The trade volume of Reddit NFT (nonfungible token) avatars has surpassed US$1.5 million, as per reports from Polygon and Dune Analytics. This surge accounts for more than one-third of the collection’s total trading volume of US$4.1 million since its inception. At the same time, 3,780 digital collectibles reached a new all-time high in the daily sales volume of Reddit NFTs.
Reddit avatars are designed by independent artists, and creators from popular creative subreddit communities and are minted on the Polygon blockchain. Such collectibles are available for purchase on Vault, Reddit’s cryptocurrency wallet. These avatars can be shown as profile images by Reddit users.
The NFTs can be purchased and traded on secondary marketplaces like OpenSea after being purchased. OpenSea provides cross-chain operability, enabling them to be bridged onto Ethereum, Klaytn, and Solana.
Some of the avatars were premium NFTs, which users bought from the website for fixed prices starting at US$10 to US$100 each. According to Reddit Floor figures, approximately 86,000 NFTs were sold to users, giving them a market cap of about US$100 million as of right now.
A few collections have little to no bids, while others have floor values above US$2,000 in certain collections. A Spooky Season avatar created by anonymous artist poieeeyeee that sold for 18 ETH, was the most expensive Reddit NFT ever.
Many more Reddit NFTs, on the other hand, were given out or airdropped for free to some of the site’s users who had earned an especially high level of karma, across over 100,000 active communities (or subreddits), introducing many individuals to NFTs for the first time. Some of these NFTs include the Meme Team, The Singularity, Aww Friends, and Drip Squad collections.
Many others were caught off guard by the news. This is because NFT subreddit on the social network had long been a medium inundated with either hate for NFTs or rants about malicious NFT frauds.
So how did Reddit pave the way for the mass adoption of NFTs?
Industry experts believe that Reddit’s deliberate decision to refrain from using any forward-looking keywords like “NFTs” or “Crypto” is to be credited for helping new technologies become more widely accepted. Under the pretense of “Collectible Avatars,” an expansion of their already-existing function of “Avatar Builder,” it was able to offer what are technically NFTs.
The “Avatar Builder” feature, which allowed users to customize their look on the network, was introduced by Reddit in 2020. Redditors could build an avatar from a selection of endless accessories, clothing, and hairstyles. The introduction enhanced the Reddit Premium program by making unique accessories only accessible to those who were a member of it. Both the profile page and profile card for the user displayed the avatar. To further encourage the adoption of NFT avatars, Reddit teamed with Netflix, Riot Games, and the Australian Football League (AFL) to provide its users with unique avatars.
It will be interesting to see how Reddit continues to dominate the NFT industry amid the criticism and environmental concerns against NFTs.
Fukuoka, the second-largest port city in Japan and a designated National Special Strategic Zone, has announced its collaboration with Astar Japan Lab to establish itself as a Web3 hub for the country. At the “Myojo Waraku 2022” conference in Fukuoka, the partnership was unveiled by Sota Watanabe, the founder of Astar Network, and Soichior Takashima, the mayor of the city.
Together, both organizations will be able to develop new use cases for Web3 technologies thanks to the Astar Japan Labs relationship. To date, Astar Labs has partnered with more than 45 businesses to realize its vision. Microsoft Japan, Amazon Japan, Dentsu, Hakuhodo, MUFG, SoftBank, Accenture Japan, and PwC Japan are some of these companies.
With the help of the relationship with Astar Japan Lab, Fukuoka City is now well on its way to achieving its goals of becoming the hub for Web3 in Japan and luring enterprises with a worldwide focus. According to Astar Network, Web3 education will be made available to Fukuoka City as part of the collaboration. With Astar Japan Lab, they will collaborate with the city to explore new opportunities for local firms to expand globally. The municipal government aspires to make Fukuoka the Silicon Valley of Japan by encouraging innovation and entrepreneurship.
Sota compared the city’s stance on cryptocurrency to that of international leaders in the field. They highlighted that several American cities, including Miami and New York, have favorable attitudes towards Web3 and crypto. Sota continues by saying that the company would collaborate closely with Fukuoka City to draw in more entrepreneurs and developers. Being one of Japan’s four Global Startup towns, Fukuoka will foster collaboration among local citizens, entrepreneurs, and developers by introducing them to the Astar Network and its ambassadors and community.
Astar Network simplifies the creation of dApps using EVM and WASM smart contracts and provides developers with real interoperability via cross-consensus messaging. With its distinctive Build2Earn model, developers are given the ability to receive payment for the code they write and the dApps they create via a staking mechanism.
With the backing of all major exchanges and tier 1 VCs, Astar’s dynamic ecosystem has emerged as Polkadot’s leading Parachain globally. Top TVL dApps can access an incubator hub from Astar SpaceLabs to help their growth on the Polkadot and Kusama Networks.
