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Matterport's Stock Is Down 17%, but There's Good News Today @themotleyfool #stocks $MTTR

In Q4 2023, the spatial data company made solid progress on several key metrics, but caution is still needed for a couple of reasons.

Matterport Stock Sinks 17% on Revenue Miss and Lighter-Than-Expected Q1 Guidance @themotleyfool #stocks $MTTR

Real estate focused virtual reality platform Matterport (NASDAQ:MTTR) missed analysts&#x27; expectations in Q4 FY2023, with revenue down 3.9% year on year to $39.55 million. Next quarter&#x27;s revenue guidance of $40 million also underwhelmed, coming in 2.9% below analysts&#x27; estimates.

Matterport (NASDAQ:MTTR) Reports Sales Below Analyst Estimates In Q4 Earnings

Matterport, a company specializing in 3D digital twins, reported its Q4 2023 financial results.

Matterport Non-GAAP EPS of -$0.04 in-line, revenue of $39.5M misses by $0.48M (NASDAQ:MTTR) | Seeking Alpha

Matterport

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At Matterport, we’ve seen firsthand the tremendous power that 3D data can have in several domains of deep . We’ve been doing research in this space for a while, and have wanted to release a fraction of our data for use by researchers. We’re excited that groups at Stanford, Princeton, and TUM have painstakingly hand-labeled a wide range of spaces offered up by customers and made these labeled spaces public in the form of the Matterport 3D dataset. learning This is the largest public dataset of its kind in the world, and the labeling of this dataset was a very significant effort. The presence of very large 2D datasets such as ImageNet and COCO was instrumental in the creation of highly accurate 2D image classification systems in the mid-2010s, and we expect that the availability of this labeled 3D+2D dataset will have a similarly large impact on improving AI systems’ ability to perceive, understand, and navigate the world in 3D. This has implications for everything from augmented reality to robotics to 3D reconstruction to better understanding of 2D images. You can access the dataset and sample code and read the paper . here here What’s in the dataset? This dataset contains 10,800 aligned 3D panoramic views (RGB + depth per pixel) from 194,400 RGB + depth images of 90 building-scale scenes. All of these scenes were captured with Matterport’s Pro 3D Camera. The 3D models of the scenes have been hand-labeled with instance-level object segmentation. You can explore a wide range of Matterport 3D reconstructions interactively at . https://matterport.com/gallery Why is 3D data important? 3D is key to our perception of the world as humans. It enables us to visually separate objects easily, quickly model the structure of our environment, and navigate effortlessly through cluttered spaces. For researchers building systems to understand the content of images, having this 3D training dataset provides a vast amount of ground truth labels for the size and shape of the contents of images. It also provides multiple aligned views of the same objects and rooms, allowing researchers to look at the robustness of algorithms across changes in viewpoint. For researchers building systems that are designed to interpret data from 3D sensors (e.g. augmented reality goggles, robots, phones with stereo or depth sensing, or 3D cameras like ours), having a range of real 3D spaces to train and test on makes the process of development easier. What’s possible with this dataset? Many things! I’m going to share a few of the areas of research Matterport is doing. We’ve used it internally to build a system that segments spaces captured by our users into rooms and classifies each room. It’s even capable of handling situations in which two types of room (e.g. a kitchen and a dining room) share a common enclosure without a door or divider. In the , this will help our customers skip the task of having to label rooms in their floor plan views. future We’re also experimenting with using deep learning to fill in areas that are beyond the reach of our 3D sensor. This could enable our users to capture large open spaces such as warehouses, shopping malls, commercial real estate, and factories much more quickly as well enable as new types of spaces to be captured. Here’s a preliminary example in which our algorithms use color and partial depth to predict the depth values and surface orientations (normal vectors) for the areas that are too distant to be picked up by the depth sensor. We’re also using it to start fully segmenting the spaces captured by our customers into objects. Unlike the 3D models we have now, these fully segmented models would let you precisely identify the contents of the space. This enables you to do a wide range of things, including automatically generating a detailed list of the contents and characteristics of a space and automatically seeing what the space would look like with different furniture. Ultimately, we want to do for the real world what Google did for the web — enable any space to be indexed, searched, sorted, and understood, enabling you to find exactly what you’re looking for. Want to find a place to live that has three large bedrooms, a sleek modern kitchen, a balcony with a view of a pond, a living room with a built-in fireplace, and floor-to-ceiling windows? No problem! Want to inventory all the furniture in your office, or compare your construction site’s plumbing and HVAC installations against the CAD model? Also easy! The paper also shows off a range of other use cases, including improved feature matching via deep learning-based features, surface normal vector estimation from 2D images, and identification of architectural features and objects in voxel-based models. Why is this preferable to a synthetic 3D dataset? Synthetic datasets are an exciting area of research and development, though they have limitations in terms of how well systems trained purely on a synthetic dataset work on real data. The tremendous variety of scene appearances in the real world is very difficult to simulate, and we’ve found synthetic datasets to be most useful as a first round of training before training on real data as opposed to the main training step. What’s next? We’re excited to hear what you all end up doing with this data! As noted above, you can access the data, code, and 3DV conference paper and we are excited to partner with research institutions on a range of projects. here If you’re passionate about 3D and interested in an even bigger dataset, Matterport internally has roughly 7500x as much 3D data than is in this dataset, and we are for a range of deep learning, SLAM, computational geometry, and other related computer vision positions. hiring We’d like to thank Matthias Niessner, Thomas Funkhouser, Angela Dai, Yinda Zhang, Angel Chang, Manolis Savva, Maciej Halber, Shuran Song, and Andy Zeng for their work in labeling this dataset and developing algorithms to run on it. We’d also like to thank all the Matterport camera owners who gave us permission to include their 3D models in this dataset. Matterport’s internal work in this area was made possible by Waleed Abdulla, Yinda Zhang, and Gunnar Hovden. Enjoy the world made possible by these spaces! We certainly have!

