Zobrazeno 1 - 10
of 3 394
pro vyhledávání: '"Near A"'
Publikováno v:
Jurnal Manajemen Dan Kewirausahaan, Vol 12, Iss 1, Pp 21-31 (2024)
This study aims to analyze the mediating role of perceived organizational support and moderating role of self-efficacy in the influence of employee green behavior on environmental performance sustainability. The method used in the study was quantitat
Externí odkaz:
https://doaj.org/article/c0d0abdb60644282bf09b979a57f8f92
Differentially private SGD (DPSGD) enables privacy-preserving training of language models, but often reduces utility, diversity, and linguistic quality. We introduce DPRefine, a three-phase method that initializes a model using data synthesis from a
Externí odkaz:
http://arxiv.org/abs/2410.17566
The miniKanren and Relational Programming Workshop is a workshop for the miniKanren family of relational (pure constraint logic programming) languages: miniKanren, microKanren, core.logic, OCanren, Guanxi, etc. The workshop solicits papers and talks
Externí odkaz:
http://arxiv.org/abs/2409.06505
Autor:
Skalka, Christian, Near, Joseph P.
Secure Multi-Party Computation (MPC) is an important enabling technology for data privacy in modern distributed applications. Currently, proof methods for low-level MPC protocols are primarily manual and thus tedious and error-prone, and are also non
Externí odkaz:
http://arxiv.org/abs/2407.16504
Choreographic programming is a concurrent paradigm in which a single global program called a choreography describes behavior across an entire distributed network of participants. Choreographies are easier to reason about than separate programs runnin
Externí odkaz:
http://arxiv.org/abs/2406.13716
Autor:
Bates, Mako, Near, Joseph P.
Concurrent distributed systems are notoriously difficult to construct and reason about. Choreographic programming is a recent paradigm that describes a distributed system in a single global program called a choreography. Choreographies simplify reaso
Externí odkaz:
http://arxiv.org/abs/2403.05417
Autor:
Bates, Mako, Near, Joseph P.
Formal methods for guaranteeing that a protocol satisfies a cryptographic security definition have advanced substantially, but such methods are still labor intensive and the need remains for an automated tool that can positively identify an insecure
Externí odkaz:
http://arxiv.org/abs/2403.04991
We adapt the architectures of previous audio manipulation and generation neural networks to the task of real-time any-to-one voice conversion. Our resulting model, LLVC ($\textbf{L}$ow-latency $\textbf{L}$ow-resource $\textbf{V}$oice $\textbf{C}$onve
Externí odkaz:
http://arxiv.org/abs/2311.00873
Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in real-world datasets and systems remains challenging. Recently developed DP tools aim to make DP implementation easier, but limited res
Externí odkaz:
http://arxiv.org/abs/2309.13506
Recent secure aggregation protocols enable privacy-preserving federated learning for high-dimensional models among thousands or even millions of participants. Due to the scale of these use cases, however, end-to-end empirical evaluation of these prot
Externí odkaz:
http://arxiv.org/abs/2302.10084