Astar Network is the preferred blockchain network for Japanese entrepreneurs and businesses, as well as foreign companies wishing to enter the Japanese market. After taking a poll, the Japanese Blockchain Association named it the most well-liked blockchain in the country.
While online dating apps are great for meeting new people, receiving inappropriate photos may be a frustrating experience for users. In order to counteract this, Bumble has been shielding its users from vulgar images since 2019. The feature, known as Private Detector, examines photos supplied by matches to see whether they include objectionable material. Although it was primarily intended to detect unwanted nude photographs, it can also flag bare-chested selfies, which are also prohibited on Bumble. The software will obscure the unwanted photo when a match is made, giving you the option of seeing it, blocking it, or reporting the sender.
Bumble claims that Private Detector is trained using extremely large datasets, with the negative samples—those devoid of any obscene content—carefully chosen to better depict edge situations and other portions of the human body (such as the legs and arms) in order to avoid flagging them as abusive. Adding samples to the training dataset iteratively to replicate the behavior of real users or test misclassification proved to be a fruitful exercise that the dating app used throughout the years in all of the subsequent machine learning projects. Nothing precludes data scientists from potentially establishing new concepts (or labels), to potentially merge them back immediately before the actual training epochs, even if the downstream goal is phrased as a binary classification issue.
Bumble’s Data Science team has now published a white paper that explains the technology behind Private Detector and made an open-source version of it available on GitHub for commercial usage, distribution, and modification. In order to make the internet a safer place, the dating app anticipates that the feature will be embraced by the larger IT community. Bumble hopes that by making Private Detector open source, other digital firms will modify it and add their own features to increase online safety and accountability in the battle against abuse and harassment.
According to Bumble’s VP of member safety, Rachel Haas, “Open-sourcing this feature is about remaining firm in our conviction that everyone deserves healthy and equitable relationships, respectful interactions, and kind connections online.”
In recent years, Bumble has waged a campaign against cyberflashing in both the UK and the United States. Whitney Wolfe Herd, the CEO and founder of the app, contributed to the approval of HB 2789, a law in Texas that makes posting non-consensual nude photographs against the law. Since then, the dating app has aided in the passage of legislation like this in Virginia (SB 493) and California (SB 53).
Bumble has been pushing for the criminalization of cyberflashing in England and Wales, and the government stated in March 2022 that it would do so under the proposed rules, with offenders facing up to two years in jail.
These new regulations are the first step toward ensuring accountability and repercussions for this common kind of harassment that leaves victims—mostly women—feeling upset, violated, and vulnerable online.
Meta shares continued to plunge to 19% in extended trading Wednesday after Facebook’s parent issued a forecast for the fourth quarter, which came up well short of expectations for earnings from Wall Street.
Meta is battling a broad slowdown in online ad spending, problems from Apple’s iOS privacy update, and increasing competition from TikTok. Meta has produced consecutive quarters of revenue declines and is expected to post its third straight drop in the fourth quarter.
The company said revenue for the fourth quarter would be $30 billion to $32.5 billion. Analysts were expecting sales of $32.2 billion. While revenue fell 4% in the third quarter, Meta’s costs and expenses rose 19% year-over-year to $22.1 billion. Operating income declined 46% from the previous year to $5.66 billion.
Meta’s operating margin, or the profits left after accounting for costs to run the business, sank to 20% from 36% a year earlier. Overall net income was down 52% to $4.4 billion in the third quarter. At an after-hours level of about $108, Meta is trading at its lowest since March 2016, eight months before Donald Trump’s presidential election.
Revenue in the Reality Labs unit, which houses the company’s virtual reality headsets and its futuristic metaverse business, fell by almost half from a year earlier to $285 million. Its loss widened to $3.67 billion from $2.63 billion in the same quarter last year. Reality Labs has lost $9.4 billion this year, and there’s no end in immediate sight.
Meta said it is holding some teams flat in headcount, shrinking others, and investing headcount growth only in our highest priorities. As a result, Meta expects headcount at the end of 2023 will be approximately in line with third-quarter 2022 levels.
Python is becoming one of the top programming languages providing all kinds of libraries and modules to perform various tasks, including complex numerical computations with multi-dimensional data, data visualization and analysis, machine learning, and deep learning. Deep learning is a subdomain of machine learning and artificial intelligence that imitates the process of gaining knowledge as the human brain does. It is an important element of data science, including statistics and predictive modeling. A variety of deep learning libraries have been developed that offer simple tools and commands to upload data and effectively train the models, assisting users in developing and deploying deep learning models. Here is a list of top deep learning libraries in Python that will help to get accurate and intuitive predictions in deep learning models.