Announcing the Matterport3D Research Dataset

Choice Is Not the Enemy: Why Users Need More Options for Better Decision-Making

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Conclusion & Future Work and References

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 4 Experiments We evaluated our approach to capturing 3D scans using AR headsets. We compared the floor plan dimensions with actual building dimensions and provided intermediate results: floor plans and 3D models. We also calculated the time taken for our algorithm steps. To demonstrate the approach robustness, we evaluated using multiple building types, including commercial buildings B1 and B3 and a residential building B3. Additionally, we validated our floor plan generation on the Matterport2D dataset [25]. Scanned Data Analysis We evaluated the precision of floor plan generation by comparing the actual dimensions of the rooms with the computed floor plan. Figure 10(a) illustrates the measurement of the building, which was scanned twice with our AR. We call these scans S1 and S2 (see figure 10(b) and figure 10(c)) For each scan, floor plans are computed and the dimensions are computed using geometric modeling software, Rhino [17]. We then compared the computed dimensions with the actual room dimensions as shown in Table 1. These results show the applicability of our approach to many building types. Our method can compute a floor plan even from relatively incomplete mesh data. With a higher quality HoloLens scan, the resulting floor plan is more precise. Figure 11 displays S1 results: both types of floor plan and the 3D model. Orienting floor and walls We must orient the mesh properly. Spherical k-means is compute intensive so we optimize it to get good performance. In Figure 4, we see the mesh of B1 before and after alignment, which took 12.4 seconds, of which 10.6 were spent aligning walls using spherical k-means. Partitioning into stories We can detect a multi-story building and divide it into stories with an additional step. The algorithm projects triangles onto the positive y-axis and creates a histogram showing horizontal peaks. By analyzing the peaks in the histogram, we can determine the number of stories. Figures 5 and 12 show a 2-story residential building and a multi-story model from the Matterport3D dataset [25] that were partitioned into stories. Finding planar walls To generate a drafting-style floor plan, we eliminate details and identify planar walls. The modified DBSCAN algorithm is the most time-consuming step. In the model of Figure 13, with 79,931 vertices and 134,235 faces, it took 27.4 seconds to prepare the data and run DBSCAN and an additional 3.79 seconds to construct flat walls from the generated clusters. For the residential building of Figure 14, with 173,941 vertices and 285,840 faces, it took 76 seconds to prepare and run DBSCAN and 23.36 seconds to compute flat walls. The results for a Matterport3D model appear in Figure 15. Generating the floor plan The final step of our floor plan generation is to slice the mesh at different heights and superimpose the slices. Figures 13, 14, and 15 show floor plans generated using our approach. We conducted experiments to evaluate the effects of changing graphical settings when rendering pen-and-ink floor plans. Each setting consists of a different combination of line segment opacity and slice count. We found that an opacity setting of 0.5 produced a floor plan that met our expectations. We also found that a floor plan with 100 slices provided a good balance between level of detail and clutter reduction. Optimal numbers will depend on use case. 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 4 Experiments We evaluated our approach to capturing 3D scans using AR headsets. We compared the floor plan dimensions with actual building dimensions and provided intermediate results: floor plans and 3D models. We also calculated the time taken for our algorithm steps. To demonstrate the approach robustness, we evaluated using multiple building types, including commercial buildings B1 and B3 and a residential building B3. Additionally, we validated our floor plan generation on the Matterport2D dataset [25]. Scanned Data Analysis We evaluated the precision of floor plan generation by comparing the actual dimensions of the rooms with the computed floor plan. Figure 10(a) illustrates the measurement of the building, which was scanned twice with our AR. We call these scans S1 and S2 (see figure 10(b) and figure 10(c)) For each scan, floor plans are computed and the dimensions are computed using geometric modeling software, Rhino [17]. We then compared the computed dimensions with the actual room dimensions as shown in Table 1. These results show the applicability of our approach to many building types. Scanned Data Analysis Our method can compute a floor plan even from relatively incomplete mesh data. With a higher quality HoloLens scan, the resulting floor plan is more precise. Figure 11 displays S1 results: both types of floor plan and the 3D model. Orienting floor and walls We must orient the mesh properly. Spherical k-means is compute intensive so we optimize it to get good performance. In Figure 4, we see the mesh of B1 before and after alignment, which took 12.4 seconds, of which 10.6 were spent aligning walls using spherical k-means. Orienting floor and walls Partitioning into stories We can detect a multi-story building and divide it into stories with an additional step. The algorithm projects triangles onto the positive y-axis and creates a histogram showing horizontal peaks. By analyzing the peaks in the histogram, we can determine the number of stories. Figures 5 and 12 show a 2-story residential building and a multi-story model from the Matterport3D dataset [25] that were partitioned into stories. Partitioning into stories Finding planar walls To generate a drafting-style floor plan, we eliminate details and identify planar walls. The modified DBSCAN algorithm is the most time-consuming step. In the model of Figure 13, with 79,931 vertices and 134,235 faces, it took 27.4 seconds to prepare the data and run DBSCAN and an additional 3.79 seconds to construct flat walls from the generated clusters. For the residential building of Figure 14, with 173,941 vertices and 285,840 faces, it took 76 seconds to prepare and run DBSCAN and 23.36 seconds to compute flat walls. The results for a Matterport3D model appear in Figure 15. Finding planar walls Generating the floor plan The final step of our floor plan generation is to slice the mesh at different heights and superimpose the slices. Figures 13, 14, and 15 show floor plans generated using our approach. Generating the floor plan We conducted experiments to evaluate the effects of changing graphical settings when rendering pen-and-ink floor plans. Each setting consists of a different combination of line segment opacity and slice count. We found that an opacity setting of 0.5 produced a floor plan that met our expectations. We also found that a floor plan with 100 slices provided a good balance between level of detail and clutter reduction. Optimal numbers will depend on use case. 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: Experiments

