An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called RRSGA which improves the fitness functions for recommending optimal results. The proposed approach employs a genetic algorithm to address the over-specialization issue of content-based filtering. The proposed method aims to incorporate genetic algorithms that bring variety to recommendations and efficiently adjust and suggest unpredictable and innovative things to the user. Experiments objectively demonstrate that our technology can recommend additional products that every consumer is likely to appreciate. The results of RRSGA have been compared against recommendation results from the content-based filtering approach. The results indicate the effectiveness of RRSGA and its capacity to make more accurate predictions than alternative approaches.

TinyML-Based Concept System Used to Analyze Whether the Face Mask Is Worn Properly in Battery-Operated Conditions

As the COVID-19 pandemic emerged, everyone’s attention was brought to the topic of the health and safety of the entire human population. It has been proven that wearing a face mask can help limit the spread of the virus. Despite the enormous efforts of people around the world, there still exists a group of people that wear face masks incorrectly. In order to provide the best level of safety for everyone, face masks must be worn correctly, especially indoors, for example, in shops, cinemas and theaters. As security guards can only handle a limited area of the frequently visited objects, intelligent sensors can be used. In order to mount them on the shelves in the shops or near the cinema cash register queues, they need to be capable of battery operation. This restricts the sensor to be as energy-efficient as possible, in order to prolong the battery life of such devices. The cost is also a factor, as cheaper devices will result in higher accessibility. An interesting and quite novel approach that can answer all these challenges is a TinyML system, that can be defined as a combination of two concepts: Machine Learning (ML) and Internet of Things (IoT). The TinyML approach enables the usage of ML algorithms on boards equipped with low-cost, low-power microcontrollers without sacrificing the classifier quality. The main goal of this paper is to propose a battery-operated TinyML system that can be used for verification whether the face mask is worn properly. To this end, we carefully analyze several ML approaches to find the best method for the considered task. After detailed analysis of computation and memory complexity as well as after some preliminary experiments, we propose to apply the K-means algorithm with carefully designed filters and a sliding window technique, since this method provides high accuracy with the required energy-efficiency for the considered classification problem related to verification of using the face mask. The STM32F411 chip is selected as the best microcontroller for the considered task. Next, we perform wide experiments to verify the proposed ML framework implemented in the selected hardware platform. The obtained results show that the developed ML-system offers satisfactory performance in terms of high accuracy and lower power consumption. It should be underlined that the low-power aspect makes it possible to install the proposed system in places without the access to power, as well as reducing the carbon footprint of AI-focused industry which is not negligible. Our proposed TinyML system solution is able to deliver very high-quality metric values with accuracy, True Positive Ratio (TPR), True Negative Ratio (TNR), precision and recall being over 96% for masked face classification while being able to reach up to 145 days of uptime using a typical 18650 battery with capacity of 2500 mAh and nominal voltage of 3.7 V. The results are obtained using a STM32F411 microcontroller with 100 MHz ARM Cortex M4, which proves that execution of complex computer vision tasks is possible on such low-power devices. It should be noted that the STM32F411 microcontroller draws only 33 mW during operation.

The Pransky interview: Dr Raffaello D’Andrea, Founder, CEO, and Chairman of the board at Verity; Entrepreneur; Professor; Scientist and Artist