1. TensorFlow
TensorFlow is one of the best deep learning libraries for high-performance numerical computation. It is an open-sourced end-to-end platform and library, first released in 2015 by the Google Brain team providing a wide range of flexible tools, libraries, and community resources. TensorFlow specializes in differential programming, meaning the library can automatically compute a function’s derivatives. It can be a great tool for beginners and professionals in building deep learning and machine learning models because of its abstraction capabilities. The main features of TensorFlow is its architecture and framework flexibility, management of deep neural networks, and capability to run on a variety of computational platforms like CPU and GPU using Tensors. Tensors are containers that can store multi-dimensional data arrays and their linear operations. Thereby, TensorFlow works best on the tensor processing unit (TPU). In addition to training and inference of deep neural networks, TensorFlow can also be used for reinforcement learning and model visualization.
2. Keras
Keras is a notable deep learning library that provides an interface for deep learning and allows rapid deep neural network testing. It supports high-level neural network API, written in Python, and can run on top of TensorFlow, Theano, and CNTK. The library was developed by Francois Chollet that provides tools to build models, visualize graphs, and analyze datasets. Keras is preferred over other deep learning libraries because it is modular, extensible, and flexible. In addition, Keras can work with the widest range of data types, including arrays, text, and images. The library is user-friendly, integrates objectives, layers, optimizers, and activation functions. Another specialty of Keras is it adopts principles of progressive disclosure of complexity, reducing complexity by introducing information and function at increment levels. Keras is powerful enough to provide industry-strength performance and used by organizations like NASA, Microsoft Research, Netflix, and YouTube. The library has use cases for building sequence-based and graph-based networks, fast and efficient prototyping, data modeling, and visualization.
3. PyTorch
PyTorch is an open-source optimized tensor library for deep learning based on the Torch library, a deep library framework written in Lua programming language. It was created in 2016 by Meta’s AI research team and is now part of the Linux Foundation Umbrella. The library provides two high-level features, which are Tensor computing with strong acceleration via GPU and deep neural networks built on a tape-based automatic differentiation system. Also, with the help of the Torch distributed backend, PyTorch enables scalable distributed training and performance optimization in research and production. PyTorch is written in Python, CUDA, and C/C++ and has the support of libraries or packages used in these programming languages for processing. It provides high flexibility because of its hybrid front-end and allows users to write neural network layers quickly with its deep integration with Python. PyTorch is primarily used in computer vision applications and natural language processing and is one of the most popular deep learning libraries in the industry that companies like Facebook, Twitter, Tesla, Uber, and Google use to build deep learning software. A few deep learning software built on top of PyTorch are Tesla autopilot, Uber’s Pyro, Hugging face’s transformer, PyTorch lighting, and catalyst.
Microsoft CNTK, or the Microsoft cognitive toolkit, is a unified open-source deep learning toolkit for commercial-grade distributed deep learning. Microsoft CNTK describes neural networks as a series of computational steps via a directed graph. Microsoft Research developed CNTK in 2016, having highly optimized built-in components capable of handling multi-dimensional dense or sparse data from Python, C++, or BrainScript (its own model description language). CNTK supports interfaces in Python and C++ and can be used for handwriting, speech, and facial recognition. This popular deep learning library is known for its speed and efficiency due to CNTK’s capability to scale models in production using GPUs. Also, CNTK applies stochastic gradient descent and error backpropagation with automatic differentiation and parallelization across multiple GPUs and servers. It enables users to combine different deep learning models, such as feed-forward deep neural networks, convolutional neural networks, and recurrent neural networks. CNTK is one of the first deep learning libraries to support the open neural network exchange (ONNX), an open format built to represent machine learning models. ONNX gives the power to move machine learning or deep learning models between CNTK, Caffe2, MXNet, and PyTorch frameworks.
5. MXNet
MXNet is one of the most flexible and efficient deep learning libraries in Python developed by Apache Software Foundation and Carlos Guestin, professor of computer science at Stanford University. It is an open-source deep learning software framework designed to train and deploy deep neural networks. The interesting thing about MXNet is that it supports flexible programming models with many languages for binding, including C++, Python, R, JavaScript, Scala, and more. It is a highly scalable library compared to other deep learning libraries providing fast model training and distributed computing, allowing it to train networks across multiple CPU/GPU machines. Also, distribution training works in cloud platforms like AWS, Azure, and YARN clusters. MXNet has a dynamic dependency scheduler that automatically parallelizes symbolic and imperative operations programming to maximize efficiency and productivity.