Startups of The Year 2023 Winners: North America 

Artificial Intelligence is a lot more than a tech buzzword these days. This technology has disrupted almost every industry within a decade. Every company wants to implement this cutting edge technology in its system to cut costs, save time, and make the overall process more efficient with automation. Initially, AI and were as simple as Simple Perceptron models and Single layered neural networks. With the passage of time and focused research, we have complex neural networks with multi-layered architectures. Companies are using algorithms such as LSTMs, GANs, variational autoencoders in their software and services.. deep learning algorithms Given below are the hottest topics in AI and the companies working on them. Get ready to get awestruck after looking at these innovations. However, if you want to explore further, you can explore it here tl motion simulator Natural Language Processing - NLP is one of the hottest topics in AI. NLP deals with language (textual data) and performs tasks such as translation, transliteration, semantic analysis, development of chatbots, text mimicry, text to speech, etc. In the present day scenario, NLP is very crucial for organizations because it has disrupted customer support by automating the process and reducing human intervention. Natural Language Processing In addition to this, NLP techniques are also utilized in developing marketing strategies on the basis of semantic analysis done on text mined from social media platforms. In its crux, most of the NLP algorithms are based on complex deep neural networks such as RNNs, LSTMs, and GRUs. To draw a basic intuition, you can look at them as conventional neural networks that emphasize on previous data inputs (i.e. memory). Moveworks and Dialogflow are one of the leading companies based on NLP. Let us take a brief look at them. Dialogflow Dialogflow is Google’s child company which focuses on latest research on NLP techniques and develops modules, APIs, and platforms related to it. You can integrate Dialogflow’s services to Amazon Alexa, Siri, Cortana, and of course - Google Home. Its services use Long Short Term Memory (LSTM) and Recurrent Neural Networks to execute tasks related to texts and languages. Dialogflow is well known for its chatbot service. In addition to this, companies such as Dominos, Mercedes, Giorgio Armani are taking the help of Dialogflow for embedding chatbots and text to speech services within their system. You can easily read Dialogflow’s documentation and have your own chatbot ready on your website/mobile application in a matter of minutes. Moveworks Moveworks is a $200 Million company based in Mountain View, California. It was founded by Bhavesh Shah, Jiang Chen, Vaibhav Nivargi, and Varun Singh. The foundation of every chatbot and NLP product is laid by RNN or its successors. In its core, Moveworks service is also based on their custom recurrent neural networks and LSTM network trained on relevant data. Moveworks provides customer support automation and NLP solutions for business enterprises. It has served giants such as Nutanix, Autodesk, and Western Digital. If you want to cut down the IT support and automate it to save costs, you must definitely consider Moveworks. Convolutional Neural Networks or CNNs are the most widely used deep learning algorithms. CNN algorithm was a slight modification to conventional neural networks, which further emerged as a revolutionary concept in the field of artificial intelligence. Today, CNNs are used mainly for object detection, face recognition, computer vision, and forecasting techniques. Convolutional Neural Networks Convolutional neural networks involve the stacking of layers in a deep neural network with operations such as padding, convolution, scaling, etc. on the input image. The best part about CNN is that the images are converted into arrays (almost similar to the 1-dimensional matrix) and mathematical operations take place which finally results in the desired output. To implement CNN, you just need a lot of data and a computer to train your AI. Here are a couple of companies that are working on computer vision and CNNs. Matterport Matterport came into limelight when their R&D team published a paper on Mask RCNN. The company is based in Sunnyvale, California is one of the patrons of computer vision products. Moreover, they have developed an efficient computer vision algorithm used for detecting objects (Mask RCNN) which is a successor of Fast RCNN and Faster RCNN. It uses coordinates of bounding boxes and masking techniques to identify objects on which the model is trained. Apart from object detection, matterport’s Mask RCNN is also used in video calling applications to change the background. Currently, they’ve integrated augmented reality with deep learning and have invented one of the finest 3-D cameras of modern times. Matterport manufactures 3-D cameras that can be used for visualizing structures of houses, give virtual tours, and display an engaging floor plan. Neurala Based in Boston, this machine learning and software development company caters to the needs of organizations looking for process automation and computer vision solutions. Neurala’s models are used on more than 50 million devices across the globe. CB Insights has recognized Neurala amongst ‘100 Most Promising AI Companies’. Thanks to its Ph.D. holding founders who made Neurala’s AI software run on light devices including smartphones. They provide services on a wide range of devices including PCs, smartphones, drones, robots, and smart devices. These were some of the most reliable and best companies which might help you in building AI-based systems. Before blindly trusting any company on the internet, you must also take a look at these firms which might help you in cutting costs and provide you a state of the art service. Many AI developers have found that CBD has helped their clarity and consistency when developing algorithms, one of the leaders in this field has actually published an article talking about how CBD can assist with focus while programming. Wtphemp offers that are completely free from THC. Wtphemp CBD products

