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检索条件"机构=Computer Science and Engineering Uc San Diego"

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FUTGA: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation 3

FUTGA: Towards Fine-grained Music Understanding through Temp...

引用

3rd Workshop on NLP for Music and Audio, NLP4MusA 2024, in conjunction with ISMIR 2024

作者： Wu, Junda Novack, Zachary Namburi, Amit Dai, Jiaheng Dong, Hao-Wen Xie, Zhouhang Chen, Carol McAuley, Julian Computer Science and Engineering UC San Diego United States Computer Science Department UC Los Angeles United States

We propose FUTGA, a model equipped with fined-grained music understanding capabilities through learning from generative augmentation with temporal compositions. We leverage existing music caption datasets and large language models (LLMs) to synthesize fine-grained music captions with structural descriptions and time boundaries for full-length songs. Augmented by the proposed synthetic dataset, FUTGA is enabled to identify the music’s temporal changes at key transition points and their musical functions, as well as generate detailed descriptions for each music segment. We further introduce a full-length music caption dataset generated by FUTGA, as the augmentation of the MusicCaps and the Song Describer datasets. The experiments demonstrate the better quality of the generated captions, which capture the time boundaries of long-form music. Generated temporal-aware music descriptions are illustrated in our demonstration https://***/***/. © 2024 NLP4MusA. All Rights Reserved.

关键词： Large datasets

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FUTGA-MIR: Enhancing Fine-grained and Temporally-aware Music Understanding with Music Information Retrieval

FUTGA-MIR: Enhancing Fine-grained and Temporally-aware Music...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Junda Novack, Zachary Namburi, Amit Dong, Hao-Wen Chen, Carol Dai, Jiaheng McAuley, Julian Computer Science and Engineering UC San Diego United States Computer Science Department UC Los Angeles United States

ISBN: (纸本)9798350368741

Recent music large language models (music LLMs) have shown great potential in music understanding through large-scale multimodal pre-training. While some existing music LLMs have been augmented with temporally-aware music captions, music information retrieval (MIR) features conventionally do not exist in music caption datasets, thus neglected by music-LLMs. To bridge the gap between recent music LLMs and conventional music information retrieval tasks, we propose FUTGA-MIR (Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation with Music Information Retrieval) to enhance the existing music LLMs by augmenting them with MIR features and aligning with human feedback. We calibrate the original positional distribution of music clips in the created pre-training synthetic music captions, conditioned on MIR features. The redistributed synthetic dataset better respects realistic MIR distributions of music and is tightly aligned with realistic music captions as well. In addition, We incorporate human-annotated music captions and MIR features from the Harmonixset dataset for an additional fine-tuning step, which addresses sim-to-real domain gaps and improves performance on realistic music tracks. We further evaluate FUTGA-MIR on several downstream tasks, including music classification, retrieval, and generation, and demonstrate the versatile capacities of FUTGA-MIR and better generation quality compared with previous music captioning models. Generated temporal-aware music descriptions are illustrated in our demonstration https://***/***/. © 2025 IEEE.

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(Invited Paper) White-box logic obfuscation: A Transparent Solution to Hardware Piracy and Reverse engineering 30

(Invited Paper) White-box logic obfuscation: A Transparent S...

引用

30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Li, Leon Orailoglu, Alex Department of Computer Science and Engineering Uc San Diego La Jolla CA United States

ISBN: (纸本)9798400706356

Reverse engineering the functional specification from a netlist is a challenging task that enables IP piracy and tampering. Traditional logic locking techniques, which depend on external activation with secrets stored in tamper-proof locations to thwart reverse engineering, have repeatedly been compromised by key recovery attacks. Their vulnerability highlights the flawed assumption of relying on tamper-proof secrets for building hardware security solutions, especially when activated devices are deployed in open environments where they are exposed to attackers for functional queries and probing. This paper presents white-box logic obfuscation (WBLO) as a novel solution to safeguard control logic from functional reverse engineering, even when attackers have full visibility of the operational netlist. WBLO eliminates the need for post-manufacturing activation and reliance on tamper-proof key storage by securing the design with keys that are autonomously updated internally through the legitimate sequential execution of the device. The proposed approach invalidates functional analysis under arbitrary probing and combinational queries. We examine the implementation challenges inherent in the WBLO process and identify critical design considerations that enhance security and efficiency. Building on these insights, we suggest a prioritization for various design transformation and synthesis rules that achieve robust security in the white-box attack model while minimizing implementation overheads. © 2025 Copyright held by the owner/author(s).