6. Caffe
Caffe (convolutional architecture for fast feature embedding) is an open-source deep learning framework built for expression, speed, and modularity. Initially, the idea of Caffe was created by Yangqing Jia during his Ph.D. at UC Berkeley and further developed by Berkeley AI Research (BAIR) and other community contributors, and is currently hosted on GitHub. The library is written in C++ with a Python interface. It supports different deep learning models, including convolutional neural networks (CNN), region-based CNN aka RCNN, long short-term memory (LSTM) networks, and fully connected neural networks. Caffe supports GPU and CPU-based acceleration computational kernel libraries like NVIDIA, cuDNN, and IntelMLK that allow faster and high-performance computing, for which the library can process over 60M images per day only with a single NVIDIA K40 GPU. It is one of the most popular Python libraries for deep learning, with its exceptional architecture encouraging applications and innovations, extensible code scripts, and the backend community on GitHub. Caffe is mainly used for implementing image detection and classification, academic research projects, startup prototypes, and large-scale industrial applications in computer vision, speech, and multimedia. Although Facebook announced Caffe2, which is based on Caffe in 2017, enabling simple and flexible construction of deep learning models in addition to recurrent neural networks (RNN), Caffe is still in use mainly for academic purposes.
Theano is one of the popular numerical computation Python libraries for deep learning. It was developed by Montreal Institute for Learning Algorithms (MILA) at the University of Montreal in 2007, and its name “Theano” is associated with the incident philosopher Theano, the first known women mathematician who worked in the development of the golden mean. Theano is an open-source project, and its computations are expressed using NumPy-esque syntax and compiled to run either on CPU or GPU-based architectures. The library is written in Python and centers around NVIDIA CUDA, which allows users to integrate it with GPS and provides for defining, optimizing, and evaluating mathematical operations involving multi-dimensional arrays and matrix calculations. Theano has various features, including tight integration with NumPy, the transparent use of a GPU, effective symbolic differentiation, speed and stable optimization, dynamic C code generation, and extensive unit testing and self-verification. It is used extensively for deep learning projects and research due to its high-performance data-intensive calculations.
8. Deeplearning4j
Deeplearning4j (DL4J), short for Eclipse Deeplearning4j is an open-source distributed deep learning library that consists of a set of tools for running and building deep learning models on Java virtual machine (JVM). It was developed by Konduit.AI and a combined effort of a machine learning group including Adam Gibson, Alex D. Black, Vyacheslav Kokorin, and Josh Patterson. In 2017, Skymind, a San Franciso-based business intelligence and enterprise software firm, joined the Eclipse Foundation and updated DL4J to integrate it with Hadoop and Apache Spark. Among other deep learning libraries in Python, only DL4J allows training models in Java while interoperating with the Python ecosystem via a mix of Python executions of CPython bindings, model import support, and interoperability of other runtimes like TensorFlow-java and onnxruntime. It is written in Java, C++, C, CUDA and supports many neural networks, including CNN, RNN, and LSTM. The use cases of DL4J are many, from importing to retraining models of PyTorch, TensorFlow, and Keras and deploying these models in JVM microservice environments, mobile devices, IoT, and Apache Spark. Also, DL4J provides toolkits for vector space and topic modeling designed to handle large text sets and use them in natural language processing.
There are other deep learning libraries, including Lasagne, Chainer, Glucon, and more which did not made it to this list but perform efficiently.
RK, the Turkish Competition Authority, which is a government agency regulating competitive market processes, fined Meta Platforms for violating the country’s competition law. The fine is 346.72 million liras, equal to 18.63 million US dollars.
According to RK, combining the data collected from Facebook, Instagram, and WhatsApp services caused the deterioration of competition which made it difficult for Meta’s competitors operating in online display advertising markets by creating barriers to enter the market.
The RK said that Meta breached the Turkish competition law’s article 6. However, Meta can appeal the decision at the Administrative Court of Ankara within next 60 days. A company spokesperson said that Meta has disputed the decision of Turkey’s RK.
The spokesperson said Meta Platforms strive to protect user privacy and give people transparency and control over their personal data. They further added that the company will consider all options.
Following a new privacy agreement in 2021 that asks WhatsApp users to share data with Facebook, Turkey launched an inverstigation into WhatsApp and Facebook. Meta had to put a stop to the new privacy agreement in light of the reactions and the investigation.
Turkey also occasionally fines social media giants, including Meta platforms, for defying the regulations and laws that aim to increase government controls. Social media platforms were focused on in Turkey after the government passed a new disinformation law on October 14 that will maximize the control of Turkish government on social media platforms.
