@arXiv_csCR_bot@mastoxiv.page2026-07-24 08:37:09
Crosslisted article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- TopoGuard: Graph Theory Based Defenses Against Split-Knowledge Attacks on RAG
Chahana Dahal, Zuobin Xiong
https://arxiv.org/abs/2607.20437 https://mastoxiv.page/@arXiv_csCL_bot/116973867316185089
- Isolating LLM Alignment from Regex: Zero Coverage and Metric-Dependent Divergence Under Adversari...
Alexandre Cristov\~ao Maiorano
https://arxiv.org/abs/2607.20494 https://mastoxiv.page/@arXiv_csAI_bot/116973970735647184
- Edit-Neighboring Data Streams and Privacy under Continual Observation
Joel Daniel Andersson, Anamay Chaturvedi, Monika Henzinger, Roodabeh Safavi
https://arxiv.org/abs/2607.20727 https://mastoxiv.page/@arXiv_csDS_bot/116973794572869732
- Beyond Heavy Log Curation: Perplexity-Based APT Detection via Unsupervised, Context-Augmented Lan...
Shoya Otsu, Kei Suzuki, Toshiaki Koike-Akino, Jing Liu, Ye Wang
https://arxiv.org/abs/2607.20832 https://mastoxiv.page/@arXiv_csLG_bot/116973999245670512
- The Consensus Number of Untraceable Cryptocurrencies
Christian Cachin, David Lehnherr, Juan Villacis, Fran\c{c}ois-Xavier Wicht
https://arxiv.org/abs/2607.20929 https://mastoxiv.page/@arXiv_csDC_bot/116973788672146161
- Weak Private Information Retrieval for Graph-based Storage
Shodasakshari Vidya, Chandan Anand, Prasad Krishnan
https://arxiv.org/abs/2607.21014 https://mastoxiv.page/@arXiv_csIT_bot/116973843557672147
- Risk-Limiting Audits for Parliamentary Majorities
Jack Freestone, Dennis Leung, Damjan Vukcevic
https://arxiv.org/abs/2607.21082 https://mastoxiv.page/@arXiv_statAP_bot/116973845690486258
- Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inference
Xiaolong Liang, Juanjuan Li, Rui Qin, Yisheng Lv
https://arxiv.org/abs/2607.21162 https://mastoxiv.page/@arXiv_csLG_bot/116974022251021755
- Advances in STV Margin Computation
Michelle Blom, Alexander Ek, Peter J. Stuckey, Vanessa Teague, Damjan Vukcevic
https://arxiv.org/abs/2607.21178 https://mastoxiv.page/@arXiv_csGT_bot/116973834510813731
- Unconditional Unclonable Encryption
Prabhanjan Ananth, Amit Sahai
https://arxiv.org/abs/2607.21551 https://mastoxiv.page/@arXiv_quantph_bot/116974006325118063
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@matematico314@social.linux.pizza2026-07-07 04:49:11
Nunca tinha parado para pensar que um subanel de um anel com unidade pode ter uma unidade diferente da unidade do anel mãe, pois pode ter um elemento que funcione como neutro multiplicativo apenas para elementos deste subanel. Por exemplo, considere o conjunto S das matrizes reais 2 x 2 em que todos os coeficientes, que não o superior esquerdo, sejam nulos. S é obviamente um subanel do anel das matrizes reais 2 x 2. Mas a sua unidade não é a matriz identidade, e sim o elemento de S cujo coef…
@arXiv_eessAS_bot@mastoxiv.page2026-05-13 07:47:08
Too Good to Be True: A Study on Modern Automatic Speech Recognition for the Evaluation of Speech Enhancement
Danilo de Oliveira, Tal Peer, Timo Gerkmann
https://arxiv.org/abs/2605.12107 https://arxiv.org/pdf/2605.12107 https://arxiv.org/html/2605.12107
arXiv:2605.12107v1 Announce Type: new
Abstract: Speech enhancement (SE) systems are typically evaluated using a variety of instrumental metrics. The use of automatic speech recognition (ASR) systems to evaluate SE performance is common in literature, usually in terms of word error rate (WER). However, WER scores depend heavily on the choice of ASR system and text normalization pipeline. In this paper, we investigate how modern ASR models correlate with human recognition of enhanced speech. A listening experiment reveals that modern ASR models with large-scale noisy training and embedded language models correlate more with human WER than simpler ones, with a transducer model providing the most reliable transcriptions. Nevertheless, we also show that these models' robustness to noise and use of context can be uninformative to an acoustics-focused evaluation of enhancement performance.
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