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Arxiv
1d
169
Image Credit: Arxiv
Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition Systems
- The paper discusses the issue of user privacy in automatic speech recognition (ASR) systems.
- It proposes a framework called AudioShield for protecting live users' speech against ASR systems.
- Central to the framework is the concept of Transferable Universal Adversarial Perturbations in the Latent Space (LS-TUAP).
- Comprehensive evaluation demonstrates the superiority of AudioShield in protecting user privacy and improving audio quality.
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Arxiv
1d
256
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Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals
- ML systems continue to harm marginalized people despite efforts to mitigate biases.
- Creating models to identify biased language instead of removing biases is a more feasible approach.
- Limitations of ML in identifying bias include its contextual nature and potential to privilege or oppress different communities.
- Expanding ML approaches to bias and fairness is necessary to address the limitations and promote a mixed-methods investigation.
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Arxiv
1d
120
Image Credit: Arxiv
CrackSQL: A Hybrid SQL Dialect Translation System Powered by Large Language Models
- CrackSQL is a hybrid SQL dialect translation system designed to enable seamless interaction across different database systems.
- It combines rule-based and large language model (LLM)-based methods to improve translation accuracy and overcome challenges such as syntactic discrepancies and semantic variations.
- CrackSQL leverages LLMs to minimize manual intervention and introduces a cross-dialect syntax embedding model for precise syntax alignment.
- It offers multiple deployment and access options, including a web console interface, a PyPI package, and a command-line prompt.
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Arxiv
1d
229
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Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks
- This paper presents TransNet, a new method for transfer learning in community detection of network data.
- TransNet aims to improve the clustering performance of the target network by utilizing auxiliary source networks that are privacy-preserved and locally stored across various sources.
- To achieve privacy preservation, the edges of each locally stored network are perturbed using the randomized response mechanism, ensuring differential privacy.
- By proposing an adaptive weighting method and regularization technique, TransNet effectively aggregates the eigenspaces of the source networks, incorporating the effects of privacy and heterogeneity.
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Arxiv
1d
176
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Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration
- Researchers have proposed a surrogate model called Explorable INR.
- Explorable INR is an implicit neural representation-based model.
- It enables efficient spatial and parameter exploration.
- The model reduces computation and memory costs while providing effective ensemble analysis.
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Arxiv
1d
184
Image Credit: Arxiv
Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents
- Agent S2 is a compositional generalist-specialist framework for computer use agents.
- It automates digital tasks by interacting with graphical user interfaces (GUIs) on computers and mobile devices.
- Agent S2 overcomes challenges such as imprecise grounding and long-horizon task planning.
- It establishes new state-of-the-art performance on prominent computer use benchmarks.
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Arxiv
1d
165
Image Credit: Arxiv
AI Judges in Design: Statistical Perspectives on Achieving Human Expert Equivalence With Vision-Language Models
- Recent advances in vision-language models (VLMs) offer the potential to automate design assessments, but it is crucial to ensure that these AI ``judges'' perform on par with human experts.
- A statistical framework has been introduced to determine whether an AI judge's ratings match those of human experts in design evaluation.
- The top-performing AI judge using text- and image-based in-context learning achieves expert-level agreement for uniqueness and drawing quality and outperforms or matches trained novices across all metrics.
- Reasoning-supported VLM models can achieve human-expert equivalence in design evaluation, impacting design evaluation in education and practice.
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Arxiv
1d
282
Image Credit: Arxiv
QSViT: A Methodology for Quantizing Spiking Vision Transformers
- Vision Transformer (ViT)-based models have shown state-of-the-art performance in vision-based AI tasks.
- Spiking Vision Transformer (SViT)-based models have emerged as low-power ViT networks.
- QSViT is a methodology to compress SViT models through quantization.
- QSViT achieves memory and power savings while maintaining high accuracy.
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Arxiv
1d
225
Image Credit: Arxiv
SentenceKV: Efficient LLM Inference via Sentence-Level Semantic KV Caching
- Efficient LLM inference can be achieved through SentenceKV, a novel sentence-level semantic KV caching approach.