A Deep Learning Overview: NLP vs CNN

Erin Simpson, associate director for technology policy at Washington, D.C.–based think tank The Center for American Progress, had a message for Facebook. Last year, she co-wrote a pair of reports advising major social media platforms, like Facebook, on how to better address the twin crises of and . election delegitimization health misinformation But Facebook has also been pushing a message at her. For more than a year, Simpson had been noticing ads pop up with the same phrasing: Facebook “supports updated internet regulations,” the ads always said. The ads appeared in podcasts and on . A few months ago, when she went to the grocery store to make a copy of a key, Simpson was startled to see that even the key copying machine was flashing the message. TV "These ads follow me everywhere,” Simpson said. “How is it here? I’m at Safeway and it’s on the key copying machine. It’s taunting me.” For years, Congress has debated how to best regulate the tech giants that hold an increasing amount of control over human communication. But no comprehensive legislation dictating their behavior has been passed in a generation. According to a slew of ads like the one Simpson saw, along with public statements, blog posts, op-eds, and congressional testimony from CEO Mark Zuckerberg, Facebook would very much like to change that. The company, which spent on advertising last year, has maintained a consistent public message: Facebook is not the one slowing down internet regulation in this country, and the company would certainly welcome reasonable curbs on its power. $2.26 billion Key copying enthusiasts and members of Congress aren’t the only targets for this message. Facebook has peppered its own platform with a PR campaign aimed at users. The Markup has seen the ads turn up repeatedly on the Facebook feeds of our panel, a group of some 1,800 users that automatically share their news feed data with us. Citizen Browser Of the 1,000 most common ads appearing on our panelists’ feeds between Nov.1, 2020, and Aug 27 of this year, just one ad was from Facebook itself: “It’s been 25 years since comprehensive internet regulations were passed,” the text of the ad says. “It’s time for an update.” Targeting information collected by Citizen Browser showed the ads were aimed at users interested in “politics” and, where additional ideological targeting was available, largely to “people in the USA who are likely to engage with liberal political content.” But why is Facebook pushing for “regulations” that would curb its own power, and what exactly are the regulations it supports? The ads are not explicit, but proposals and pieces of proposals laid out on Facebook’s , which is linked in the Facebook ads, as well as in and congressional from Facebook CEO Mark Zuckerberg, offer some insight into what Facebook wants. regulations hub op-eds testimony Facebook also provided directly when The Markup reached out for information. additional materials In short, Facebook’s proposals seem to consist largely of implementing requirements for content moderation systems that Facebook has previously put in place, potentially forcing competitors to do work Facebook has already done. While there are instances, in some cases, of Facebook asking for guidance on how certain thorny questions should be handled, most of Facebook’s proposals seem unlikely to fundamentally change how the company itself does business. For skeptics about Facebook’s efforts, like Nathalie Maréchal, who does work on platform accountability at the New America Foundation’s project, the biggest problem with Facebook’s proposals are the things it doesn’t mention—the nonpolitical advertising that is the core of Facebook’s business. Ranking Digital Rights “The bigger picture here is that as long as everyone is focused on user content and all of its discontents, we are not talking about advertising. We are not talking about the money,” Maréchal explained. “It’s a PR blitz to get lawmakers and other policy-adjacent people to believe that Facebook is not standing in the way of change.” Why Is Facebook Saying It Wants Regulation? Surveys show Americans are broadly supportive of imposing new regulations on social media platforms. For example, a recent Morning Consult found that more than three-quarters of Democrats and more than half of Republicans would back legislation holding platforms accountable for spreading COVID-19 misinformation. poll Lawmakers around the country have taken notice of that sentiment. There were introduced this year regarding digital privacy. 31 state-level bills Lawmakers in have introduced bills this year that would penalize social media platforms for taking down political content, most of which were introduced after former president Donald Trump was suspended from the major social media platforms for provoking violence in the wake of the 2020 election. 18 states There’s also been action at the federal level, with legislation like Sens. Amy Klobuchar and Ben Luján’s or Sens. Brian Schatz and John Thune’s . Misinformation Act Platform Accountability and Consumer Transparency (PACT) Act Last year, the House Antitrust subcommittee issued a calling out Facebook for anti-competitive practices, and the Biden administration has pursued into the company’s practices. blistering report ongoing investigations Regulation, it seems, is in the air. Facebook, experts say, has a strong incentive to get out in front of it while also steering the conversation about new rules to the areas where it's comfortable seeing tighter government controls and away from areas where it isn’t. “Facebook is just reading the tea leaves,” Simpson said. “And they’re trying to put themselves in a position where they think they would have some credibility in the legislative conversation by saying that they’re interested in policy—not that they’re reflexively against it.” When The Markup reached out to Facebook asking for details on its regulatory proposals, spokesperson Rachel Holland replied with a statement highlighting how Facebook developed its moderation processes. “Facebook regularly works with external stakeholders when developing our Community Standards to balance competing interests, such as freedom of expression, safety, and privacy,” Holland said. “Depending on the topic, we will continue to work with subject matter experts, civil society organizations, and law enforcement, among others, to develop robust content policies that keep communities safe and apply to a global user base.” Facebook isn’t the first technology company to publicly push for greater regulation. Microsoft, for example, has been a of tighter regulations on the use of facial recognition technology. major supporter A law passed in the company’s home state of Washington last year regulating facial recognition technology was sponsored by a lawmaker who for Microsoft. literally worked Facebook spent nearly lobbying the U.S. government last year, making it the of any organization. However, Sen. Mark Warner, who has introduced about regulating big tech platforms, said Facebook hasn’t been doing a lot of the things savvy corporations do when they want their favored legislation passed. $20 million sixth-highest spender several bills “There’s no doubt that Facebook, like other industry giants, knows exactly what it takes to get a bill passed and signed into law. Unfortunately, despite a pervasive ad campaign designed to tout its support for updated internet regulations, Facebook’s proactive engagement has been limited and it has continued to sponsor a number of groups focused on undermining regulatory proposals,” Warner told The Markup. “As we continue to try to bring internet law into the 21st century, I invite Facebook to engage meaningfully with my office on this complex issue.” Facebook’s campaign is more than political; it’s public relations, said Matt Navarra, a U.K.