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All Topic Review Biography
Topic Review
A Benchmark Dataset for Wearable Low-Light Pedestrian Detection
Detecting pedestrians in low-light conditions is challenging, especially in the context of wearable platforms. Infrared cameras have been employed to enhance detection capabilities, whereas low-light cameras capture the more intricate features of pedestrians. With this in mind, a low-light pedestrian detection (called HRBUST-LLPED) dataset by capturing pedestrian data on campus using wearable low-light cameras is introduced.
  • 160
  • 19 Dec 2023
Topic Review
A Certain Magical Index
A Certain Magical Index (Japanese: とある魔術の禁書目録 (インデックス), Hepburn: Toaru Majutsu no Indekkusu)[lower-alpha 1] is a Japanese light novel series written by Kazuma Kamachi and illustrated by Kiyotaka Haimura, which has been published by ASCII Media Works under their Dengeki Bunko imprint since April 2004. The plot is set in a world where supernatural abilities exist. The light novels focus on Toma Kamijo, a young high school student in Academy City who has an unusual ability, as he encounters an English nun named Index. His ability and relationship with Index proves dangerous to other sorcerers and Espers who want to discover the secrets behind him and Index, as well as the city. Yen Press have licensed the novels in North America. A manga adaptation by Chuya Kogino began serialization in Monthly Shōnen Gangan from May 2007. J.C.Staff produced two 24-episode anime series between 2008 and 2011. An animated film was released in February 2013. The anime adaptations are licensed in North America by Funimation. A 26-episode third season aired between 2018 and 2019, licensed by Funimation. The side-story manga series, A Certain Scientific Railgun, began serialization in Dengeki Daioh in February 2007. A second side-story manga series, A Certain Scientific Accelerator, began serialization in Dengeki Daioh in December 2013.
  • 1.2K
  • 19 Oct 2022
Topic Review
A Cyber-Physical System for Wildfire Detection and Firefighting
The increasing frequency and severity of forest fires necessitate early detection and rapid response to mitigate their impact. This project aims to design a cyber-physical system for early detection and rapid response to forest fires using advanced technologies. The system incorporates Internet of Things sensors and autonomous unmanned aerial and ground vehicles controlled by the robot operating system. An IoT-based wildfire detection node continuously monitors environmental conditions, enabling early fire detection. Upon fire detection, a UAV autonomously surveys the area to precisely locate the fire and can deploy an extinguishing payload or provide data for decision-making. The UAV communicates the fire's precise location to a collaborative UGV, which autonomously reaches the designated area to support ground-based firefighters. The CPS includes a ground control station with web-based dashboards for real-time monitoring of system parameters and telemetry data from UAVs and UGVs. The real-time fire detection capabilities of the proposed system are demonstrated using simulated forest fire scenarios. The objective is to provide a practical approach using open-source technologies for early detection and extinguishing of forest fires, with potential applications in various industries, surveillance, and precision agriculture.
  • 479
  • 26 Jul 2023
Topic Review
A discrete quantum momentum operator
We introduce finite-differences derivatives intended to be exact when applied to the real exponential function. We want to recover the known results of continuous calculus with our finite differences derivatives but in a discrete form. The purpose of this work is to have a discrete momentum operator suitable for use as an operator in discrete quantum mechanics theory.
  • 1.8K
  • 24 Aug 2021
Topic Review
A Generative Adversarial Network Technique for Ransomware Behavior Prediction
The ransomware attacks threaten not only personal files but also critical infrastructure like smart grids, necessitating early detection before encryption occurs. Current methods, reliant on pre-encryption data, suffer from insufficient and rapidly outdated attack patterns, despite efforts to focus on select features. Such an approach assumes that the same features remain unchanged. This approach proves ineffective due to the polymorphic and metamorphic characteristics of ransomware, which generate unique attack patterns for each new target, particularly in the pre-encryption phase where evasiveness is prioritized. 
  • 283
  • 30 Oct 2023
Topic Review
A Lightweight Double-Stage Scheme to Identify Malicious DNS
The Domain Name System (DNS) protocol essentially translates domain names to IP addresses, enabling browsers to load and utilize Internet resources. Despite its major role, DNS is vulnerable to various security loopholes that attackers have continually abused.
  • 255
  • 04 Dec 2023
Topic Review
A Lightweight Object Detection Network with Attention Modules
Object detection methods based on deep learning typically require devices with ample computing capabilities, which limits their deployment in restricted environments such as those with embedded devices.
  • 112
  • 22 Nov 2023
Topic Review
A Lightweight UAV SLAM System
Unmanned aerial vehicles (UAVs) can experience significant performance issues during flight due to heavy CPU load, affecting their flight capabilities, communication, and endurance.
  • 244
  • 09 Jun 2023
Topic Review
A Machine Learning-Based Sustainable University Field Training Framework
The proposed sustainable University Field Training (SUNFIT) is an educational data mining framework based on the pedagogical strategies of preparing, conducting, and assessing computing students’ skills in courses involving practical industry engagement.
  • 293
  • 29 May 2023
Topic Review
A New Container Throughput Forecasting Paradigm under COVID-19
COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. Combining this with change-point analysis and empirical mode decomposition (EMD), this uses the decomposition–ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, here tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. 
  • 464
  • 24 Mar 2022
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