关键词： Reverse engineering

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Inexact Augmented Lagrangian Methods for Conic Programs: Quadratic Growth and Linear Convergence 38

Inexact Augmented Lagrangian Methods for Conic Programs: Qua...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liao, Feng-Yi Ding, Lijun Zheng, Yang Department of Electrical and Computer Engineering UC San Diego United States Department of Mathematics UC San Diego United States

Augmented Lagrangian Methods (ALMs) are widely employed in solving constrained optimizations, and some efficient solvers are developed based on this framework. Under the quadratic growth assumption, it is known that the dual iterates and the Karush-Kuhn-Tucker (KKT) residuals of ALMs applied to semidefinite programs (SDPs) converge linearly. In contrast, the convergence rate of the primal iterates has remained elusive. In this paper, we resolve this challenge by establishing new quadratic growth and error bound properties for primal and dual SDPs under the strict complementarity condition. Our main results reveal that both primal and dual iterates of the ALMs converge linearly contingent solely upon the assumption of strict complementarity and a bounded solution set. This finding provides a positive answer to an open question regarding the asymptotically linear convergence of the primal iterates of ALMs applied to semidefinite optimization. © 2024 Neural information processing systems foundation. All rights reserved.

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The Future Is Rosie?: Disempowering Arguments about Automation and What to Do about It 25

The Future Is Rosie?: Disempowering Arguments about Automati...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Riek, Laurel D. Irani, Lilly Computer Science and Engineering UC San Diego San Diego CA United States Department of Communication UC San Diego San Diego CA United States

ISBN: (纸本)9798400713941

Many technologists who work in robotics and AI bristle at the idea that human worker displacement is problematic. Others wish to account for workers' needs, but face pervasive myths about the impacts of these technologies. This paper aims to clear the air by refuting five common arguments for automation: 1) the jobs being automated are undesirable, 2) labor shortages necessitate automation, 3) by "augmenting rather than automating"labor displacement will be prevented, 4) there will be new and better job creation, and 5) automation will give us all more leisure time. The advent of foundational models has led to an industrial gold rush, accelerating deployment without careful consideration of responsible and sustaiable design and deployment of these technologies. Despite technologists' best intentions, this path of pervasive automation we are on is not a good one, and we offer suggestions for how technologists, designers, and decision makers can push for worker-centered technological change moving forward. © 2025 Copyright held by the owner/author(s).

关键词： AI automation critical computing ethics future of work labor robotics social theory

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CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation

CoLLAP: Contrastive Long-form Language-Audio Pretraining wit...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Junda Li, Warren Novack, Zachary Namburi, Amit Chen, Carol McAuley, Julian Computer Science and Engineering UC San Diego La Jolla United States Computer Science Department UC Los Angeles Los Angeles United States

ISBN: (纸本)9798350368741

Modeling temporal characteristics plays a significant role in the representation learning of audio waveform. We propose Contrastive Long-form Language-Audio Pretraining (CoLLAP) to significantly extend the perception window for both the input audio (up to 5 minutes) and the language descriptions (exceeding 250 words), while enabling contrastive learning across modalities and temporal dynamics. Leveraging recent Music-LLMs to generate long-form music captions for full-length songs, augmented with musical temporal structures, we collect 51.3K audio-text pairs derived from the large-scale AudioSet training dataset, where the average audio length reaches 288 seconds. We propose a novel contrastive learning architecture that fuses language representations with structured audio representations by segmenting each song into clips and extracting their embeddings. With an attention mechanism, we capture multimodal temporal correlations, allowing the model to automatically weigh and enhance the final fusion score for improved contrastive alignment. Finally, we develop two variants of the CoLLAP model with different types of backbone language models. Through comprehensive experiments on multiple long-form music-text retrieval datasets, we demonstrate consistent performance improvement in retrieval accuracy compared with baselines. We also show the pretrained CoLLAP models can be transferred to various music information retrieval tasks, with heterogeneous long-form multimodal contexts. © 2025 IEEE.

