On-site, live video streaming is an important application that many depend on to receive entertainment and local and international news. Streaming live video over wireless (e.g., 3G, LTE, 5G) has begun to replace television production vans that use direct microwave links to the television station since they offer greater physical flexibility and have shorter setup times. In this method, several links are often aggregated together to stream video. Efficiently using multiple links for live, adaptive video streaming is challenging because a protocol must determine the amount of data to send on each link, the video bitrate, and the appropriate level of redundancy so that video frames can be delivered within the user’s strict latency requirements. In this paper, we present Conflux: a multi-homed, adaptive video bitrate protocol for live video streaming. Conflux’s main contribution is the design of a modular live video streaming platform that supports multipath scheduling, video bitrate adaption, and adaptive Forward Error Correction. Conflux presents a probabilistic link quality model that is used in conjunction with a user-specific utility function to determine the video bitrate and redundancy levels that maximize the user’s expected utility. These models are contained in separate modules which serve as building blocks to create customized, multi-homed adaptive video bitrate protocols for users with different requirements. Our experimental results show that Conflux provides more than 22% improvement in video quality over other multi-homed systems for typical, two-link network environments and as much as 108% improvement in more challenging network environments with up to five links.

The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust, efficient, and generalizable communication frameworks. However, integrating these frameworks with existing wireless systems and effectively managing noise and bit errors pose significant challenges. In this work, we introduce a practical on-device AI communication framework, integrated with physical layer (PHY) communication functions, demonstrated through its performance on a link-level simulator. Our framework incorporates end-to-end training with channel noise to enhance resilience, incorporates vector quantized variational autoencoders (VQ-VAE) for efficient and robust communication, and utilizes pre-trained encoder-decoder transformers for improved generalization capabilities. Simulations, across various communication scenarios, reveal that our framework achieves a 50% reduction in transmission size while demonstrating substantial generalization ability and noise robustness under standardized 3GPP channel models.

This paper describes our pathloss prediction system submitted to the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. We describe the architecture of PMNet, a neural network we specifically designed for pathloss prediction. Moreover, to enhance the prediction performance, we apply several machine learning techniques, including data augmentation, fine-tuning, and optimization of the network architecture. Our system achieves an RMSE of 0.02569 on the provided RadioMap3Dseer dataset, and 0.0383 on the challenge test set, placing it in the 1st rank of the challenge.

Recognizing software entities such as library names from free-form text is essential to enable many software engineering (SE) technologies, such as traceability link recovery, automated documentation, and API recommendation. While many approaches have been proposed to address this problem, they suffer from small entity vocabularies or noisy training data, hindering their ability to recognize software entities mentioned in sophisticated narratives. To address this challenge, we leverage the Wikipedia taxonomy to develop a comprehensive entity lexicon with 79K unique software entities in 12 fine-grained types, as well as a large labeled dataset of over 1.7M sentences. Then, we propose self-regularization, a noise-robust learning approach, to the training of our software entity recognition (SER) model by accounting for many dropouts. Results show that models trained with self-regularization outperform both their vanilla counterparts and state-of-the-art approaches on our Wikipedia benchmark and two Stack Overflow benchmarks. We release our models11https://huggingface.co/taidng/wikiser-bert-base; https.//huggingface.co/taidng/wikiser-bert-large., data, and code for future research.22https://github.com/taidnguyen/software_entity_recognition

Abstract Clusters, composed of symmetric multiprocessor (SMP) machines and heterogeneous machines, have become increasingly popular for high‐performance computing. Message‐passing libraries, such as message‐passing interface (MPI) and parallel virtual machine (PVM), arede factoparallel programming libraries for clusters that usually consist of homogeneous and uni‐processor machines. For SMP machines, MPI is combined with multithreading libraries like POSIX Thread and OpenMP to take advantage of the architecture. In addition to existing parallel programming libraries that are in C/C++ and FORTRAN programming languages, the Java programming language presents itself as another alternative with its object‐oriented framework, platform neutral byte code, and ever‐increasing performance. This paper presents a new parallel programming model and a library, VCluster, which implements this model. VCluster is based on migrating virtual threads instead of processes to support clusters of SMP machines more efficiently. The implementation uses thread migration, which can be used in dynamic load balancing. VCluster was developed in pure Java, utilizing the portability of Java to support clusters of heterogeneous machines. Several applications are developed to illustrate the use of this library and compare the usability and performance of VCluster with other approaches. Copyright © 2007 John Wiley & Sons, Ltd.

This paper explores the notion of computational resiliency to provide reliability in heterogeneous distributed applications. This notion provides both software fault-tolerance and the ability to tolerate information-warfare attacks. This technology seeks to strengthen a military mission, rather than to protect its network infrastructure using static defense measures such as network security, intrusion sensors, and firewalls. Even if a failure or attack is successful and never detected, it should be possible to continue information operations and achieve mission objectives. Computational resiliency involves the dynamic use of replicated software structures, guided by mission policy, to achieve reliable operation. However, it goes further to regenerate, automatically, replication in response to a failure or attack, allowing the level of system reliability to be restored and maintained. This paper examines a prototype concurrent programming technology to support computational resiliency in a heterogeneous distributed computing environment. The performance of the technology is explored through two example applications.

NYNET (ATM wide area network testbed in New York state) Communication System (NCS) is a multithreaded message passing system developed at Syracase University that provides high performance and flexible communication services over asynchronous transfer mode (ATM) based high performance distributed computing (HPDC) environments. NCS capitalizes on thread based programming model to overlap computations and communications, and develop a dynamic message passing environment with separate data and control paths. This leads to a flexible and adaptive message passing environment that can support multiple flow control, error control, and multicasting algorithms. We provide an overview of the NCS architecture and present how NCS point to point communication services are implemented. We also analyze the overhead incurred by using multithreading and compare the performance of NCS point to point communication primitives with those of other message passing systems such as p4, PVM, and MPI. Benchmarking results indicate that NCS shows comparable performance to other systems for small message sizes but outperforms other systems for large message sizes.