-based social media consultant who produces a newsletter and podcast about social platforms. Facebook’s decision to go directly to the public with its legislative message, in addition to its lobbying, he said, is part of a larger shift in brand marketing, largely enabled by social platforms like Facebook. “It wasn't so many decades ago that if a company had a big issue it was trying to influence, it would just stick to the usual routes of lobbying ministers and making their products seem super amazing,” Navarra explained. “Now it’s easy to build a community around a cause and activate people. We've seen a shift in how people communicate and engage with each other because of those platforms, and that's led to a shift in why companies want to reach consumers with these messages. What Exactly Is Facebook Proposing? Facebook’s ad campaign centers on the Communications Decency Act. The 1996 law has a provision, , which makes websites generally posted by users and provides the legal basis platforms use to justify their content moderation policies. Section 230 not liable for the content Section 230 is widely considered the guiding legal principle for the modern internet; since many people across the political spectrum have qualms about the modern internet, Section 230 has become something of a flashpoint. Facebook’s ads, which often at least obliquely reference Section 230, wisely don’t mention many of the specific reasons people might be upset enough with Facebook to desire new rules governing its conduct. Instead, the ads focus on the law’s age. The quarter-century since its passage is an eternity in internet years. In before Congress in March, Zuckerberg proposed that the law should be updated to require tech platforms to have certain content moderation policies and systems in place to catch “unlawful content and activity on their platforms.” testimony If those systems exist, platforms can continue to enjoy all the protections—meaning essentially zero liability for anything that’s posted on their platforms—of Section 230. He went on to ask Congress to pass legislation to “bring more transparency, accountability, and oversight to the processes by which companies enforce their rules about content that is harmful but legal.” While Zuckerberg’s testimony doesn’t go into detail, proposals in Facebook’s public-facing policy documents, conveniently, mostly happen to be things that Facebook already put in place following public pressure. Some are things already required of the company by laws like the European Union’s General Data Protection Regulation or the California Consumer Privacy Act ( ). GDPR CCPA These processes, outlined , include: here Allowing users to report problem content and having a content moderation program in place—both of which Facebook . already does Having some form of external body evaluate content moderation standards and also provide oversight on enforcement decisions. Facebook already has an . oversight board Publishing content moderation standards, which Facebook after those standards were to The Guardian. began doing leaked Providing transparency into how content moderation standards are set, which Facebook has done since 2018 by publishing the of the internal meetings where its content policy is set (although those minutes were eventually replaced by glossy, less informative slide presentations). minutes Giving users input on content rules, which Facebook at least ostensibly does through its process. Stakeholder Engagement Allowing affected users to appeal content moderation decisions, a process Facebook in 2018. implemented Requiring responses to all user-initiated content reports, which Facebook users can on the platform. track the status of Notifying users when their content is removed, which Facebook . does Disclosing publicly aggregate content enforcement numbers, which Facebook has been doing for years, with companies’ potentially getting hit with more oversight or even sanctions if the prevalence of offending content on their platform hits a certain threshold. quarterly The documents in Facebook's regulation hub also spell out a handful of things Facebook want to see in new rules. doesn’t The company says it is requiring platform moderators to pre-approve user content before it can be posted, short time frames for taking down content, the criminalization of misinformation, and any kind of specific product design mandates. against Facebook also says that, ideally, any content moderation requirements should scale up with size, giving smaller platforms more leeway than larger ones. Facebook singled out existing proposals for regulating political ads to combat foreign interference—the and the —as ones it supports, although, as Quartz , Facebook simultaneously claimed to back the Honest Ads Act in public while lobbying against its passage in private. DETER Act Honest Ads Act reported The company favors passing federal privacy legislation modeled very closely on privacy rules already on the books in Europe. “I believe it would be good for the internet if more countries adopted regulation such as GDPR as a common framework,” Zuckerberg in a 2019 Washington Post op-ed. wrote In that same op-ed, he supported requiring platforms to have data portability, which is the idea that, as Zuckerberg wrote, “if you share data with one service, you should be able to move it to another.” An element of data portability is mandated as part of the GDPR and the CCPA, and in the op-ed, Zuckerberg highlighted Facebook’s support for the , an open-source initiative creating a common framework for sharing user data among online service providers. Data Transfer Project Facebook has a for users to port things like their photos and posts but doesn’t currently offer the ability to move users’ friends lists to other services, as as a way to more meaningfully increase competition. tool some have called for In addition to requiring some measure of data portability, Facebook it hopes the government provides guidance on some of the privacy issues surrounding moving data across platforms, particularly when moving one person’s data also involves transferring information about other users. said For example, what happens if someone ports their friends list from a social network where people can go by pseudonyms to one where everyone uses their real name? That could expose people's real identities without their consent. Daphne Keller, director of the Program on Platform Regulation at Stanford’s Center for Internet and Society, said this desire for clarity is likely born out of regulatory inconsistencies surrounding data portability under E.U. law that the company wouldn’t want to see replicated under U.S. law. “They wanted people to stop asking them for incompatible things, for someone with authority to tell them what the real answer is,” Keller said. “They don’t care what it is. They just want someone to decide so they're not in an impossible position.” Could Passing Facebook’s Proposals Actually Be Harmful? Offering already implemented policy changes, said Maréchal of Ranking Digital Rights, lets Facebook crowd out tougher legislation, like a proposed bill that would require transparency in . targeted digital advertising The bill, which has , popped up in May in the wake of Facebook’s to NYU researchers studying ad targeting on the platform. not yet come up for a vote cutting off access Many of Facebook’s now favored policies, Maréchal noted, were pushed by public interest groups over the course of a decade before Facebook finally implemented them. “They're conceding on a point that they've already lost,” she said. And new regulations might very well hurt competitors more than they hurt Facebook. Even just dragging GDPR across the pond from Europe, as Facebook suggests, would trigger significant compliance costs from U.S. companies. While major players with big European footprints, like and , have GDPR compliance all squared away, Keller notes that many other U.S. firms would almost certainly have to invest significant resources to survive an investigation by European regulators. Facebook Google Scaling these requirements up as companies grow, as Facebook suggests, is a common feature in this type of legislation. However, Aaron Mackey, a senior staff attorney at the Electronic Frontier Foundation who has about Facebook’s proposals, worries that setting up a regulatory framework where the end goal is to be like Facebook directly encourages an internet where everything works like Facebook. written critically “Not every large social media service desires or aspires to be like Facebook or even aspires to monetize their users in a certain way. That’s a problem too, this idea that the law should be modeled off of one individual company and the way they have developed their business model,” Mackey said. “There are a lot of for-profit or community-led or individual-led user generated services and social networks that don't have that model and they don't aspire to be like Facebook.” Facebook’s proposals haven’t seemed to gain much traction on Capitol Hill. After Zuckerberg put forward these ideas in congressional testimony earlier this year, Politico lawmakers on both sides of the aisle who were deeply skeptical of his intentions. talked to “Mark Zuckerberg knows that rolling back section 230 will cement Facebook's position as the dominant social media company and make it vastly harder for new startups to challenge his cash cow,” Sen. Ron Wyden, a Democrat who was an original author of Section 230, told Politico. “Section 230 reform will hit Facebook regardless of what these self-interested Silicon Valley CEOs want,” concurred Republican senator Marsha Blackburn, also to Politico. “Big Tech only wants reform when it bolsters their power at the expense of competitors.” Additional reporting by Corin Faife This article was originally published on The Markup and was republished under the Creative Commons Attribution-NonCommercial-NoDerivatives license. Written by By: Aaron Sankin.