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What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context on Comfort, Reliance, Satisfaction, and Driving Confidence 25

What Did My Car Say? Impact of Autonomous Vehicle Explanatio...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Kaufman, Robert A Broukhim, Aaron Kirsh, David Weibel, Nadir Cognitive Science University of California San Diego CA United States Computer Science & Engineering University of California San Diego San Diego CA United States Computer Science and Engineering The Design Lab UC San Diego San Diego CA United States

ISBN: (纸本)9798400713941

Explanations for autonomous vehicle (AV) decisions may build trust, however, explanations can contain errors. In a simulated driving study (n = 232), we tested how AV explanation errors, driving context characteristics (perceived harm and driving difficulty), and personal traits (prior trust and expertise) affected a passenger's comfort in relying on an AV, preference for control, confidence in the AV's ability, and explanation satisfaction. Errors negatively affected all outcomes. Surprisingly, despite identical driving, explanation errors reduced ratings of the AV's driving ability. Severity and potential harm amplified the negative impact of errors. Contextual harm and driving difficulty directly impacted outcome ratings and influenced the relationship between errors and outcomes. Prior trust and expertise were positively associated with outcome ratings. Results emphasize the need for accurate, contextually adaptive, and personalized AV explanations to foster trust, reliance, satisfaction, and confidence. We conclude with design, research, and deployment recommendations for trustworthy AV explanation systems. © 2025 Copyright held by the owner/author(s).

关键词： AI Errors Autonomous Vehicles Explainable AI

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QUADRATIC MODELS FOR UNDERSTANDING CATAPULT DYNAMICS OF NEURAL NETWORKS 12

QUADRATIC MODELS FOR UNDERSTANDING CATAPULT DYNAMICS OF NEUR...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhu, Libin Liu, Chaoyue Radhakrishnan, Adityanarayanan Belkin, Mikhail Department of Computer Science UC San Diego United States Halicioglu Data Science Institute UC San Diego United States Harvard & Broad Institute of MIT and Harvard United States

While neural networks can be approximated by linear models as their width increases, certain properties of wide neural networks cannot be captured by linear models. In this work we show that recently proposed Neural Quadratic Models can exhibit the "catapult phase" (Lewkowycz et al., 2020) that arises when training such models with large learning rates. We then empirically show that the behaviour of neural quadratic models parallels that of neural networks in generalization, especially in the catapult phase regime. Our analysis further demonstrates that quadratic models can be an effective tool for analysis of neural networks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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HDXpose: Harnessing Hyperdimensional Computing’s Explainability for Adversarial Attacks 24

HDXpose: Harnessing Hyperdimensional Computing’s Explainabi...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Asgarinejad, Fatemeh Pozina, Flavio Gungor, Onat Rosing, Tajana Aksanli, Baris Department of Computer Science and Engineering UC San Diego La Jolla CA92093 United States Department of Electrical and Computer Engineering SDSU CA92182 United States

ISBN: (纸本)9798400710773

Hyperdimensional Computing (HDC), a promising alternative to address the limitations of edge devices, is not exempt from the security challenges confronted by machine learning algorithms, in particular, adversarial attacks. The limited body of research exploring the security implications of HDC overlooks its inherent algorithm. In this paper, we propose a novel and effective adversarial attack technique targeting HDC. Our approach analyzes and prioritizes the impact of input features as well as encoded elements on decision boundaries and perturbs the input towards incorrect decisions in a guided manner. We evaluate our method on different datasets and attack models (i.e., untargeted/targeted, white-box/gray-box). Experimental results indicate that our proposed design, HDXpose, significantly outperforms the state-of-the-art attack techniques by achieving higher success rate with smaller distortion and execution time, rendering its efficacy for real-time attack generation. © 2024 Copyright is held by the owner/author(s).

关键词： Adversarial machine learning

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Online Consistency of the Nearest Neighbor Rule 38

Online Consistency of the Nearest Neighbor Rule

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dasgupta, sanjoy So, Geelon Department of Computer Science UC San Diego La Jolla CA92023 United States

In the realizable online setting, a learner is tasked with making predictions for a stream of instances, where the correct answer is revealed after each prediction. A learning rule is online consistent if its mistake rate eventually vanishes. The nearest neighbor rule (Fix and Hodges, 1951) is a fundamental prediction strategy, but it is only known to be consistent under strong statistical or geometric assumptions-the instances come i.i.d. or the label classes are well-separated. We prove online consistency for all measurable functions in doubling metric spaces under the mild assumption that the instances are generated by a process that is uniformly absolutely continuous with respect to a finite, upper doubling measure. © 2024 Neural information processing systems foundation. All rights reserved.

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