This paper presents a network-aware automated machine learning (AutoML) framework for detecting distributed denial of service (DDoS) attacks in software-defined sensor networks (SDSN). The proposed framework selects an ideal machine…
Continue readingThe paper presents Fetal Ultrasound Semantic Clustering (FUSC), an unsupervised method that automates the clustering of ultrasound images into various fetal views. This approach eliminates the need for labor-intensive manual…
Continue readingThis article deals with Code-switching (CS), the phenomenon of mixing languages in a single sentence, and its challenges in Natural Language Processing (NLP) settings. The study extends existing research on…
Continue readingThe article discusses the issue of privacy in graph embedding, a powerful tool for learning latent representations for nodes in a graph. The authors highlight the need for privacy-preserving graph…
Continue readingThis paper evaluates the robustness of transformers in the face of adversarial samples in cybersecurity applications. The authors fine-tuned a set of transformer models, Convolutional Neural Network (CNN), and hybrid…
Continue readingThe article presents MalDICT, a collection of four benchmark datasets that support different, under-represented malware classification tasks. Malware can be classified according to various attributes, and the ability to identify…
Continue readingThe paper discusses the vulnerability of multi-agent collaborative (MAC) perception, particularly in applications like autonomous driving. MAC perception, despite its advantages, is more susceptible to adversarial attacks due to the…
Continue readingThe paper presents a new approach combining Maximum Entropy Deep Reinforcement Learning with a model-based control architecture to create an adaptive controller for autonomous underwater vehicles (AUVs). The approach involves…
Continue readingThis research investigates the success of message-passing graph neural networks (MPNNs) in solving linear optimization problems. The authors demonstrate that MPNNs can mimic standard interior-point methods for these problems, which…
Continue readingThis paper discusses the T02 team’s system for the Singing Voice Conversion Challenge 2023 (SVCC2023). The system includes a VITS-based SVC model, which consists of a feature extractor, a voice…
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