How Much Internet Regulation Does Facebook Support? 

Recent technological advances such as Virtual Reality has grown rapidly into a viable medium that connects us in the real world with cyberspace. Enhancing the customer service of companies is no longer a dream, as it has been happening already in some sectors of business for years. With an increasing potential of truly exciting and immersive experiences, Virtual Reality is already used in applications within retail, medicine, education, and many other. Here are just a few instances of VR’s practical uses for a better customer experience. 1.	LiveLike - Experience Sports in VR Combining streaming of live sports with the immersion of VR technology, LiveLike is the most innovative sports viewing experience ever built. With an onsite technical crew of as little as five staff members controlling unmanned cameras, LiveLike made it possible to stream sport from within a virtual stadium. The live streaming white-label platform by LiveLike allows broadcasters to stream their content to end-users on regular broadcast television, on mobile phones, or as interactive Virtual Reality. Because VR technology is constantly and rapidly improving, the experience is currently still a work in progress, so viewers who expect perfect and complete immersion will have to be patient. 2.	Crystal Bathrooms - of Bathroom Renovations Virtual Reality Tour Because building plans and architecture terminology is quite often a confusing experience for a lot of people, Crystal Bathrooms decided to make a change and embrace some of the recent advances in technology. Instead of asking customers to use imagination when looking at a graphic designer’s 2D interpretation of their bathroom reno, they can use a software program combined with a pair of VR goggles to explore a wide range of design options in Virtual Reality. By designing a virtual 3-dimensional environment for their clients to walk through, Crystal Bathrooms had also found themselves a unique sales hook which would also ultimately help to increase their sales as well as their customer satisfaction. Because homeowners can be completely confident knowing exactly what their new bathroom will look like before they pay even their deposit or make their final decision. 3.	Talespin - Improving Staff Training Often highlighted as professionally valuable to employers, soft skills are also some of the more difficult to teach. That’s why spatial computing company Talespin have developed virtual human technology to provide a platform for employees to focus and practice these nuanced interpersonal skills. By taking advantage of the ongoing technological advancements in the realms of Artificial Intelligence and Virtual reality, Talespin have created a method to teach humans by utilizing machine learning. Staff training in Virtual Reality reduces how long it takes to learn by changing how this knowledge is transferred, which ultimately helps staff by teaching them how to master these soft skills. 4.	Black Box - Gym Training Immersive Experience Meet Black Box VR, the first virtual reality and full-fitness gym in the world. Utilizing an integrated system of cable pulleys controlled by a virtual reality engine, Black Box VR uses strength-building resistance to help the whole body to build real muscle. Black Box VR makes this possible through the unique combination of resistance variability in real-time and total immersion in virtual reality. Similar to other VR games, your body becomes the controller with Black Box VR. After working out for only 30 minutes, fitness results are optimised for every minute due to the combination of an adaptive workout with artificial intelligence. 5.	Surgical Theatre – For Doctors & Patients Precision Virtual Reality Created using Surgical Theatre’s Medical Virtual Reality platform, Precision VR is the world’s first fully immersive Virtual Reality environment designed specifically for the medical industry. Used every day by neurosurgeons and doctors in hospitals, universities, and medical institutions all around the globe, Precision VR provides doctors with next-generation tools for increased planning and navigation capabilities, before the patient is even being prepared for their operation. Precision VR uses Virtual Reality to create a 3-dimensional digital version of the patient’s own anatomy, which allows them to experience walking around between their blood vessels and the foreign growth that needs to be removed. Surgical Theatre provides patient access to Precision VR in the days and weeks leading up to an operation, because it often results in unparalleled engagement, a better understanding of the procedure, and they will ultimately feel much more comforted. 6.	Matterport – your home before you buy Virtual Reality Tours One of today’s leading spatial data companies, Matterport primarily focuses on indexing the built-up world around us to create a truly immersive digital version. Matterport enables an extremely accurate 3D digital twin by using their powerful neural network and deep learning. Called the Cortex AI program, it can be used to understand, design, and create any space, as well as automate any associated tasks required. The powerful 3D data platform from Matterport lets you create and combine experiences unlike anything you’ve seen. Digitise your space, get accurate measurements, view it from multiple angles, build on the current functionality, and so much more. As you can see above, we have already begun seeing brands come alive with virtual worlds, but this is only the beginning. Virtual Reality has a profound ability to improve the experiences of customers an inspire emotional stimuli, so you really should be taking full advantage of the tech before it becomes the norm.