- SentenceKV addresses the limitations of traditional token-level caching methods by considering semantic relationships between tokens.
- By compressing sentence representations into concise semantic vectors, stored on the GPU, SentenceKV reduces memory overhead and improves computational efficiency.
- Extensive evaluations show that SentenceKV outperforms existing methods in terms of efficiency, memory usage, and model accuracy.
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Arxiv
1d
338
Image Credit: Arxiv
When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning
- Scaling test-time compute has emerged as a key strategy for enhancing the reasoning capabilities of large language models (LLMs), particularly in tasks like mathematical problem-solving.
- Recent advancements in Generative Reward Models (GenRM) reframe verification as a next-token prediction task, enabling inference-time scaling along a new axis.
- However, the evaluation shows that Self-Consistency (SC) is more compute-efficient than GenRM for most practical inference budgets across diverse models and datasets.
- The work provides practical guidance on optimizing test-time scaling by balancing solution generation and verification.
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Arxiv
1d
346
Image Credit: Arxiv
Data-Driven Safety Verification using Barrier Certificates and Matrix Zonotopes
- Ensuring safety in cyber-physical systems (CPSs) is a critical challenge when system models are difficult to obtain or cannot be fully trusted.
- A data-driven safety verification framework is proposed that leverages matrix zonotopes and barrier certificates to verify system safety directly from noisy data.
- Instead of relying on a single unreliable model, a set of models is constructed that captures all possible system dynamics aligning with the observed data.
- The model set is compactly represented using matrix zonotopes for efficient computation and propagation of uncertainty, resulting in rigorous safety guarantees without requiring an explicit system model.
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Arxiv
1d
233
Image Credit: Arxiv
Designing Heterogeneous GNNs with Desired Permutation Properties for Wireless Resource Allocation
- Graph neural networks (GNNs) have been designed for learning a variety of wireless policies, leveraging permutation prior.
- To satisfy complicated permutation properties in wireless policies, heterogeneous GNNs (HetGNNs) are used.
- The paper presents a systematic approach for designing HetGNNs with desired permutation properties.
- Power allocation and hybrid precoding policies are used as examples for validation through simulations.
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14 Likes
Arxiv
1d
256
Image Credit: Arxiv
MSCMNet: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification
- The main challenge in the Visible-Infrared Person Re-Identification (VI-ReID) task lies in how to extract discriminative features from different modalities for matching purposes.
- A Multi-scale Semantic Correlation Mining network (MSCMNet) is proposed to comprehensively exploit semantic features at multiple scales and simultaneously reduce modality information loss during feature extraction.
- The proposed MSCMNet includes three novel components: Multi-scale Information Correlation Mining Block (MIMB), quadruple-stream feature extractor (QFE), and Quadruple Center Triplet Loss (QCT).
- Extensive experiments on various datasets show that MSCMNet achieves high accuracy in Visible-Infrared Person Re-Identification.
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Arxiv
1d
188
Image Credit: Arxiv
ResNLS: An Improved Model for Stock Price Forecasting
- Stock price forecasting is a challenging task with varying degrees of dependencies between stock prices.
- The ResNLS hybrid model improves stock price prediction by emphasizing the dependencies between adjacent stock prices.
- ResNLS, composed of ResNet and LSTM, extracts features and analyzes time series data to capture dependencies.
- ResNLS-5, using closing price data for the previous 5 trading days as input, outperforms baselines with at least a 20% improvement.
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Arxiv
1d
259
Image Credit: Arxiv
SVInvNet: A Densely Connected Encoder-Decoder Architecture for Seismic Velocity Inversion
- Researchers have developed a deep learning-based approach, SVInvNet, for seismic velocity inversion.
- SVInvNet employs a novel architecture with a multi-connection encoder-decoder structure enhanced with dense blocks.
- The model effectively processes time series data and addresses non-linear seismic velocity inversion challenges.
- Despite having fewer parameters, SVInvNet outperforms the baseline model in terms of performance.
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