Explainable Artificial Intelligence (XAI) has emerged as a critical tool for interpreting the predictions of complex deep learning models. While XAI has been increasingly applied in various domains within acoustics, its use in bioacoustics, which involves analyzing audio signals from living organisms, remains relatively underexplored. In this paper, we investigate the vocalizations of a bird species with strong geographic variation throughout its range in North America. Audio recordings were converted into spectrogram images and used to train a deep Convolutional Neural Network (CNN) for classification, achieving an accuracy of 94.8\%. To interpret the model's predictions, we applied both model-agnostic (LIME, SHAP) and model-specific (DeepLIFT, Grad-CAM) XAI techniques. These techniques produced different but complementary explanations, and when their explanations were considered together, they provided more complete and interpretable insights into the model's decision-making. This work highlights the importance of using a combination of XAI techniques to improve trust and interoperability, not only in broader acoustics signal analysis but also argues for broader applicability in different domain specific tasks.

As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures. Relatively simple algorithmic systems already steer human decision-making, such as social media feed algorithms that lead people to unintentionally and absent-mindedly scroll through engagement-optimized content. In this paper, we develop the idea of human agency by integrating philosophical and scientific theories of agency with AI-assisted evaluation methods: using large language models (LLMs) to simulate and validate user queries and to evaluate AI responses. We develop HumanAgencyBench (HAB), a scalable and adaptive benchmark with six dimensions of human agency based on typical AI use cases. HAB measures the tendency of an AI assistant or agent to Ask Clarifying Questions, Avoid Value Manipulation, Correct Misinformation, Defer Important Decisions, Encourage Learning, and Maintain Social Boundaries. We find low-to-moderate agency support in contemporary LLM-based assistants and substantial variation across system developers and dimensions. For example, while Anthropic LLMs most support human agency overall, they are the least supportive LLMs in terms of Avoid Value Manipulation. Agency support does not appear to consistently result from increasing LLM capabilities or instruction-following behavior (e.g., RLHF), and we encourage a shift towards more robust safety and alignment targets.

Despite its significance, the underwater world remains largely overlooked as a result of the challenging conditions that hinder traditional research methods. Historically, the study of marine ecosystems relied on labor intensive research [1], which provided limited data and had a high error margin. In recent years, advances in autonomous and remotely operated vehicles (AUVs and ROVs) have revolutionized underwater exploration. These technologies, equipped with object detection systems, now allow real-time monitoring, which includes capturing images of marine organisms, environmental conditions, and even assessing biodiversity [2], [3]. However, the quality of images and videos captured underwater remains a significant obstacle. Light absorption, scattering, and water-related distortions, such as haze and color shifts [4], create noisy low-contrast images, further compounded by complex underwater backgrounds and camera motion. These challenges call for advanced detection techniques capable of accurately identifying and localizing objects despite underwater noise. Efficient underwater object detection (UOD) is crucial for a variety of marine applications, including biodiversity monitoring, conservation efforts, and resource management.

Audio and music generation systems have been remarkably developed in the music information retrieval (MIR) research field. The advancement of these technologies raises copyright concerns, as ownership and authorship of AI-generated music (AIGM) remain unclear. Also, it can be difficult to determine whether a piece was generated by AI or composed by humans clearly. To address these challenges, we aim to improve the accuracy of AIGM detection by analyzing the structural patterns of music segments. Specifically, to extract musical features from short audio clips, we integrated various pre-trained models, including self-supervised learning (SSL) models or an audio effect encoder, each within our suggested transformer-based framework. Furthermore, for long audio, we developed a segment transformer that divides music into segments and learns inter-segment relationships. We used the FakeMusicCaps and SONICS datasets, achieving high accuracy in both the short-audio and full-audio detection experiments. These findings suggest that integrating segment-level musical features into long-range temporal analysis can effectively enhance both the performance and robustness of AIGM detection systems.