Purpose The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD and inventor regarding his pioneering efforts and the commercialization of bringing a technological invention to market. This paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Raffaello D’Andrea, a highly successful entrepreneur and proven business leader and one of the world’s foremost leaders in robotics and machine learning. D’Andrea is Founder, CEO and Chairman of the Board at Verity, the world’s leading autonomous indoor drone company, as well as a Professor of Dynamic Systems and Control at the Swiss Federal Institute of Technology (ETH) in Zurich. D’Andrea is also one of the co-founders and advisors of Robo-Global, an index and research company focused on investments in robotics, automation and artificial intelligence. In this interview, D’Andrea shares some of his business and personal experiences of working in industry and academia and his criteria for turning his ideas into successful working systems. Findings Raffaello D’Andrea’s entire career is built on his ability to bridge theory and practice. D’Andrea combined his love for science with his need to create and received a BS degree in engineering science at the University of Toronto, where he was awarded the Wilson Medal as the top graduating student in 1991. He obtained both his MS and PhD degrees in electrical engineering at Caltech, and then he joined the Cornell faculty as an assistant professor. While on leave from Cornell, from 2003 to 2007, he co-founded the disruptive warehouse automation company Kiva Systems, where he led the systems architecture, robot design, robot navigation and coordination, and control algorithms efforts. In 2014, D’Andrea took robotics technology into the air and founded Verity, the world’s first company to deliver a fully integrated autonomous, indoor drone-based system solution. Originality/value Raffaello D’Andrea combines academia, business and the arts to reinvent autonomous systems. D’Andrea was a founding member of the Systems Engineering Program at Cornell, where he established robot soccer as the flagship, multidisciplinary team project. In addition to pioneering the use of semi-definite programming for the design of distributed control systems, he went on to lead the Cornell Robot Soccer Team to win four world international RoboCup championships. Kiva Systems, co-founded by D’Andrea and acquired by Amazon in 2012, helped the re-branded Amazon Robotics to disrupt the entire warehousing and logistics systems industry. Additionally, D’Andrea is an internationally-exhibited new media artist, best known for the Robotic Chair (Ars Electronica, ARCO, London Art Fair, National Gallery of Canada) and Flight Assembled Architecture (FRAC Centre). With his team at Verity, he created the drone design and choreography for Cirque Du Soleil’s Paramour on Broadway, Metallica’s WorldWired Tour and Céline Dion’s Courage Tour. Other D’Andrea creations include the Flying Machine Arena, where flying robots perform aerial acrobatics, juggle balls, balance poles and cooperate to build structures; the Distributed Flight Array, a flying platform consisting of multiple autonomous single propeller vehicles that are able to drive, dock with their peers and fly in a coordinated fashion; the Balancing Cube, a dynamic sculpture that can balance on any of its edges or corners and its little brother Cubli, a small cube that can jump up, balance and walk; Blind Juggling Machines that can juggle balls without seeing them, and without catching them. D’Andrea is also collaborating with scientists, engineers, and wingsuit pilots to create an actively controlled suit that will allow humans to take off and land at will, to gain altitude, even to perch, while preserving the intimacy of wingsuit flight. D’Andrea has received the IEEE Robotics and Automation Award, the Engelberger Robotics Award, the IEEE/IFR Invention and Entrepreneurship Award in Robotics and Automation and the Presidential Early Career Award for Scientists and Engineers. In 2020, he was inducted in the National Inventors Hall of Fame and elected to the National Academy of Engineering.

Educational research on mathematics differential equation to simulate the model of children's mental health prevention and control system

In order to solve the practicality of the infant mental health tracking system, the system is simulated based on mathematical differential equations. The SSH framework technology of the mode-view-controller (MVC) design model analyses and designs the model of the prevention and control system of children's mental health. In this design pattern, the Struts framework is used to realise the separation of the view layer, the business layer and the control layer, and the Hibernate framework is used to realise the data persistence layer, thereby obtaining a system solution with strong scalability, low coupling and easy maintenance.

Studies of the movement of ice through low-pressure waterworks

Passage of ice through hydraulic structures is a significant and urgent issue for study. When examining this issue, various conditions should be taken into account. Thus, the successful passage of ice through hydraulic structures depends both on ice characteristics, the parameters of the spillway structures, and on the ice movement velocity in front of the spillway. The goals have been formed; the research tasks have been set. As a result, a theoretical solution to determine the bending moments necessary for calculating the fracture of ice floes has been obtained. The proposed analytical solution for determining the bending moments is found based on a system solution of differential equations of a rigid body motion in a fluid. The assumptions made for this case are set out in the article. The resulting dependence makes it possible to determine the bending moments in any section of the ice section and take into account the flow dynamics. A complex of laboratory studies was carried out, aimed at studying the ice movement velocities on the developed and manufactured model of a spillway with a wide threshold, in front of which an auxiliary threshold is installed to create a concentrated fall. The dependence for determining the velocity in the form of a trigonometric function is obtained and converted into a convenient formula. The performed calculations are compared with the field data and satisfactory results are given.