Blockchain is a distributed, decentralized ledger (or database) used to store information electronically in a list of ordered records known as “blocks.” These blocks are shared across multiple computers via cryptography. Each block contains a cryptograph hash (a function that converts input data into fixed-size output), timestamp, and transaction data related to the previous block. The chain of blocks records transactions securely and protects against changes or alterations. Blockchain technology is gaining popularity in real estate, insurance, E-voting, government benefits, artist royalties, etc.
Free Blockchain Courses
There are several resources available using which you can learn more about the technology. Some potent and free Blockchain Courses have been mentioned below.
Blockchain Basics by Great Learning
Blockchain Basics is a beginner-level course developed by Great learning that aims to empower newcomers with robust blockchain fundamentals. Initially, you will learn about essential concepts like cryptography, consensus mechanism, transaction mechanisms, etc. The course also features a blockchain ecosystem and its adoption process in industries. The course comprises real-world examples to make it more realistic and practical for the students.
After understanding the basics, if you wish to advance your skills in Blockchain and IoT, you can check out a comprehensive course on Advanced Software Engineering for Blockchain hosted by IIT Madras in collaboration with Great Learning.
YouTube is an excellent source for learning without paying vast sums of money. Even when it comes to learning such complex technological topics as blockchain, there are several short courses that you can find on YouTube. This YouTube video provides 3-4 hours of accessible course material discussing blockchain’s basics. The entire video tutorial is divided into three parts; the first describes blockchain, the second talks about the applications, and the third shows how it works. You will also learn about NFTs (non-fungible tokens), Web3 (a new iteration of WWW or the World Wide Web), smart contracts in Ethereum, and the blockchain metaverse.
After gaining a basic understanding of blockchain, you can proceed with more advanced courses that give you a real-world perspective on the technology. This free crash course will acquaint you with blockchain’s impact on your business with real-world examples from famous corporate practitioners’ interviews. The course’s primary objective is to empower entrepreneurs with all the necessary learning materials and resources to capitalize on business opportunities.
It will be an excellent place to start your blockchain journey if you want to gain valuable knowledge and revolutionize your business ecosystem.
Decentralized cryptocurrencies are gaining popularity lately, and most of it is credited to the use of blockchain. Blockchain technology features distribution and decentralization capabilities extensively applied in the cryptocurrency market. In this free online course, you will learn about the fundamental concepts of blockchain and its applications in Bitcoin. The course initially talks about the factors that reason in favor of blockchain technology. It discusses the benefits of Bitcoin as a cryptocurrency, its requisites, exchanges, wallets, and much more. The course provides a detailed explanation of the inner workings and guiding principles.
If you are interested in cryptocurrencies, you can start your journey by gaining a valuable understanding of what they are and how they work.
This free online course on edX is specially designed for developers who want to learn about blockchain technology. It is developed at Berkeley in conjunction with experts from the Computer Science Department. In this course, you will learn about blockchain foundations with a mathematical approach. It covers topics like the CAP theorem, the Byzantine Generals problem, and many other mathematical concepts. The course also provides an overview of Bitcoin and its application of Blockchain. Toward the end of this course, you will learn about several enterprise-level implementations by companies like JP Morgan, Tendermint, and HyperLedger.
Introduction to Digital Currencies and Blockchain MOOC
This free blockchain program by the University of Nicosia is the first M.Sc course in digital currencies and blockchain technology. The MOOC (massive open online course) is taught by experts like Andreas Antonopoulos, Antonis Polemitis, and George Giaglis. It is a great place to start if you are interested in learning the technical overview of decentralized digital currencies like Bitcoin. Students will get hands-on experience with how blockchain works in the provision of financial services. Additionally, they will get credits for clearing mandatory graded activities along with the concluding essay-based examination.
One of the most popular blockchain applications is Ethereum technology, a computing platform where developers can create and deploy applications decentrally. This course is developed at Berkeley under the guidance of leaders from Blockchain startups like Consensys, Virtue Poker, and BlockApps. The free course will teach Blockchain by helping you create a Hello World Blockchain Application within four modules.
The course is ideal for developers interested in DApps (decentralized apps) and seeking in-depth knowledge of the process. College students, practicing developers, or individuals interested in solidity concepts should try this free blockchain course.
Blockchain and FinTech: Basics, Applications, and Limitations.
Blockchain technology grew in tandem with Bitcoin and now forms the core of several other FinTechs. Today, many companies utilize blockchain for multiple applications in finance, logistics, insurance, etc. However, it is not easy to understand how to incorporate the technology. To get a clear picture of applications, this course aims to provide a general overview of the technical details and limitations of applying blockchain across your fintech application. In conclusion, the course will also brief you on the downsides of this technology in providing security against criminal activities. The course was developed and led by Professor Siu Ming Yiu at the University of Hong Kong.
