into If you somehow navigate the mysterious path to reach the Notion HQ at 1:00 PM PST on a Friday, you would find an empty office with two golden poodles and a mutt running around. The entire team would not be far though, just shy of a mile away sitting at Barzotto and eating Extra-Long Noodles pasta over a glass of sparkling white wine. Add some soft serve gelato to that. This is just one of the many idiosyncrasies you would find in this 20 member start-up that has captured over a million users with their sleek product. 36. Data Leak of $1B “Tech Unicorn” Gorillas Shows How Far Venture Capital Has Fallen VC money is spilling out onto the streets, and despite millions of dollars of investment tech companies still neglect their security. 37. Raising Money? Prioritize Smart Money When raising capital, not all startups have options—they only receive one term sheet from one interested investor. And that should be celebrated. However, some startups are “hot” enough to attract multiple VCs. 38. For Founders: How the Venture Capital Investors Deal With Your Pitch Deck А useful guide for founders on how to prepare a pitch deck and how we look on it as a VC investor. 39. Over 90% of Startups Fail, So it is Difficult for Banks to Commit Millions BBVA launched BBVA Spark in July 2022, its comprehensive financial services proposal for innovative companies in their various stages of growth. 40. How Aggregators Are Replacing Brands [Tweetstorm] 41. Your Complete Guide To Venture Capital Funding For Startups Venture Capital is an institutional investment made into early-stage startup companies. If you want the investment, you need to work on a growth plan. 42. How Technology is Helping Venture Capitalists Make Investment Decisions From stone tools to the invention of the wheel to steam engines to the internet, technology has come a long way, it has overtaken nearly all major aspects of our lives. Just take this article for example, you wouldn’t be reading it if it wasn’t for technology. 43. Critical Necessities of the Venture-Backed Startup Once you’ve obtained the capital required to launch your startup, it’s time to look at fulfilling your technical needs. Do you have a secure web server? An IT security specialist? A reliable computer setup? These are all important, but there’s more. 44. 3 Best Tips To Attract Investors Being An Outsider Unshackled Ventures founding partner Nitin Pachisia explains three ways he built a strong network during his fundraising journey. 45. Passion Economy is a Consumer Enterprization Movement 46. List of Top VC Firms Investing Into Promising HR Startups [Part 4] Hi there! My name is Vitalii Berdichevskyi, I’m a Product Manager. In this essay of the series I will share with you List of top VC's invested in HR Tech. Previous parts of the series you can find below: 47. Startup Metrics: The Dynamics of Network Effects [This essay was co-authored by my colleague D’Arcy Coolican (@dcoolican) and me. We also compiled a list of metrics to measure your network effects.] 48. [Announcement] Wise Raises $12M To Accelerate Embedded Banking Partnerships Wise, the leading embedded banking FinTech, that offers business banking via partnerships, announced today that it has closed a $12M Series A. 49. How Crypto Fund Iconium is Leading The Charge for Web3 In this post we’ll be looking at how crypto investment fund Iconium is leading the charge for development of Web3 infrastructure. 50. How To Raise Capital for Blockchain Projects (and other startups) Venture capitalists get pitched hundreds of times a year. And, even though countless articles have been written on the topic of how to present a startup to professional investors, entrepreneurs consistently fail to address check points needed by the investor to come to a decision. 51. The Hedge Fundization of VC The Hedge Fundization of VC 52. Rock, Paper, Blockchain Most data from government and financial institutions is still siloed. Public blockchains may be used by anyone and are permissionless. But how far off are we? 53. 263 Meetings, 12 Months and $128,700 in Startup Funding Later Is traction enough to raise funding for a Startup or do you need more than that? Here's how we raised $128,700 at Alpe. 54. Passion Economy: How to Organically Grow While Maintaining Paid Groups 55. Walnut Raises $2.5M Seed To Make Product Demos Easier For the Post-Pandemic World Seed round is being led by NFX with additional participation from other top SF Angels 56. Growing Your Influencer Brand Into a Sustainable Business 57. Networking at VC Events: How to Gain More Value? The pandemic and lockdowns have proven to the world the simple truth that we are social beings. 58. What Rowing For The U.S. National Team And Building A Startup Have In Common Saying “it’s not fair” won’t get you there. To be the best, you have to beat everyone. 59. Passion Economy Shall Be The Disruptor of 2021 and Beyond Clayton Christensen's disruption theory and what it means for the Passion Economy 60. The Secret Behind Successful Marketplaces [Tweetstorm] Thread: The best consumer marketplaces end up
