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While image captioning has gained significant attention, the potential of captioning time-series images, prevalent in areas like finance and healthcare, remains largely untapped. Existing time-series captioning methods typically offer generic, domain-...

Weakly supervised semantic segmentation using image-level class labels is challenging due to the limitations of Class Activation Maps (CAMs) in convolutional neural networks (CNNs), which often highlight only the most discriminative image regions. ...

The difficulty of unmanned aerial vehicle (UAV) control recently lies in multidirectional movement in 3-dimensional space, improving control accuracy and manipulation safety. To address these challenges, a UAV control system that ...

Deep learning has brought about a revolutionary transformation in network applications, particularly in domains like e-commerce and online advertising. Distributed training (DT), as a critical means to expedite model training, has progressively emerged ...

The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requiring spontaneous or unpredictable tasks demanded by users. This paper ...

Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges. Recent studies have explored image-driven approaches using computer vision models to address these challenges, often ...

To solve the problem of insufficient and imbalanced data in Network Intrusion Detection (NID) in practical scenarios, which leads to low detection accuracy of the model. We propose a network intrusion algorithm based on the Federated Learning with ...

The neural network data-free backdoor attack is an emerging and potent threat, which requires minimal resources and does not rely on original training data to implant backdoors. This threat poses a significant risk to industrial control systems, where ...

When an end-user instructs a taskable robot on a new task, it is important for the robot to learn the user’s intention for the task. Knowing the user’s intention, represented as desired goal conditions, allows the robot to generalize across ...

• Semantic Robot programming from multiple demonstrations for taskable robotics.
• Factor graph belief propagation for generative inference of user intention.
• Generalization to unseen task with novel classes and numbers of objects.

Network administrators face the challenge of efficiently patching overwhelming volumes of vulnerabilities with limited time and resources. To address this issue, they must prioritize vulnerabilities based on the associated risk/severity measurements (i.e.,...

Service robots will be co-located with human users in an unstructured human-centered environment and will benefit from understanding the user's daily activities, preferences, and needs towards fully assisting them. This workshop aims to explore how ...

Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts. Despite the effectiveness of existing efforts, ...

Sign language is the most important communication method for people with speech impairments, and automatic sign language recognition helps them communicate with normal people without barriers. For portability considerations, the device that ...

At present, resource allocation schemes based on cellular users and D2D users have been widely concerned by the society. With the development of society, the number of cellular users and D2D users has been increasing, but the frequency spectrum ...

In this paper, we study the transmission power allocation of the secondary base station to the secondary user in cognitive radio networks when multiple primary users (PU) and multiple secondary users (SU) adopt NOMA. In the proposed Underlay ...

Security and risk assessment aims to prioritize detected vulnerabilities for remediation in a computer networking system. The widely used expert-based risk prioritization approach, e.g., Common Vulnerability Scoring System (CVSS), cannot realistically ...

Assembly tasks are challenging for robot manipulation because the robot must reason over the composed effects of actions and execute multi-objective behaviors. Robots typically use predefined priorities provided by users to determine how to compose ...

The wide deployment of internet of things (IoT) devices makes a profound impact on the data center industry from various perspectives, varying from infrastructure operation, resource management, to end users. This is a double-edged sword – it enables ...

The most existing sign language recognition methods have made significant progress. However, there are still problems in the field of sign language recognition: Traditional SLR technology relies on external devices such as data gloves, position ...

This dissertation investigates the problem of efficiently and effectively prioritizing a vulnerability risk in a computer networking system. Vulnerability prioritization is one of the most challenging issues in vulnerability management, which ...