Coursera is an online platform that offers online courses with a vision of providing “”life-transforming” learning experiences. Coursera has a full-fledged course on Blockchain Specialization that gives students a broad idea of essential blockchain concepts. This specialization comprises many sub-courses, some of which are:
Blockchain Basics by Coursera
The University of Buffalo and The State University of New York have developed this course to include hashing techniques and cryptography foundations that form the foundation of blockchain programming. The course begins with defining blockchain and moves forward to Ethereum Blockchain as an application of the technology. You will also learn about the algorithms and techniques behind asymmetric key encryption, hashing, etc.
Blockchain Platforms is the fourth block of the Blockchain Specialization offered by Coursera. The course provides learners with a basic knowledge of a blockchain ecosystem on several platforms. You will learn about two detailed decentralized applications, Augur and Grid+. These applications will use Hyperledger blockchain architectures and service models to analyze the decentralized apps while discussing their privacy and scalability challenges. This course will thus help you to advance your blockchain knowledge in solving real-world problems. Here is the link to sign up- Blockchain Platforms
Adobe MAX recently announced its approach to developing creator-centric Generative AI offerings using Content Authenticity Initiative standards. The company is also investing in new research to enhance creatives’ control over their work and style.
This transformational technology accelerates how artists brainstorm and explore creative avenues. It enhances the accessibility of creativity. The CAI is Adobe’s initiative, with over 800 partners working to increase trust online. CAI’s open-source technology securely lets creators attach provenance data to digital content.
According to Adobe researchers, Generative AI is a hyper-competent creative assistant that can multiply what creators can achieve by presenting alternative approaches and new images without losing the essence of human imagination. Adobe is taking a step by investing its research and product design talent to formulate an approach that revolves around the needs of creatives by incorporating Generative AI in Adobe creative tools.
The research is still at a nascent stage. According to Adobe, AI within Photoshops generates rich, editable PSDs. The AI can generate a plethora of approaches, and the creator can pick two or three they want to explore using Photoshop’s wide selection of tools to transform the AI-generated image based on the artist’s creative perspective.
Generative AI in Adobe Express will aid inexperienced creators in achieving their unique goals. For example, rather than finding a premade template to start a project, the users could generate a template through a prompt and leverage Generative AI to put an object on the scene or create a unique test effect based on their description.
The artist will still have complete control. They can use Adobe Express tools to edit images, change solos, and add fonts to create the flyer, poster, or social media post they imagine.
Facial recognition has become a part of our daily life in mobile phones, computers, biometrics, and more, providing a sense of personal security. Computer vision is the new age of technology that powers facial recognition and sometimes outperforms humans in the facial recognition solution of face detection, analysis, and recognition. The algorithms use computer vision techniques to map, examine, and verify to identify a face in a picture or a video. Thereby, we rely on facial recognition along with biometrics greatly for information security, access control, and surveillance systems. According to Allied Market Research, the global facial recognition market has been increasing since the COVID-19 pandemic and is predicted to reach $16.74 billion by 2030 at CAGR of 16.0%. This will lead to significant advancements in computer vision, particularly for facial recognition, and the idea to pursue a profession in computer vision is a good idea, or learn computer vision out of curiosity. You can practice model building using the listed facial recognition datasets to get started.
1. Flickr Faces HQ (FFHQ) Dataset
Flickr Faces HQ dataset is a high-quality image dataset of human faces created in 2019 as a benchmark for generative adversarial networks (GAN) in the research paper “A Style-Based Generator Architecture for Generative Adversarial Networks” by Tero Karras, Samuli Laine, and Timo Aila. This facial recognition dataset comprises 70,000 high-quality PNG images at 1024×1024 resolution and has age, ethnicity, and image background variations. The images collected in this dataset are crawled from Flickr, an American image hosting and video hosting service. To note, under NVIDIA Research Licensing, the dataset is not intended to be used in any development or improvement of facial recognition projects and technologies.
Tufts Face dataset is a comprehensive and large-scale facial recognition dataset containing over 10,000 images and have seven image modalities, including visible, near-infrared, thermal, computerized sketch, LYTRO, recorded video, and 3D images. The Tufts Face dataset collected images from more than 15 countries, of which 74 are females, 38 are males and an age range from 4 to 70 years. The dataset was created in 2019 and made available to researchers worldwide to use for non-commercial and educational purposes benchmarking facial recognition algorithms such as sketches, thermal, NIR (near-infrared), 3D face recognition, and heterogamous face recognition.