supply-constrained, because they tap into an incredible amount of demand. For instance, Airbnb, Lyft, and Uber have seemingly infinite demand since the product/market fit is so strong–and this demand puts pressure on supply. 61. 7 Ways to Stand Out at a Tech Conference After more than 60 meetings at a major tech conference in Dubai Gitex Global 2022, I figured out what helps projects stand out and get stuck in the memory. 62. The State of Startups in the Southeast Fourth annual report reveals continued market maturation; $34.5 billion invested in southeastern startups since 2015 63. How to Choose the Right Partners in a Venture Capital Fund When you are searching for the right venture capital fund, there are a lot of factors that can go into deciding which fund to choose. 64. How Hacker Noon Leans on Our Community -  a #CUBoulder Talk by Linh Dao Smooke Transcript of COO Linh Dao Smooke's talk on how Hacker Noon leans on their community as a fundraising mechanism as opposed to traditional VCs. 65. Takeaways from Sequoia Capital's Black Swan Memo Soothsayer Sequoia Capital warned its portfolio companies in a memo—how can startups stay healthy? 66. Disruption Theory for the Passion Economy: 4 Major Implication (Part 2) Passion Economy platforms are rooted in a value proposition that deeply appeals to workers—they lower the barriers to earning income 67. The Passion Economy’s Worklife Balance The top-earning writer on the paid newsletter platform Substack earns more than $500,000 a year from reader subscriptions. The top content
creator on Podia, a platform for video courses and digital memberships,
makes more than $100,000 a month. And teachers across the US are
bringing in thousands of dollars a month teaching live, virtual classes
on Outschool and Juni Learning. 68. The Future of Marketplaces: Reinventing the $10 Trillion Service Economy [I co-wrote this essay with my a16z partner Andrew Chen. We’re excited about the future of marketplaces and the service economy. Hope you enjoy this glimpse into our thinking, and let us know if you have any feedback!] 69. Live Shopping Features On Social Media And Interview With Chinese Social Commerce Tycoon How new models of e-commerce can enable the Passion Economy 70. How We Closed Our First Pre-Seed Round in 2 Weeks We got accepted into YC on the 9th of December 2019. Originally, we wanted to raise a pre-seed round before starting YC on the 6th of January. But at the same time, we didn’t want to incorporate the company in 2019 so we wouldn’t have to deal with the taxes for 2019. Call us lazy! One would say lazy is not bad, as long as it’s smart lazy! 71. How to Fix Entrepreneurship in Europe I Studied Entrepreneurship in 4 Countries. Here's How To Fix it in Europe. 72. What to Learn from the Power User Curve The importance of power users Power users drive some of the most successful companies — people who
love their product, are highly engaged, and contribute a ton of value to the network. In ecommerce marketplaces it’s power sellers, in ridesharing platforms it’s power riders, and in social networks it’s influencers. 73. Startup Metrics: 16 Ways to Measure Network Effects [This post was co-authored by me and my colleague at a16z, D’Arcy Coolican (@dcoolican). This is a companion to our other essay exploring the dynamics of network effects. If you’re an operator, we’d love to hear your thoughts on how you measure your own product’s network effects!] 74. How to Build a Company in Highly Regulated Marketplaces: 8 Things to Consider What can founders / operators do when it comes to navigating the complexities that come with the territory in regulated industries? Here are some opportunities. 75. Bigger picture with the Decentralized identifiers This article summarizes DIDs and VCs' fundamental principles, describes the use in the real world, and provides bonus materials for tech-savvy readers. 76. How to Invest Intelligently in the Podcast Ecosystem In the world of podcasting, the flywheel is spinning: new technologies including AirPods, connected cars, and smart speakers have made it much easier for consumers to listen to audio content, which in turn creates more revenue and financial opportunity for creators, which further encourages high-quality audio content to flow into the space. There are now over 700K free podcasts available and thousands more launching each week. Thank you for checking out the 76 most read stories about Vc on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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