How Big Data Can Drive Mobile Applications

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The concept of smart cities can only be brought to life by forming an integrated, intelligent, and interdependent network of a few core smart city technologies.
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When people think of smart city technologies, the words they’re most likely to think of are ‘IoT’ and ‘sensors’. And this association is not unwarranted or unfounded, as by far the biggest enabler of smart cities will be their intricate network of interlinked sensors collecting all kinds of data from citizens, infrastructure, and pretty much everything else. However, while advanced sensor technology will play a critical role in empowering smart cities, a smart city is reliant on many more technologies other than sensors. These data-driven technologies function together in order to optimize resource utilization, enable convenience for citizens, and provide greater visibility and control to governments. Following are the different technologies that will form the core of smart city infrastructures:

1.  The Internet of Things
All the other technologies in this list have always been part -- albeit in more rudimentary forms -- of government operations. However, none is as much associated with the concept of smart cities as IoT is. That’s because smart cities, and also smart governance for that matter, rely on the collection, analysis, and processing of large volumes of granular, real-time data, which has only become possible with the help of IoT sensors. IoT sensors and cameras can constantly collect detailed information in various forms and in real-time. Data like the footfall at a railway station, traffic on a road, contamination levels in a water source, and the energy consumption in a residential block can be collected in real-time using different types of IoT sensors. By using this data, government agencies can make quick decisions pertaining to the allocation of different resources and assets. For instance, based on the footfall and ticket sales information from railway stations, transport agencies can reroute their trains to cater to the varying demand. Similarly, health, safety, and environmental agencies can monitor the contamination levels of water bodies and notify the people responsible to take remedial action. In certain cases, IoT actuators can automatically initiate responsive actions in exigent situations, such as stopping the supply of contaminated water to domestic households.
Thus, IoT networks and sensors will essentially form the nervous system of smart cities, delivering critical information to the controlling entities, and relaying responsive commands to the appropriate endpoints.
2.  Big Data Analytics
All aspects of smart cities will be primarily driven by data. All the decisions -- from long-term strategic ones like public policy to short-term decisions like evaluating the welfare worthiness of individual citizens -- will be taken through the analysis of pertinent data. With the increased volume, velocity, and variety of data generated thanks to IoT sensors and other advanced data gathering methods, the need for high-capacity analytics tools will be greater than ever before. Big data analytics tools are already being used by governments for a wide range of applications, from predicting the likelihood of crime in specific areas of the city to preventing crimes like child trafficking and abuse. As IoT enables the collection of data from a host of new sources, big data analytics will be used in all areas including critical ones like education, healthcare, and transportation. In fact, big data analytics is already an inextricable part of IoT networks, and can potentially be subsumed under IoT in the future. Big data can enable governments to make sense of the data they possess by finding patterns that are reflective of trends within cities. For instance, big data analytics can help education departments to spot trends like low retention rates so that they can prevent such outcomes. Big data can also be used to find the causes of such issues and plan remedial actions. Thus, big data will become a critical decision-making aid to smart city governments.
3.  Artificial Intelligence
Building upon the capabilities of IoT and big data will be artificial intelligence. Artificial intelligence can be used to bolster smart cities’ big data and IoT initiatives by automating smart decision making. In fact, IoT’s ability to initiate responsive action will be largely driven by some or the other form of artificial intelligence. The most obvious application areas for AI in smart cities is automating high-volume and data-intensive tasks like offering basic citizen services in the form of chatbots. For instance, AI-based chatbots can be used to process simple requests like:

providing new water, electricity, and gas connections,
changing property ownership details, and
changing addresses and other registered details on government records.

