Tootfinder

Johnson chastises Musk for getting "personal" in Trump feud (Justin Green/Axios)
https://www.axios.com/2025/06/06/johnson-musk-trump-reud-orckets
http://www.memeorandum.com/250606/p41#a250606p41

Dinner Party Orecchiette #Eggs, #Pasta

Dinner Party Orecchiette
Orecchiette is one of my favourite ways to use (and preserve) eggs. This batch is for a dinner party. Homemade orecchiette, frozen ahead of time and just cooked straight from frozen while the guest…

HelioIndex: A Directory of Active Researchers in Solar and Heliospheric Physics
Peter R. Young
https://arxiv.org/abs/2506.00159 https://

HelioIndex: A Directory of Active Researchers in Solar and Heliospheric Physics
HelioIndex is a directory of authors who are active in solar and heliospheric physics (SHP). It is available at the webpage HelioIndex.org, and it includes several derived products such as publication lists, country and institute data, journal data, and lists of the most cited articles in the field. HelioIndex is built from ORCID identifiers and publication data obtained from the Astrophysics Data System and ORCID. Selection criteria have been chosen to approximately correspond to a researcher …

Today is #BandcampFriday! (thank you @… for the reminder: https://ravenati…

soc_net_comms: Networks with group metadata
Snapshots of LiveJournal, Friendster, Orkut, and YouTube online social networks, as well as DBLP and Amazon. Node metadata represents a post hoc definition of a 'community' that a node belongs to, derived from topical labels of the node or interest-based 'groups' that a node links to.
This network has 317080 nodes and 1049866 edges.
Tags: Online, Social, Collaboration, Informational, Relatedness, Unweighted, Metada…

Towards Source Attribution of Singing Voice Deepfake with Multimodal Foundation Models
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar, Swarup Ranjan Behera, Priyabrata Mallick, Pailla Balakrishna Reddy, Arun Balaji Buduru, Rajesh Sharma
https://arxiv.org/abs/2506.03364

Towards Source Attribution of Singing Voice Deepfake with Multimodal Foundation Models
In this work, we introduce the task of singing voice deepfake source attribution (SVDSA). We hypothesize that multimodal foundation models (MMFMs) such as ImageBind, LanguageBind will be most effective for SVDSA as they are better equipped for capturing subtle source-specific characteristics-such as unique timbre, pitch manipulation, or synthesis artifacts of each singing voice deepfake source due to their cross-modality pre-training. Our experiments with MMFMs, speech foundation models and mus…

Well, that's more-or-less what I was saying, though obviously addition
is a little more cosmic than the bitwise operators.
-- Larry Wall in <199709051808.LAA01780@wall.org>

soc_net_comms: Networks with group metadata
Snapshots of LiveJournal, Friendster, Orkut, and YouTube online social networks, as well as DBLP and Amazon. Node metadata represents a post hoc definition of a 'community' that a node belongs to, derived from topical labels of the node or interest-based 'groups' that a node links to.
This network has 317080 nodes and 1049866 edges.
Tags: Online, Social, Collaboration, Informational, Relatedness, Unweighted, Metada…

HYFuse: Aligning Heterogeneous Speech Pre-Trained Representations in Hyperbolic Space for Speech Emotion Recognition
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar, Swarup Ranjan Behera, Pailla Balakrishna Reddy, Arun Balaji Buduru, Rajesh Sharma
https://arxiv.org/abs/2506.03403

HYFuse: Aligning Heterogeneous Speech Pre-Trained Representations in Hyperbolic Space for Speech Emotion Recognition
Compression-based representations (CBRs) from neural audio codecs such as EnCodec capture intricate acoustic features like pitch and timbre, while representation-learning-based representations (RLRs) from pre-trained models trained for speech representation learning such as WavLM encode high-level semantic and prosodic information. Previous research on Speech Emotion Recognition (SER) has explored both, however, fusion of CBRs and RLRs haven't been explored yet. In this study, we solve this gap…

SNIFR : Boosting Fine-Grained Child Harmful Content Detection Through Audio-Visual Alignment with Cascaded Cross-Transformer
Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish, Swarup Ranjan Behera, Abu Osama Siddiqui, Sarthak Jain, Priyabrata Mallick, Jaya Sai Kiran Patibandla, Pailla Balakrishna Reddy, Arun Balaji Buduru, Rajesh Sharma
https://

SNIFR : Boosting Fine-Grained Child Harmful Content Detection Through Audio-Visual Alignment with Cascaded Cross-Transformer
As video-sharing platforms have grown over the past decade, child viewership has surged, increasing the need for precise detection of harmful content like violence or explicit scenes. Malicious users exploit moderation systems by embedding unsafe content in minimal frames to evade detection. While prior research has focused on visual cues and advanced such fine-grained detection, audio features remain underexplored. In this study, we embed audio cues with visual for fine-grained child harmful c…