Challenges to the End-to-End Internet Model

In 1981 Saltzer, Reed, and Clark identified “end-to-end” principles related to the design of modern layered protocols. The Internet today is not as transparent as envisioned by [SALTZER81]. While most of the intelligence remains concentrated in end-systems, users are now deploying more sophisticated processing within the network for a variety of reasons including security, network management, E-commerce, and survivability. Applications and application-layer protocols have been found to interact in unexpected ways with this new intelligent software within the network such as proxies, address translators, packet filters, intrusion detection, and differentiated service functions. In this paper we survey examples of the problems caused by the introduction of this new processing within the network which is counter to the end-to-end Internet model proposed by [SALTZER81]. * 1 2 3 The conflict between the end-to-end Internet model and the introduction of new processing within the network is being addressed on a case-by-case basis in each development effort. There are no indications that new devices installed within the network (which break the end-to-end model) will disappear and in fact there has been dramatic growth in their implementation due to recent denial-ofservice attacks. Transition to IPv6 only solves a subset of these issues, and its deployment is proceeding slowly. Future work is obviously needed to create a consistent environment for protocol development that preserves the transparency provided by the end-to-end Internet model

Uso de chat GPT y pensamiento crítico en estudiantes de educación física de una universidad pública de Junín, 2024

El presente trabajo académico esta alineado al Objetivo de Desarrollo sostenible 4: “Educación de calidad”, el mismo que busca determinar la correlación entre el uso del Chat GPT y el pensamiento crítico en estudiantes universitarios de Educación Física de una universidad publica de Junín. El estudio adopta un enfoque cuantitativo con un diseño correlacional, con una muestra de 155 sujetos elegidos mediante el muestreo probabilístico. Los resultados en la variable uso de Chat GPT demuestra que la mayoría (56%) se encuentra en un nivel medio, asimismo en la variable del nivel de pensamiento crítico se evidencia que la mayoría (56%) se encuentra en un nivel medio, todo ello corrobora que existe una correlación positiva y significativa (Rho de Spearman = 0,999) entre el uso del Chat GPT y el pensamiento crítico. Se observa una mejora en las habilidades o dimensiones de análisis, evaluación, razonamiento inferencial, solucion de problemas y autorregulción. La conclusión principal destaca el potencial del Chat GPT como herramienta educativa eficaz para fortalecer el pensamiento crítico, siempre que su uso esté acompañado de orientación pedagógica adecuada

Robust Motion In-betweening

In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial recurrent neural networks. The system synthesizes high-quality motions that use temporally-sparse keyframes as animation constraints. This is reminiscent of the job of in-betweening in traditional animation pipelines, in which an animator draws motion frames between provided keyframes. We first show that a state-of-the-art motion prediction model cannot be easily converted into a robust transition generator when only adding conditioning information about future keyframes. To solve this problem, we then propose two novel additive embedding modifiers that are applied at each timestep to latent representations encoded inside the network's architecture. One modifier is a time-to-arrival embedding that allows variations of the transition length with a single model. The other is a scheduled target noise vector that allows the system to be robust to target distortions and to sample different transitions given fixed keyframes. To qualitatively evaluate our method, we present a custom MotionBuilder plugin that uses our trained model to perform in-betweening in production scenarios. To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation. We are releasing this new dataset along with this work, with accompanying code for reproducing our baseline results.Comment: Published at SIGGRAPH 202