However, the real value of artificial intelligence can be derived by smart cities by utilizing advanced AI applications like deep learning and computer vision. For instance, computer vision can be used by transportation regulators to analyze traffic footage to identify instances where drivers park illegally. Computer vision can also be used to look out for and flag vehicles that are associated with reported crimes to help law enforcement track down criminals.
Deep reinforcement learning can also be used to automatically optimize resources based on emerging needs in smart cities. With the help of reinforcement learning, governments can make their operations increasingly efficient as these AI systems can get better with experience.
4.  5G Connectivity
Smart cities are founded upon the ability of their different subdivisions to communicate and share information in real time for ensuring complete synchronicity in operations. By achieving this synchronicity, governments can ensure that their citizens receive critical services like healthcare, emergency response, and transportation in a timely manner. As a result, they can not only ensure their citizens’ convenience but also improve their safety and overall wellbeing. For instance, in case of an emergency like an explosion or a fire, enabling real-time communication between the fire department, city ambulance service, and traffic control units can ensure perfect real-time coordination among these entities, potentially keeping casualties to a minimum.
To enable this seamless communication between the different government entities, it is important to have a communications network capable of handling high volumes of communication with low latency and high reliability. By using 5G communication technology, governments can ensure that all government bodies can collaborate seamlessly, despite the volume of data shared every minute.
5.  Augmented Reality
Offering citizens timely services means ensuring that the government employees are given the information they need to perform their tasks effectively. For instance, a physician at a government health centre must be given information on the patients they are working with. Or, workers tasked with fixing a damaged railway line should be given the updated layout of the tracks and the accurate location of the damaged part. By using an AR headset, such information can be relayed to the workers in real-time as soon as they need it. This minimizes the effort and time required by the workers to find the necessary information. As a result, they can initiate action without any delays. AR can also be used by traffic management personnel to get real-time information regarding parking violations and stolen vehicles through smart glasses or smartphone applications. Thus, they can ensure that drivers have a convenient experience during their commutes.
Each of these smart city technologies, as you may have surmised already, are interdependent on each other. Thus, governments should develop an integrated strategy for implementing these technologies in conjunction with each other, taking into account the advantages brought about by each of them.
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Enterprises can leverage video analytics for manufacturing to streamline end-to-end operations right from production to delivery of goods.
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Video surveillance has been around since 1942. The first recorded use of CCTV was to view the launch of V2 rockets in Germany. Since then, the installation of video surveillance cameras to monitor various activities has become common practice across the globe. Surveillance operators are deployed to monitor activities through cameras. But, as the number of cameras per operator increases, their ability to monitor all the cameras decreases. Video content analysis or now commonly referred to as video analytics emerged as a solution to aid these operators.
Video analytics technology enables machines to automatically analyze videos to detect and trigger alarms for any spatial events. It was primarily used for motion detection. But, due to a lack of accuracy and the inability to understand the context in motion detection, earlier versions resulted in frequent false alarms. Incorporation of modern-day technologies like AI, deep learning, and computer vision has enhanced the accuracy of video analytics. Cameras embedded with video analytics are now able to categorize objects in the video based on size, shape, and color among other parameters. These new versions of video analytics embedded cameras are finding applications in almost all industries including manufacturing. By using video analytics for manufacturing, enterprises can enhance operations, worker safety, and asset monitoring.
Developers are amalgamating modern-day digital technologies like AI, CV, and deep learning with video analytics to minimize false alarms and enhance the accuracy of analysis to prevent spatial events.