6 VR Applications Currently Improving Customer Experience Across Industries

This article proposes an easy and free solution to train a Tensorflow model for instance segmentation in Google Colab notebook, with a custom dataset. was about Object Detection in Google Colab with Custom Dataset, where I trained a model to infer bounding box of my dog in pictures. The protagonist of my article is again my dog: in this case we take a step forward, we identify not only the bounding box, we make even pixel wise classification. Previous article Compared to previous article, we hold the same characteristics: Only requirement is the dataset, created with annotation tool A single Google Colab notebook contains all the steps: it starts from the dataset, executes the model’s training and shows inference It runs in Google Colab (GPU enabled environment) and Google Drive storage, so it’s based exclusively on free cloud resources These features allow anybody following this tutorial to create an instance segmentation model, and test it in Google Colab or export the model to run in a local machine. Source code of this article, including the sample dataset, is available in my . Github repo Choosing Framework There are various open source frameworks to implement instance segmentation, you can find an overview in of Stanford University. this presentation We discard solutions that are not based on Tensorflow, such as Facebook Detectron based on Caffe2, because we decided to train the model in Google Colab, that is already integrated with Tensorflow. One of the most popular frameworks, easy to use and well documented, is . From my tests it’s one of the simplest and most robust implementations available. Matterport Mask R-CNN In addition, a big effort I faced with other implementations is to convert the annotations output file to framework input format. To be clear, once you create the pixel annotations of dataset with a graphical tool, you should convert it to input format defined by training framework. Matterport developed this task in a clear , demonstrating how to transform annotations file to Matterport Mask R-CNN format. article Making Dataset In previous article we created bounding box annotations to obtain object detection model, now we are going to train instance segmentation model, therefore we create pixel level mask annotations to define the boundaries of the objects in dataset. Among various available tools, I chose an intuitive and well done tool: by University of Oxford, you can see documentation in the of the project. Furthermore, it’s easy to integrate VIA with Matterport framework. VGG Image Annotator (VIA) official page This tool doesn’t need any installation, you just download the package and open the file with a modern browser. via.html It’s important to create a good dataset to achieve a well performing trained model. Taking pictures of objects with different lighting conditions, from various angles and in different contexts, are good principles to obtain a well generalized model, and avoid overfitting. Dataset of dog At the end of the annotation process, I created “images.zip” file with the following structure: images.zip|- "train" directory|- jpg image files of training data|- "via_region_data.json" annotations file of training data|- "val" directory|- jpg image files of validation data|- "via_region_data.json" annotations file of validation data Lastly I uploaded zip file into Google Drive, to use it during the training and test process. I included the dataset file in my Gitub repo, having pixel wise annotations of dog images. Training Model All the steps are in Google Colab notebook included in my repo. In my example, training process last about half an hour for 5 epochs, to get a more accurate model you can increase the number of epochs and the dataset size. I selected Python3 GPU enabled environment, to use up to 12 hours of Tesla K80 GPU offered in Google Colab. Next steps in notebook are: : install packages, repositories and environment variables for Matterport instance segmentation with Tensorflow. Install required packages : download images.zip dataset in Google Colab filesystem, previously uploaded in Google Drive. Update variable with Google Drive id of your image.zip dataset. Download and extract dataset fileId : code in my repo is inspired by Matterport Splash of Color sample, to run with a different dataset you should replace occurrences of “balloon” and “Balloon” with the name of object. Edit settings file : use pretrained weights to apply transfer learning in training process. Options are COCO and ImageNet. Train model Training process outputs the structure of neural network and various parameters, like the network architecture (Resnet50 or Resnet101). Using TensorFlow backend.Weights:  cocoDataset:  dataset/Logs:  /logs Configurations:BACKBONE                       resnet101BACKBONE_STRIDES               [4, 8, 16, 32, 64]BATCH_SIZE                     2...GPU_COUNT                      1GRADIENT_CLIP_NORM             5.0IMAGES_PER_GPU                 2...Selecting layers to trainfpn_c5p5               (Conv2D)fpn_c4p4               (Conv2D)fpn_c3p3               (Conv2D)fpn_c2p2               (Conv2D)... Below the Tensorboard charts of training process: Tensorboard charts Inference Finally we can run test dataset inference with trained model. Output includes inference data (image resolution, anchors shapes, …), and test images with bounding box, segmentation mask and confidence score. Conclusions If you want to run instance segmentation on a single object class, you can make a few minor changes to my Github code and adapt it to your dataset. I hope you liked this article, in case leave some claps, it will encourage me to write other practical articles about machine learning for computer vision :)