The Real and Fake Face Detection dataset is a face dataset created by Computer Intelligence and Photography Lab at Yonsei University in 2019. The dataset is well known for its high-quality photoshopped fake face images generated by experts. The collection of real and fake face images are put in separate files under the parent directory as training_real and training_fake files and contains around 1000+ real face images and 900 fake face images. The images in the Real and Fake Face Detection dataset vary for different face sizes and the features of the eyes, nose, mouth, and whole face. Also, the fake face images have a label for recognizable difficulty ranging from easy, mid, and hard.
Multi-Attribute Labelled Faces dataset is the first face dataset supporting fine-grain evaluation of face detection in the wild. The dataset contains 5,250 images with 11,931 labelled faces collected from the internet and introduced in 2015 in the paper “Fine-grained Evaluation on Face Detection in the Wild” by Zhen Lei, Bin Yang, Junjie Yan, and Stan Z.Li at the Chinese Academy of Sciences. The dataset has two main features that the annotations or labels of multiple facial attributes make it possible for fine-grained performance analysis, and it reveals the true performance of algorithms in practice.
Wider Face dataset is one of the biggest large-scale face detection benchmark datasets containing rich annotations, including poses, event categories, face bounding boxes, and more. The dataset was created in 2018 by the Multimedia laboratory at the Chinese University of Hong Kong. The Wider face dataset contains 32,203 images and labels 393,703 faces with scale, pose, and occlusion variety. Additionally, the dataset follows an event class-based organization with 61 event classes, and for each event class, random sets are selected in the ratio of 40%/10%/50% for training, validation, and testing.
6. Face Detection Dataset and Benchmark (FDDB) Dataset
Face Detection Dataset and Benchmark dataset is a facial recognition dataset designed to study the problem of unconstrained face detection. The dataset was created by the Department of computer science at the University of Massachusetts Amherst and introduced in the paper, “FDDB: A Benchmark for Face Detection in Unconstrained Settings.” This dataset contains 2845 images from the Labelled Faces in the Wild dataset, with annotations for 5171 faces. FDDB can be challenging to work with as it includes difficult pose angles, out-of-focus faces, low-resolution images as the resolutions of images varies greatly, and greyscale and color images.
Google Facial Expression Comparison dataset is a facial recognition dataset created and introduced by Raviteja Vemulapalli and Aseem Agarwala, Research scientists at Google. This is a large-scale facial expression dataset with face image triplets and human annotations specifying which two faces show the most similar expression. It contains more than 156k face images with 500k triplets. The dataset is intended for researchers who are interested in facial expression analysis, including emotion classification, expression synthesis, and more expression-based analysis. It was published in 2018 and focuses primarily on discrete emotion classification or action unit detection than plane old face expression datasets.
8. Face Images with Marked Landmark Points Dataset
Face Images with Marked Landmark Points dataset is a facial recognition dataset containing 7049 images with up to 15 key points. This dataset is a primary face dataset that can be used as a building block in various face recognition projects like tracking faces in images and videos, detecting dysmorphic facial signs for medical diagnosis and biometrics, and so on. The dataset was published in 2018 by Kaggle and was provided by Dr. Yoshua Bengio of the University of Montreal.
Labelled Faced in the Wild dataset is one of the most popular facial recognition datasets. The dataset is a public benchmark for face verification, also called pair matching, containing web images of 13233 images of 5749 people and 1680 people with two or more images. LFW was published in 2018 by the University of Massachusetts and was designed to study the problem of unconstrained face recognition. This dataset provides information for supervised learning with image-restricted and unrestricted training modules. Also, the modeling results using the LFW dataset are promising. To date, 123 models have been applied to the dataset, and the results have been publicly released on the website.
YouTube Faces with facial key points is a processed version of the YouTube Faces Dataset, a collection of short videos of celebrities downloaded from YouTube. The dataset comprises around 1293 videos with up to 240 frames for each video and 155,560 single image frames. It was created and uploaded by Dr. Guillermo on Kaggle, the inspiration came from the Face Images with Marked Landmark Points and was intended for facial recognition across videos. The dataset can be used for test transfer learning between other face datasets, other face recognition projects like animal face detection, and many more.
CelebFaces Attributes dataset is a large-scale face attributes dataset having more than 200k+ celebrity images each with 40 attributes. The diversity of images in CelebA is vast, the dataset comprises 10,177 identities and five landmark locations, and rich annotations. CelebA can be used in many facial recognition projects, including face classification, face detection, face editing and synthesis, face localization, and so on. The dataset was released in 2015 by Multimedia Lab at the Chinese University of Hong Kong for non-commercial research purposes. The CelebA dataset was used in the paper “Deep Learning Face Attributes in the Wild,” which may provide more insight into the dataset.