Enhancing Worker Safety
Manufacturing factories are equipped with large-sized critical machinery for daily operations. Also, the release of hazardous gases and electric damage incidents can cause smoke and fire. This makes worker safety an important concern for manufacturers. Video analytics can help monitor activity in critical areas. It can also trigger alarms for non-compliance with safety measures. For instance, heavy machinery is marked with proximity alert lines, and workers are advised not to cross those lines while machines are functioning. If a person intentionally or unintentionally crosses that alert line, video analytics can trigger an alarm to alert the worker. When combined with AI it can also auto-stop machines to minimize accident risks.
Directions and ways are aligned for moving end-products from factories to warehouses to prevent any inconvenience. But some laborers might take nonoptimal ways to speed up transportation. It might speed up transportation, but it causes inconvenience to other workers. And it can also cause a collision of transportation vehicles with employees. To avoid such incidents, manufacturers can use video analytics to detect wrong-way motion. Developers can embed a route for transportation in video analytics. Whenever any worker takes a different route, video analytics can trigger alarms so that other employees can be aware and try to avoid any collision.
Enabling Asset Security
Video analytics and facial recognition can together detect and alert unauthorized access into work premises. This will not only enhance outdoor security but also indoor security. It will enable only authorized personnel to enter predefined areas in manufacturing facilities. For instance, it will allow only security personnel to enter surveillance rooms. And it can prevent access for any worker other than authorized electricians into electricity or generator rooms.
With facial recognition, video analytics can also prevent theft in factories. It can not only trigger an alarm for trespassers but also when an asset is moved out of place without authorization. When embedded with computer vision, cameras can classify different assets of the manufacturing company based on different parameters like shape, size, and color. If someone is leaving the workplace with a valuable asset, then video analytics tools can help raise alarm on such incidents to prevent theft.
Facilitating Lights-out Manufacturing
Lights-out manufacturing, sometimes also referred to as a dark factory, is the production of goods without the need for any human intervention, and thus without the need for lights. Dark factories are fully automated that can operate without any presence of humans on-premises. Manufacturers are shifting towards the use of lights-out production to leverage 24/7 productions. Almost every manufacturing company uses a conveyor belt production line. But these conveyor belts can face typical processing failures like belt deviation, belt slippage, mistracking, and blockages. When humans are present nearby, they can stop production in case of any malfunction. But, in lights-out manufacturing, when there is no human present, conveyer belt malfunction can result in heavy loss and wastage. Video analytics with advanced AI technology can help prevent such wastage caused due to conveyor belt break down and other similar disruptions. For instance, video analytics and computer vision can accurately detect conveyer belt break down even in dark conditions. AI systems can then automatically switch-off conveyer production belts to reduce wastage.
Assisting Commercial Vehicle Drivers
Video analytics can assist drivers of supply chain vehicles by providing a warning to avoid any casualties. For instance, IntelliVision has developed an advanced driver assistance system, an AI and video analytics tool for assisting drivers. The system can alert drivers to prevent any forward and backward collision, lane departure, and pedestrian collision. It can also monitor drivers and detect road signs. Such video analytics and AI systems can streamline supply chain management for manufacturers.
Deep learning can enable emotional recognition that can make the roads safer for both drivers and pedestrians. Emotions can influence how a driver drives. For instance, anger can influence rage driving, and stress can lead to a lack of focus, which can increase the chances of road casualties and wastage of valuable goods and materials. Emotional recognition systems can detect rage driving or fatigue and alert drivers to avoid any accidents.
Improving Product Quality
Video analytics, with the help of computer vision, can accurately detect even the slightest non-conformities in finished products as they move along the production line. By doing so, it can help in improving the quality of end products. Video analytics can constantly monitor the conveyor belt and detect how each product is produced and assembled. In convergence with AI systems, video analytics can help set aside damaged products from the rest of the products. For instance, in a toy manufacturing company, if a toy is not assembled properly, then video analytics can detect that toy and trigger the AI system. AI systems can then automatically remove the defective toy from the rest to assure that only the best quality products are delivered to retailers and then to the customers.
Leveraging video analytics for manufacturing is just the tip of the iceberg when it comes to the adoption of digital technologies in the industry. Manufacturers have started relying completely on digital technologies like IoT, robotics, and AI. According to a report by Microsoft on manufacturing trends, 58 thousand units of cobots (collaborative robots) were sold in 2018, and 90 thousand were sold in 2019, and it is expected that the number will rise to 150 thousand in 2020. The same report also sheds light on the use of technology, saying that it is helping manufacturers to get closer to end-consumers, enabling them to enhance product iterations and improve customer experience. If you haven't incorporated any technology in your daily operations, this is the time. Although it is never too late to start, it can leave you lagging behind the others. And being left behind is not an option in today's digital world.
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Smart stadiums will reinvent the experience fans have at sporting events by providing them contextual, convenient, and personalized experiences.
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The fan experience at stadiums hasn’t undergone a major change since ages. And the challenges that come with the traditional experience remains. Individuals find it difficult to find directions to empty parking spaces, food stalls, or even have trouble finding their seats. IoT brings real-time coordination between event organizers and individuals to provide a customized and seamless experience to the fans through smart stadiums.
The Future of Sporting Events with Smart Stadiums
IoT devices help stadium networks, and team owners better connect with their fans. They can leverage mobile apps to connect with individuals to provide a personalized experience. With the help of sensors and GPS technology, an app can assist people in finding empty parking spaces quickly. They can also replace physical tickets with digital ones, helping shorten the time usually spent in long queues outside arenas for checking in.

