5G Network slicing is one of the key enabling technologies that offer dedicated logical resources to different applications on the same physical network. However, a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore, recent DoS/DDoS attack detection techniques are based on the available data sets which are collected from simulated 5G networks rather than from 5G network slices.
Gowers' Sign is a visual symptom exhibited by many neuromuscular dystrophies, including Becker muscular dystrophy, congenital muscular dystrophy, congenital myopathy, and Duchenne muscular dystrophy, which is the most aggressive, with a life expectancy of 20 to 30 years. Additionally, there is a 2.5-year gap between the onset of initial symptoms and a confirmed diagnosis. Early detection allows for the treatment of the disease, leading to a better quality of life. To the best of our knowledge, a non-invasive computer vision system for detecting Gowers' Sign has not yet been proposed.
Recognizing and categorizing banknotes is a crucial task, especially for individuals with visual impairments. It plays a vital role in assisting them with everyday financial transactions, such as making purchases or accessing their workplaces or educational institutions. The primary objectives for creating this dataset were as follows:
Physically unclonable functions (PUFs) are foundational components that offer a cost-efficient and promising solution for diverse security applications, including countering integrated circuit (IC) counterfeiting, generating secret keys, and enabling lightweight authentication. PUFs exploit semiconductor variations in ICs to derive inherent responses from imposed challenges, creating unique challenge-response pairs (CRPs) for individual devices. Analyzing PUF security is pivotal for identifying device vulnerabilities and ensuring response credibility.
The risks to children of online predators in real time gaming environments have been an area of growing concern. Research towards the development of near real time capabilities has been the focus of most queries published in this area of study. In this paper, we present Protectbot, a comprehensive safety framework used to interact with users in online gaming chat rooms. Protectbot employs a variant of the GPT-2 model known as DialoGPT, a generative pre-trained transformer designed specifically for conversation.
The Internet of Things (IoT) is the nova emergence of connecting devices, people, and objects over the internet as sensors and actuators for a targeted application. Among popular IoT applications, location based IoT services are curtailed. Where the geographical location of a moving object will be estimated based on signal strength values using algorithms. To estimate the locations using algorithms distance measurement such as Received Signal Strength Indicator (RSSI) is required.
This data collection focuses on capturing user-generated content from the popular social network Reddit during the year 2023. This dataset comprises 29 user-friendly CSV files collected from Reddit, containing textual data associated with various emotions and related concepts.
This dataset contains the measurement in an ultrawide band (UWB) system in the 6.5 GHz band, considering the presence of the human body as the only obstacle. There are measurements in line-of-sight condition to compare the results of the analysis performed. The measurements are part of our research on the adverse effects of the body shadowing in pedestrian localization systems.
The UWB system has four fixed terminals and one mobile terminal. The mobile terminal is worn on the chest of the person.
Problems related to ventral hernia are very common, and evaluating them using computational methods can assist in selecting the most appropriate treatment. This study collected data from over 3500 patients from different European countries observed during last 11 years (2012-2022), which were collected by specialists in hernia surgery. The majority of patients underwent standard surgical procedures, with a growing trend towards robotic surgery.
A long-standing problem in thermal imaging is the inherent assumption of a uniform and known emissivity across an entire image. Semantic segmentation of the materials in a thermal image can identify the pixel-wise emissivity, thus rectifying the spatially uniform emissivity assumption with no human intervention. We have created a multispectral thermal image dataset consisting of nine materials (acrylic, aluminum, bakelite, ceramic, cork, EVA, granite, maple, and silicone) at six different temperatures.
