Related papers: Development of Dynamic Local Area Network (LAN) Ba…
Large language models (LLMs) have achieved impressive performance across various natural language benchmarks, prompting a continual need to curate more difficult datasets for larger LLMs, which is costly and time-consuming. In this paper,…
Deep learning has made many remarkable achievements in many fields but suffers from noisy labels in datasets. The state-of-the-art learning with noisy label method Co-teaching and Co-teaching+ confronts the noisy label by mutual-information…
Purpose: This study proposes an adaptive bandwidth management system which can be explicitly used by educational institutions. The primary goal of the system is to increase the bandwidth of the users who access more on educational websites.…
Plagiarism detection in educational programming assignments is still a problematic issue in terms of resource waste, ethical controversy, legal risks, and technical complexity. This paper presents AC, a modular plagiarism detection system.…
Active learning enables the efficient construction of a labeled dataset by labeling informative samples from an unlabeled dataset. In a real-world active learning scenario, considering the diversity of the selected samples is crucial…
Web applications are distributed applications, they are programs that run on more than one computer and communicate through a network or server. This very distributed nature of web applications, combined with the scale and sheer complexity…
Attacks targeting software on embedded systems are becoming increasingly prevalent. Remote attestation is a mechanism that allows establishing trust in embedded devices. However, existing attestation schemes are either static and cannot…
The functionality of electronic circuits can be seriously impaired by the occurrence of dynamic hardware faults. Particularly, for digital ultra low-power systems, a reduced safety margin can increase the probability of dynamic failures.…
Predictable network performance is key in many low-power wireless sensor network applications. In this paper, we use machine learning as an effective technique for real-time characterization of the communication performance as observed by…
Accurately and efficiently estimating system performance under uncertainty is paramount in power system planning and operation. Monte Carlo simulation is often used for this purpose, but convergence may be slow, especially when detailed…
In malware detection, dynamic analysis extracts the runtime behavior of malware samples in a controlled environment and static analysis extracts features using reverse engineering tools. While the former faces the challenges of…
Imitation learning field requires expert data to train agents in a task. Most often, this learning approach suffers from the absence of available data, which results in techniques being tested on its dataset. Creating datasets is a…
Real-time on-device continual learning is needed for new applications such as home robots, user personalization on smartphones, and augmented/virtual reality headsets. However, this setting poses unique challenges: embedded devices have…
The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base…
Most reinforcement learning (RL) platforms use high-level programming languages, such as OpenAI Gymnasium using Python. These frameworks provide various API and benchmarks for testing RL algorithms in different domains, such as autonomous…
Ultrafast online learning is essential for high-frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub-microsecond timescales. Meeting these requirements demands low-latency,…
This paper provides an overview of students behavior analysis on a learning management system (LMS), Blackboard (Bb) Learn for a core data communications course of the Undergraduate IT program in the Information Sciences and Technology…
With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational…
Machine-learning-based prediction of future wireless link quality is an emerging technique that can potentially improve the reliability of wireless communications, especially at higher frequencies (e.g., millimeter-wave and terahertz…
The reliability of deep learning accelerators (DLAs) used in autonomous driving systems has significant impact on the system safety. However, the DLA reliability is usually evaluated with low-level metrics like mean square errors of the…