Additionally, with the help of augmented reality, organizers can overlay additional or otherwise missing information regarding various areas of the stadium. It can assist individuals in finding washrooms, food stalls, and the shortest route to their seats. People can even order food on an app and get it delivered without having to move from their seat. Thus, fans can enjoy the game without any interruptions.
Service providers can also utilize digital payments for processing every purchase made by individuals, right from ticket purchasing, parking fees, to food payment.
The Roadmap to Building Smart Stadiums
The deployment costs for IoT are very high, and owners can expect a considerably long period to get their ROI. However, these costs are insignificant when compared to the possible increase in revenue it can bring.
Also, with the changing demographics, people are preferring to stay at home to enjoy sporting events. However, with the customized and engaging experiences that IoT brings, stadium owners can expect an increase in their dedicated fanbase, bringing more people to the stadium.
Similarly, the competition for stadium owners has increased significantly in the past few years due to the availability of high-definition television and content, cheap cable and on-demand subscriptions. Owners can provide dedicated fans discounts on purchases, grant access to behind the scenes footage and unseen locker room footage to attract people.
Networking can also become an issue when you have about fifty thousand people using the stadium’s internet. Owners, therefore, need to invest in a high-speed internet connection to ensure that their services run uninterrupted.
IoT devices are set to reform the sports industry, and smart stadiums are just a part of it. IoT devices also help with team management and improving athletes’ training and safety. The era of smart stadiums has already dawned upon us. Levi’s smart stadium, home to the San Francisco 49ers, has implemented IoT infrastructure that allows its 70,000+ fans to have personalized experience at the stadium. This has caught the attention of other stadium owners, and they too will possibly invest in smart stadiums. Soon, every stadium that you visit will be a smart stadium.
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Technology is a modern tool that has been constantly evolving, and humans need to adapt just as fast in order to keep up.
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While it has transformed every aspect of life, its impact on education is just becoming more apparent in recent years.
True, educators and universities have been tapping into the digital revolution trends and adopting modern technologies for decades now. But recent years proved just how significant this development has become.
From artificial intelligence to augmented reality machine learning to educational software, there are endless possibilities that can usher in a whole digital revolution for most classrooms all over the world. Some schools are already starting.
The Emergence of Digital Learning
One of the growing trends is the introduction of e-learning. Until the early 2000s, formal education had always been conducted inside the classroom. But as the internet took off, the transition began.
Now, the advancement in technology is significant enough that it can bridge the geographical gap through online tools, sharing all kinds of learning materials in a variety of formats, from videos of lectures and slideshows to word documents and PDFs. Live online classes, often in the form of webinars, have also become increasingly common. Professors also give instructions through chat and message forums.
Learning virtually is no longer a novelty. In fact, 43% of students use online platforms as helping tools while they do their homework. But more and more universities are introducing distant learning as one of the options for many students.
Lessons can be provided on different platforms and applications. They can be accessed from any kind of device too, from desktop computers and tablets to and tablets to smartphones.
Other tools and applications like Google Drive and Dropbox make documents easily accessible for students. Most teachers also discovered the power of videos to maximize classroom learning. Students can learn new content through videos, allowing them to absorb the information a lot easier and longer than text-based learning.
The Implications of Evolving Technology for Modern Learning
Unlike many traditional teaching methods, e-learning can better accommodate the unique learning styles of students on a case-to-case basis. There is advanced software that can adapt to each student’s skill level, letting them learn at their own pace. These kinds of software are replacing the roles of textbooks.
It is easy for students to learn a lesson through the help of tailor-made computer programs that can better assist their unique learning needs.
Another interesting development in the field of education is the emergence of artificial intelligence. This technology can allow for more effective one-on-one tutoring. Adaptive technology makes it easier for different students to learn on their own. However, teachers remain an integral part of every classroom.
Wrapping it up
The advancement in technology and its growing role in classroom education is an interesting development for future learning and teaching. Modern tools and applications are important assets that can enrich the field of education. However, it is important for educators to structure and run their classrooms in a way that can make the most out of these evolving technologies.
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Generative adversarial networks have a plethora of applications in industries such as cybersecurity, computer gaming, photography, and many more.
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Generative adversarial networks are a type of neural network that can generate new images from a given set of images that are similar to the given dataset, yet individually different. They are composed of two neural network models, a generator and a discriminator. The generator learns to develop new samples, whereas the discriminator learns to differentiate the generated examples from the real ones. These two models work together for training the generative adversarial network to generate and distinguish new plausible samples from the existing dataset. Since generative adversarial networks learn to recognize and distinguish images, they are used in industries where computer vision plays a major role such as photography, image editing, and gaming, and many more.
Five Eye-Catching Applications of Generative Adversarial Networks
Generative adversarial networks are unsupervised neural networks that train themselves by analyzing the information from a given dataset to create new image samples. Thus, they find applications in industries which rely on computer vision technology such as:
1. Improving Cybersecurity