Instance Segmentation in Google Colab with Custom Dataset

This post is curated by that an issue based bounty platform for open source projects. It is the easiest way to make bounty program for OSS. IssueHunt Anyone can fund any issues on GitHub and these money will be distributed to maintainers and contributors 😃 IssueHunt help build sustainable open source community by rewarding project owners and developers. _IssueHunt 🦉 = OSS Development ⚒ + Bounty Program 💰. IssueHunt is an issue-based bounty platform for open source…_issuehunt.io IssueHunt - Boost your issue and boost your happiness 1) TensorFlow Models If you are interested in Machine learning and Deep learning, you must have heard about the TensorFlow. TensorFlow Models is the open-source repository to find many libraries and models related to deep learning. GitHub: https://github.com/tensorflow/models 2) Keras Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. GitHub: https://github.com/keras-team/keras 3) Flask Flask is a lightweight WSGI web application framework. It is designed to make getting started quick and easy, with the ability to scale up to complex applications. It began as a simple wrapper around Werkzeug and Jinja and has become one of the most popular Python web application frameworks. GitHub: https://github.com/pallets/flask 4) scikit-learn scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. GitHub: https://github.com/scikit-learn 5) Zulip Zulip is a powerful, open source group chat application that combines the immediacy of real-time chat with the productivity benefits of threaded conversations. Zulip is used by open source projects, Fortune 500 companies, large standards bodies, and others who need a real-time chat system that allows users to easily process hundreds or thousands of messages a day. With over 300 contributors merging over 500 commits a month, Zulip is also the largest and fastest growing open source group chat project. GitHub: https://github.com/zulip/zulip 6) Django Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. GitHub: https://github.com/django/django 7) Rebound Want to save your time during coding session when you get a bug and you want to search it on Stack Overflow? Rebound is a command-line tool that instantly fetches Stack Overflow results when you get a compiler error. This is very handy library for programmers. GitHub: https://github.com/shobrook/rebound 8) Google Images Download This is a command line python program to search keywords/key-phrases on Google Images and optionally download images to your computer. You can also invoke this script from another python file. GitHub: https://github.com/hardikvasa/google-images-download 9) YouTube-dl Youtube-dl — download videos from youtube.com or other video platforms. GitHub: https://github.com/rg3/youtube-dl 10) System Design Primer This repo is an organized collection of resources to help you learn how to build systems at scale. GitHub: https://github.com/donnemartin/system-design-primer 11) Mask R-CNN Mask R-CNN is for object detection and segmentation. This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It’s based on Feature Pyramid Network (FPN) and a ResNet101 backbone. GitHub: https://github.com/matterport/Mask_RCNN 12) Face Recognition Recognize and manipulate faces from Python or from the command line with the world’s simplest face recognition library. This also provides a simple face_recognition command line tool that lets you do face recognition on a folder of images from the command line! GitHub: https://github.com/ageitgey/face_recognition 13) snallygaster Tool to scan for secret files on HTTP servers. GitHub: https://github.com/hannob/snallygaster 14) Ansible Ansible is a radically simple IT automation system. It handles configuration-management, application deployment, cloud provisioning, ad-hoc task-execution, and multinode orchestration — including trivializing things like zero-downtime rolling updates with load balancers. GitHub: https://github.com/ansible/ansible 15) Detectron Detectron is Facebook AI Research’s software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. GitHub: https://github.com/facebookresearch/Detectron 16) asciinema Terminal session recorder and the best companion of asciinema.org. GitHub: https://github.com/asciinema/asciinema 17) HTTPie HTTPie is a command line HTTP client. Its goal is to make CLI interaction with web services as human-friendly as possible. It provides a simple http command that allows for sending arbitrary HTTP requests using a simple and natural syntax, and displays colorized output. HTTPie can be used for testing, debugging, and generally interacting with HTTP servers. GitHub: https://github.com/jakubroztocil/httpie 18) You-Get You-Get is a tiny command-line utility to download media contents (videos, audios, images) from the Web, in case there is no other handy way to do it. GitHub: https://github.com/soimort/you-get 19) Sentry Sentry fundamentally is a service that helps you monitor and fix crashes in realtime. The server is in Python, but it contains a full API for sending events from any language, in any application. GitHub: https://github.com/getsentry/sentry 20) Tornado Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed. By using non-blocking network I/O, Tornado can scale to tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user. GitHub: https://github.com/tornadoweb/tornado 21) Magenta Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new deep learning and reinforcement learning algorithms for generating songs, images, drawings, and other materials. But it’s also an exploration in building smart tools and interfaces that allow artists and musicians to extend their processes using these models. GitHub: https://github.com/tensorflow/magenta 22) ZeroNet Make decentralized websites using Bitcoin crypto and the BitTorrent network. GitHub: https://github.com/HelloZeroNet/ZeroNet 23) Gym OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This is the gym open-source library, which gives you access to a standardized set of environments. GitHub: https://github.com/openai/gym 24) Pandas Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is already well on its way toward this goal. GitHub: https://github.com/pandas-dev/pandas 25) Luigi Luigi is a Python package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more. GitHub: https://github.com/spotify/luigi 26) spaCy spaCy is a library for advanced Natural Language Processing in Python and Cython. It’s built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 20+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration. GitHub: https://github.com/explosion/spaCy 27) Theano Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation. GitHub: https://github.com/Theano/Theano 28) TFlearn TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. GitHub: https://github.com/tflearn/tflearn 29) Kivy Kivy is an open source, cross-platform Python framework for the development of applications that make use of innovative, multi-touch user interfaces. The aim is to allow for quick and easy interaction design and rapid prototyping whilst making your code reusable and deploy-able. GitHub: https://github.com/kivy/kivy 30) Mailpile Mailpile is a modern, fast web-mail client with user-friendly encryption and privacy features. The development of Mailpile is funded by a large community of backers and all code related to the project is and will be released under an OSI approved Free Software license. GitHub: https://github.com/mailpile/Mailpile 31) Matplotlib Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. GitHub: https://github.com/matplotlib/matplotlib 32) YAPF YAPF takes the code and reformats it to the best formatting that conforms to the style guide, even if the original code didn’t violate the style guide. GitHub: https://github.com/google/yapf 33) Cookiecutter A command-line utility that creates projects from cookiecutters (project templates), e.g. creating a Python package project from a Python package project template. GitHub: https://github.com/audreyr/cookiecutter 34) HTTP Prompt HTTP Prompt is an interactive command-line HTTP client featuring autocomplete and syntax highlighting, built on HTTPie and prompt_toolkit. GitHub: https://github.com/eliangcs/http-prompt 35) speedtest-cli Command line interface for testing internet bandwidth using speedtest.net. GitHub: https://github.com/sivel/speedtest-cli 📣IssueHunt This post is curated by that a crowdfunding and sourcing platform for open-source projects. IssueHunt Anyone can fund any issues on GitHub and these money will be distributed to maintainers and contributors. https://issuehunt.io/ 36) Pattern Pattern is a web mining module for Python. It has tools for Data Mining, Natural Language Processing, Machine Learning, and Network Analysis. GitHub: https://github.com/clips/pattern 37) Gooey (Beta) Turn (almost) any Python 2 or 3 Console Program into a GUI application with one line. GitHub: https://github.com/chriskiehl/Gooey 38) Wagtail CMS Wagtail is a content management system built on Django. It’s focused on user experience, and offers precise control for designers and developers. GitHub: https://github.com/wagtail/wagtail 39) Bottle Bottle is a fast, simple and lightweight WSGI micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the Python Standard Library. GitHub: https://github.com/bottlepy/bottle 40) Prophet (by Facebook) Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers. GitHub: https://github.com/facebook/prophet 41) Falcon Falcon is a reliable, high-performance Python web framework for building large-scale app backends and microservices. It encourages the REST architectural style, and tries to do as little as possible while remaining highly effective. GitHub: https://github.com/falconry/falcon 42) Mopidy Mopidy is an extensible music server written in Python. Mopidy plays music from local disk, Spotify, SoundCloud, Google Play Music, and more. You edit the playlist from any phone, tablet, or computer using a range of MPD and web clients. GitHub: https://github.com/mopidy/mopidy 43) Hug Hug aims to make developing Python driven APIs as simple as possible, but no simpler. As a result, it drastically simplifies Python API development. GitHub: https://github.com/timothycrosley/hug 44) SymPy A Python library for symbolic mathematics. GitHub: https://github.com/sympy/sympy 45) Dash Dash is a Python framework for building analytical web applications. No JavaScript required. https://github.com/plotly/dash 46) Visdom A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy. GitHub: https://github.com/facebookresearch/visdom 47) LUMINOTH Luminoth is an open source toolkit for . Currently, we support object detection, but we are aiming for much more. It is built in Python, using TensorFlow and Sonnet. computer vision GitHub: https://github.com/tryolabs/luminoth 48) Pygame Pygame is a cross-platfrom library designed to make it easy to write multimedia software, such as games, in Python. GitHub: https://github.com/pygame/pygame 49) Requests Requests is a Python Library that lets you send HTTP/1.1 requests, add headers, form data, multipart files, and parameters with simple Python dictionaries. It also lets you access the response data in the same way. GitHub: https://github.com/requests/requests 50) Statsmodels Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. GitHub: https://github.com/statsmodels/statsmodels This post is curated by that a crowdfunding and sourcing platform for open-source projects. IssueHunt Anyone can fund any issues on GitHub and these money will be distributed to maintainers and contributors. https://issuehunt.io/

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