CybAI news

arXiv:2406.19770v1 Announce Type: new Abstract: Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a…

arXiv:2406.19668v1 Announce Type: new Abstract: Text-to-image (T2I) models achieve high-fidelity generation through extensive training on large datasets. However, these models may unintentionally pick up undesirable biases of their training data, such as over-representation of particular identities in gender or ethnicity neutral prompts. Existing alignment methods such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) fail to address this problem effectively because they operate on pairwise preferences consisting of individual samples, while the aforementioned biases can only be measured at a population level. For example, a single sample for the prompt "doctor" could be male or female,…

arXiv:2406.19564v1 Announce Type: new Abstract: Yor`ub'a an African language with roughly 47 million speakers encompasses a continuum with several dialects. Recent efforts to develop NLP technologies for African languages have focused on their standard dialects, resulting in disparities for dialects and varieties for which there are little to no resources or tools. We take steps towards bridging this gap by introducing a new high-quality parallel text and speech corpus YOR`ULECT across three domains and four regional Yor`ub'a dialects. To develop this corpus, we engaged native speakers, travelling to communities where these dialects are spoken, to collect text and speech data.…

From TVs to switchboards to cameras and even vacuums, nowadays, all common household appliances have a tiny computer. While this tech has undoubtedly made lives drastically easier, it also has certain limitations and threats due to misuse. No device is 100%, and robot vacuum cleaners are no different. Users are concerned that if these devices with cameras (used for mapping and navigating floors) are hacked, they can become dangerous surveillance equipment. This guide discusses the truth of such concerns and outlines ways to minimize the chances of these concerns becoming a reality. Why Are Robot Vacuum Cameras at Risk of…