Instances of cyber threats have increased in the last few years. Organizations are adopting advanced security measures to prevent sensitive information from being leaked and misused. Yet, hackers are coming up with new methods to obtain and exploit user data. Criminal activities like blackmailing users to keep their information private, publicly posting data to humiliate people, or tarnishing their images using fake images and videos are on the rise and are a grave concern. More and more data is willingly shared by people, in the form of images and videos, on the internet, and hence becomes an easy source to be wrongfully used. An adversarial attack is one such method used by hackers. Hackers manipulate images by adding malicious data to them. This tricks the neural network itself and compromises the intended working of the algorithm. This, in turn, can result in unwanted information being disclosed and compromised.
Generative adversarial networks can be trained to identify such instances of fraud. They can be used to make deep learning models more robust. The neural network can be trained to identify any malicious information that might be added to images by hackers. Researchers and analysts create fake examples on purpose and use them to train the neural network. The network improves upon itself as it analyzes multiple images. The Secure Steganography based on generative adversarial network technique is used to analyze and detect malicious encodings that shouldn’t be part of the images.
2. Improving Healthcare
The healthcare and pharmaceutical industry is poised to be one of the biggest beneficiaries of implementing artificial intelligence, neural networks, and generative adversarial networks. GANs can be used in medical tumor detection. The neural network can be used to identify tumors by comparing images with a dataset of images of healthy organs. The neural network can detect anomalies in the patient’s scans and images by identifying differences when comparing them to the dataset images. Using generative adversarial networks results in faster and accurate detection of cancerous tumors. It helps save costs for patients as well as doctors. However, most importantly, generative adversarial networks can potentially help save human lives.
Another area in the healthcare domain where generative adversarial networks can assist is drug discovery. The networks can be used for generating molecular structures for medicines that can be utilized in targeting and curing diseases. Researchers can train the generator with the existing database to find new compounds that can potentially be used to treat new diseases. The researchers don’t have to manually go through the entire database to search for compounds that can help fight new diseases. The algorithm automatically identifies such compounds and helps reduce the time required for research and development of such drugs.
3. Generating Animation Models
The video game industry can benefit hugely from generative adversarial networks. Developers and designers will have their work cut short, thanks to GANs. GANs can be used to automatically generate 3D models required in video games, animated movies, or cartoons. The network can create new 3D models based on the existing dataset of 2D images provided. The neural network can analyze the 2D photos to recreate the 3D models of the same in a short period of time. For example, 3D objects such as tables, chairs, cars, and guns can be generated by providing 2D images of these objects to the neural network. This will significantly help animators save time and utilize their time elsewhere for other important tasks.
Generative adversarial networks can also be used for creating 2D cartoons. For example, GAN can be used for the automatic generation of facial images for animes and cartoons. The generative adversarial network is trained on a specialized dataset such as anime character designs. The GAN generates new characters by analyzing the dataset of images provided. Taking inspiration from anime characters, individuals have tried to create Pokemon characters with generative adversarial networks with projects such as the pokeGAN project.
Another cool application of the generative adversarial network is creating emojis from human photographs. The neural network analyzes facial features to create a cartoonish version of individuals. Major technology companies such as Apple have leveraged the technology to generate custom emojis similar to an individual’s facial features.
4. Editing Photographs
When one thinks of using generative adversarial networks for editing photographs, they have to think beyond the usual enhancements with photo editing. Generative adversarial networks can be used for reconstructing images of faces to identify changes in features such as hair color, facial expressions, or gender, etc. This can help authorities identify criminals that might have undergone surgeries to modify their appearance. Similarly, face aging, with the help of generative adversarial networks, can be used to create facial images of people at various ages. This, again, can help identify people who have gone missing or absconded for years.
Additionally, GANs can be used to enhance images to make them more appealing and informative. Certain details can be removed from the image to make it more detailed. For example, He Zang et al., in their paper titled, “Image De-raining Using a Conditional Generative Adversarial Network” used generative adversarial networks to remove rain and snow from photographs.
5. Translating Images
Generative adversarial networks can be used for translating data from images. GANs can be utilized for image-to-image translations, semantic image-to-photo translations, and text-to-image translations.
Image to image translations: In image-to-image translations, GANs can be utilized for translation tasks such as:

Converting satellite photographs to Google Maps.
Editing details from day to night and vice versa.
Transforming black and white photographs to color.
Changing sketches to color photographs.

Jun-Yan Zhu introduced CycleGAN and other image translation examples such as translating horse from zebra, translating photographs to artistic style paintings, and translating a photograph from summer to winter, in their 2017 paper titled, “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.”
Semantic image-to-photo translations: Conditional GANs can be used to create a realistic image from a given semantic sketch as input. Some examples include; cityscape, apartments, human face, scenic environments, and vehicles whose photorealistic translations can be generated with the semantic input provided. For example, Ting-Chun Wang et al., in their 2017 paper titled “High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs,” demonstrated the use of conditional GANs for semantic image-to-photo translations.
Text-to-image translations: With generative adversarial networks, the neural network can automatically generate images by analyzing the text input. For example, the neural network can generate an image of a blue and black bird with yellow beak almost identical to an actual bird in accordance with the text data provided as input.

Generative adversarial networks already have a plethora of applications, and with ongoing research and advancements, it is poised to benefit many other industries. Unsupervised learning and generative adversarial networks are the next frontiers in artificial intelligence, and we are slowly but surely moving towards it.
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