As a result of continuous advances in Music Information Retrieval (MIR) technology, generating and distributing music has become more diverse and accessible. In this context, interest in music intellectual property protection is increasing to safeguard individual music copyrights. In this work, we propose a system for detecting music plagiarism by combining various MIR technologies. We developed a music segment transcription system that extracts musically meaningful segments from audio recordings to detect plagiarism across different musical formats. With this system, we compute similarity scores based on multiple musical features that can be evaluated through comprehensive musical analysis. Our approach demonstrated promising results in music plagiarism detection experiments, and the proposed method can be applied to real-world music scenarios. We also collected a Similar Music Pair (SMP) dataset for musical similarity research using real-world cases. The dataset are publicly available.

The proliferation of online misinformation videos poses serious societal risks. Current datasets and detection methods primarily target binary classification or single-modality localization based on post-processed data, lacking the interpretability needed to counter persuasive misinformation. In this paper, we introduce the task of Grounding Multimodal Misinformation (GroundMM), which verifies multimodal content and localizes misleading segments across modalities. We present the first real-world dataset for this task, GroundLie360, featuring a taxonomy of misinformation types, fine-grained annotations across text, speech, and visuals, and validation with Snopes evidence and annotator reasoning. We also propose a VLM-based, QA-driven baseline, FakeMark, using single- and cross-modal cues for effective detection and grounding. Our experiments highlight the challenges of this task and lay a foundation for explainable multimodal misinformation detection.

FBI under pressure over open airport five miles from Charlie Kirk assassination hit as private jet'vanishes' after shooting MSNBC analyst Matthew Dowd fired over'disgusting' on-air comments about Charlie Kirk shortly after conservative star was assassinated Elite sniper breaks down Charlie Kirk assassin's sick plot... and reveals tiny detail everyone's missed: The gun. MAUREEN CALLAHAN: Charlie Kirk's body wasn't even cold... before the fighting started again. Do these ghouls not see where this is headed? Charlie Kirk's powerful tribute to murdered Ukrainian refugee hours before his own assassination: 'America will never be the same' Musk dethroned as richest person by forgotten Wall Street darling's founder as stock soars 42% Charlie Kirk dead at 31: What we know so far about MAGA star's death at Utah campus that sent shockwaves around the world as FBI botches arrest and Trump promises ultimate punishment TMZ forced to apologize after staff heard erupting in laughter as Charlie Kirk's death was announced Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season Trump issues Oval Office address over Charlie Kirk's assassination: 'This is a dark moment for America' Fierce debate erupts over'non-human' technology in space after video captures UFO surviving Hellfire strike Is this Charlie Kirk's killer? This Oscar-nominated actress, 68, will soon reunite with her ex in Spain for their daughter's wedding, can you guess who?

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Poland has intercepted Russian drones that were flying over its airspace after completing a mission in western Ukraine. It's the first time a NATO member nation has fired shots in Russia's war on Ukraine.

One of Chantal Akerman's Best Films Is in Legal Limbo The Belgian-born director's 1994 coming-of-age masterwork, about a precocious teen-ager's romantic audacity, can't be reissued because of its needle drops. Much of direction is production: the material conditions under which a movie is made plays a major role in the creative process. Movie lovers tend to think of producers as dictators of formulas, oppressors of originality, the enemies of art, but that just reflects the unfortunate history of studio filmmaking in Hollywood and elsewhere. In fact, producing a movie can be a kind of art in itself, a practical imagining of possibilities for filmmakers that they wouldn't themselves have come up with. The complete retrospective of Chantal Akerman's work that runs at from September 11th to October 16th includes a superb instance of this phenomenon--of visionary production fostering directorial artistry--in her "Portrait of a Young Girl at the End of the 60s in Brussels," an hour-long movie from 1994.

After fifty years of books, games and movies, what more could the Aliens franchise deliver? The description "genre-defying" gets thrown around a lot these days - it is a convenient sticking plaster for any film or series that hasn't quite figured out what it wants to be. That said, it is an apt term for the Alien franchise. Ridley Scott's 1979 movie Alien, in which Ellen Ripley (Sigourney Weaver) is part of a crew trapped on a spaceship with a salivating, scorpion-like "xenomorph", had such blood-curdling visuals that it made an indelible impact on both science fiction and horror films. But while the deadly parasite and its psychosexual torment were ever present, subsequent instalments tried their hand at being everything from a blockbuster to a prison flick to a philosophical drama.