Integration of Low Interaction Honeypot and ELK Stack as Attack Detection Systems on Servers

The high need for information technology that can be accessed anywhere and anytime indirectly opens a big opportunity for irresponsible parties to attack and destroy the system. The server farm is one of the targets most hunted by attackers, intending to damage, and even retrieving victim data. One of the efforts to deal with this problem is to add server security by using honeypot. The existence of a honeypot is one of the efforts to prevent system hacking by creating a fake server to divert attackers access. In its application, the logs generated from the honeypot are only letters and numbers, making it difficult to analyze the logs. It became a problem it will being a lot of log data being processed. To make it easier for administrators in analyzing logs, a visualization system using the ELK Stack is proposed. Honeypot and ELK Stack integration can be a security system solution in detecting attacks while providing visualization to administrators. Five testing schemes were carried out to provide a comparative study between the low interaction honeypot Cowrie and Dionaea. Cowrie delivers a better performance detection system (real-time) compared to the detection system offered by Dionaea, and the average delay time is 3.75 seconds, while ELK managed to provide better monitoring results to administrators through its visualization.

Real-Time Image Based Lighting for 360 Degree Panoramic Video

<p>The application of the newly popular content medium of 360 degree panoramic video to the widely used offline lighting technique of image based lighting is explored, and a system solution for real-time image based lighting of virtual objects using only the provided 360 degree video for lighting is developed. The system solution is suitable for use on live streaming video input, and is shown to run on consumer grade graphics hardware at the high resolutions and framerates necessary for comfortable viewing on head mounted displays, rendering at over 60 frames per second for stereo output at 1182x1464 per eye on a mid-range graphics card. Its use in several real-world applications is also studied, and extension to consider real-time shadowing and reflection is explored.</p>

Real-Time Image Based Lighting for 360 Degree Panoramic Video

<p>The application of the newly popular content medium of 360 degree panoramic video to the widely used offline lighting technique of image based lighting is explored, and a system solution for real-time image based lighting of virtual objects using only the provided 360 degree video for lighting is developed. The system solution is suitable for use on live streaming video input, and is shown to run on consumer grade graphics hardware at the high resolutions and framerates necessary for comfortable viewing on head mounted displays, rendering at over 60 frames per second for stereo output at 1182x1464 per eye on a mid-range graphics card. Its use in several real-world applications is also studied, and extension to consider real-time shadowing and reflection is explored.</p>

A GPU-Based Integrated Simulation Framework for Modelling of Complex Subsurface Applications

Alternative to CPU computing architectures, such as GPU, continue to evolve increasing the gap in peak memory bandwidth achievable on a conventional workstation or laptop. Such architectures are attractive for reservoir simulation, which performance is generally bounded by system memory bandwidth. However, to harvest the benefit of a new architecture, the source code has to be inevitably rewritten, sometimes almost completely. One of the biggest challenges here is to refactor the Jacobian assembly which typically involves large volumes of code and complex data processing. We demonstrate an effective and general way to simplify the linearization stage extracting complex physics-related computations from the main simulation loop and leaving only an algebraic multi-linear interpolation kernel instead. In this work, we provide the detailed description of simulation performance benefits from execution of the entire nonlinear loop on the GPU platform. We evaluate the computational performance of Delft Advanced Research Terra Simulator (DARTS) for various subsurface applications of practical interest on both CPU and GPU platforms, comparing particular workflow phases including Jacobian assembly and linear system solution with both stages of the Constraint Pressure Residual preconditioner.