The iQIYI-VID dataset is the largest in size among this list of facial recognition datasets due to the presence of face videos. The face videos make it unique and challenging to handle compared to other facial recognition datasets. It is a large-scale dataset for multi-modal person identification comprising 600k videos of 5,000 celebrities collected from the website iQIYI, a Chinese online video platform. All the clips in the dataset pass through a careful human annotation process, and the error rate of labels to be lower than 0.2% is considered a part of it. The dataset was introduced in the paper “iQIYI-VID: A Large Dataset for Multi-modal Person Identification” by iQIYI Incorporation. The dataset is not available yet but will soon be public.
In this article, we want to share thoughts about project management homework and how to make it more effective and quick.
Before we start, let’s discuss the main components of project management’s (PM) responsibilities. The PM should use the existing resources to lead the client’s ideas to successful implementation. To make it real, the manager should make a plan, organize a team of specialists, structure the work process, create feedback loops between the team and a client, and react to all moments that negatively influence the project implementation.
Your project management course may contain assignments that are directly related to your future profession. For example, students are typically asked to compose project documents, risk analysis, create project plans, and more. If it is difficult to do a project management assignment, you can always ask for project management homework help. When you get expert help, you will have the possibility to clarify all points that you can’t understand and implement new knowledge in new projects.
So, is it hard to do your project management homework tasks independently? The answer is yes and no. Here are the tips that can help you while doing project management assignments.
9 Tips to Do Project Management Homework with Ease
1. Make up your workspace.
First things first, take care of the environment you will be working in. The area should be silent and clean to boost your concentration and performance. Ask your roommates, partner, or family members to keep quiet. Make sure that your table contains only important things like a laptop, notes, and a pencil. Besides, you can take some snacks and a cup of drink to avoid heading to the kitchen to find something tasty. If you want to do the project homework faster, you will need to focus totally on the work.
2. Read the assignment carefully.
A project manager’s good quality is taking notes of the details, paying attention, and concentrating on the task. One assignment may ask you to draw Gants and PERT charts, create a report, make a feasibility study, or just answer a list of questions. What does the teacher want to see in your homework? What are the key elements? Do you have all the needed information? What stages do you need to overcome? Write down the list of required actions to do.
3. Come up with the idea.
Typically, teachers provide you with a case study or a project concept that you need to carefully read and imagine yourself in the role of project manager. A project management process is tied directly to major points of what should be done. If your homework is to create a project plan, you need to define the project scope, key objectives, and a team of executors. Make a list of questions you need to ask the high management to clarify some moments.
4. Read out the instructions.
If you need to do your project in a specific program, make sure that you follow your teacher’s guidelines or specific instructions. Also, guidelines are very helpful when you need to create a more solid project management assignment or report. A step-by-step guide will help you not to lose track of your work and organize your essay in a logical structure.
5. Plan your time.
You may need sufficient time to complete a list of project management tasks and solve managerial problems. Studies will take more or less time and depend on your abilities and the scope of the problem. Make sure that you have left enough time before the deadline to do your homework on time.
6. Conduct research.
Look through your textbook and class notes for more information on the project. Find out whether your teacher has given you a list of recommended sources where you can find information about project management methodologies and project management tools. You may need to find additional information, statistics, and other data to make your project management homework. Find supporting evidence and facts to make your work look credible and professional. Find journals and blogs related to project management and look through current information on management issues and tools. Make sure that the found sources are credible.
7. Ask someone for help.
If you can’t handle the project management assignment, you can speak to your teacher or other students involved in similar projects for clarification. Consider the fact that your teacher won’t give you direct recommendations or point out your mistakes. You will get general tips or a reminder of what you have already learned in class. If you need more clarifications, you can search for online help with project management homework and ask experts to assist you with specific project management problems. It’s a quick way to get prompt and complete answers to all your tasks and questions. When you see how something should be done, you can use your knowledge in future projects.
8. Properly organize your work.
Sometimes the assignment may require you to format the text into tables, diagrams, decision trees, or management reports in the appropriate format. Moreover, the information may be presented on video or digital media.
Make calculations if needed. For example, you may be asked to calculate probability in %, estimate the cost for each task, KPI indexes, project costs, and other key numbers. Don’t forget to support your numbers with explanations. Check whether you need to create a title page, table of contents, and works cited list.
9. Revise and finalize your work.
When you have finished all calculations and comments, look it through. Fix all grammar and punctuation mistakes, and check whether your calculations are correct. A good project manager should be attentive to details and always ensure that mistakes are eliminated. Moreover, such a checkup will help you find the gaps in your research or find something that may be improved or changed.
Summary
Project management homework may be challenging, especially when you need to create a big multi-level project. All the skills you will get after completing numerous project management assignments will help you understand the project life cycle, be able to plan projects, execute the project, and carry out project evaluations.
