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Topic Review
Unmanned Aerial Vehicles and Federated Learning
Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy.
  • 813
  • 22 Jul 2022
Topic Review
Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs). They overcome the low data availability of some biological and engineering systems that makes most state-of-the-art machine learning techniques lack robustness, rendering them ineffective in these scenarios. The prior knowledge of general physical laws acts in the training of neural networks (NNs) as a regularization agent that limits the space of admissible solutions, increasing the correctness of the function approximation. This way, embedding this prior information into a neural network results in enhancing the information content of the available data, facilitating the learning algorithm to capture the right solution and to generalize well even with a low amount of training examples.
  • 813
  • 28 Nov 2022
Topic Review
AI and Time Management: Boosting Productivity and Efficiency
In today's fast-paced world, time is a precious resource that needs to be managed efficiently to achieve maximum productivity. Artificial intelligence (AI) has emerged as a game-changer in this regard, providing individuals and institutions with powerful tools to optimize their time management. The research explores the various ways in which AI is helping individuals and institutions to boost their productivity and efficiency through better time management. The AI-based productivity tools, automated time tracking, predictive analytics, and personalized time management, highlighting the benefits and potential limitations of each approach were discussed.
  • 812
  • 22 May 2023
Topic Review Video Peer Reviewed
Geometry-Based Deep Learning in the Natural Sciences
Nature is composed of elements at various spatial scales, ranging from the atomic to the astronomical level. In general, human sensory experience is limited to the mid-range of these spatial scales, in that the scales which represent the world of the very small or very large are generally apart from our sensory experiences. Furthermore, the complexities of Nature and its underlying elements are not tractable nor easily recognized by the traditional forms of human reasoning. Instead, the natural and mathematical sciences have emerged to model the complexities of Nature, leading to knowledge of the physical world. This level of predictiveness far exceeds any mere visual representations as naively formed in the Mind. In particular, geometry has served an outsized role in the mathematical representations of Nature, such as in the explanation of the movement of planets across the night sky. Geometry not only provides a framework for knowledge of the myriad of natural processes, but also as a mechanism for the theoretical understanding of those natural processes not yet observed, leading to visualization, abstraction, and models with insight and explanatory power. Without these tools, human experience would be limited to sensory feedback, which reflects a very small fraction of the properties of objects that exist in the natural world. As a consequence, as taught during the times of antiquity, geometry is essential for forming knowledge and differentiating opinion from true belief. It not only provides a framework for understanding astronomy, classical mechanics, and relativistic physics, but also the morphological evolution of living organisms, along with the complexities of the cognitive systems. Geometry also has a role in the information sciences, where it has explanatory power in visualizing the flow, structure, and organization of information in a system. This role further impacts the explanations of the internals of deep learning systems as developed in the fields of computer science and engineering.
  • 812
  • 21 Jun 2023
Topic Review
Risk Analysis of Engineering Procurement and Construction
The lump sum turn key (LSTK) contract for engineering, procurement, and construction (EPC) projects is a typical contract type used in large-scale and complex plant projects. 
  • 812
  • 21 Jun 2022
Topic Review
Parisoma
Coordinates: 37°46′24.89″N 122°24′57.19″W / 37.7735806°N 122.4158861°W / 37.7735806; -122.4158861 Parisoma (/ˈpærɪˈsoʊmə/) is a coworking space and open incubator in the SoMa district of San Francisco founded and managed by the firm Fabernovel. In addition to providing shared work space to approximately 120 members, it also hosts events and classes related to design, business, technology, and entrepreneurship.
  • 810
  • 22 Nov 2022
Topic Review
Loot System
In video games, a loot system is a method of distributing in-game items amongst a group of players, after having "looted" them.
  • 810
  • 07 Nov 2022
Topic Review
NeWS
NeWS (Network extensible Window System) is a discontinued windowing system developed by Sun Microsystems in the mid-1980s. Originally known as "SunDew", its primary authors were James Gosling and David S. H. Rosenthal. The NeWS interpreter was based on PostScript (as was the later Display PostScript, although the two projects were otherwise unrelated) extending it to allow interaction and multiple "contexts" to support windows. Like PostScript, NeWS could be used as a complete programming language, but unlike PostScript, NeWS could be used to make complete interactive programs with mouse support and a GUI.
  • 809
  • 14 Nov 2022
Topic Review
Measuring Network Throughput
Throughput of a network can be measured using various tools available on different platforms. This page explains the theory behind what these tools set out to measure and the issues regarding these measurements. Reasons for measuring throughput in networks. People are often concerned about measuring the maximum data throughput in bits per second of a communications link or network access. A typical method of performing a measurement is to transfer a 'large' file from one system to another system and measure the time required to complete the transfer or copy of the file. The throughput is then calculated by dividing the file size by the time to get the throughput in megabits, kilobits, or bits per second. Unfortunately, the results of such an exercise will often result in the goodput which is less than the maximum theoretical data throughput, leading to people believing that their communications link is not operating correctly. In fact, there are many overheads accounted for in throughput in addition to transmission overheads, including latency, TCP Receive Window size and system limitations, which means the calculated goodput does not reflect the maximum achievable throughput.
  • 809
  • 17 Oct 2022
Topic Review
Model Predictive Traffic Control by Bi-Level Optimization
A bi-level model for traffic signal optimization is developed. The model predictive framework is applied for traffic control in an urban traffic network. The potential of the bi-level formalization is used to increase the space of control influences with simultaneous evaluation of the green light and cycle durations. Thus, the increased control space allows more traffic parameters to be considered, such as vehicles queues and traffic flows. A particular modification of the bi-level control is applied for the synchronization of the traffic lights in the network. The model predictive approach is used for the real-time management of the traffic in the network. The control implementations are constrained by the shortest evaluated cycle.
  • 809
  • 11 May 2022
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