1,720,964 research outputs found
Multi-Agent Reinforcement Learning for Distributed Workflow Orchestration at the Tactical Edge
No full textThe dynamic nature of tactical edge networks has led to the design of architectures that enable real-time data processing and analytics at the edge, to ensure the continuation of operations when the connection to the headquarters is unavailable. However, workflow orchestration faces unique challenges over frequently disconnected, intermittent, and limited (DIL) networks, where traditional approaches, mainly developed for cloud-like environments, lack the flexibility to react promptly to ever-changing conditions. This paper presents a novel decentralized partially observable Markov decision process (DEC-POMDP) formulation for the distributed workflow orchestration problem, where agents need to cooperate to maximize the computation efficiency while reducing the data transmission time. We propose a solution based on multi-agent reinforcement learning (MARL) that leverages graph convolutional reinforcement learning (DGN) and graph attention networks (GAT) to enable agents to share information with each other, capture the network's structural information, ensure scalability, and eliminate the needs for global knowledge of the network. Training and experiments, which compare our solution with the corresponding constraint satisfaction problem (CSP), are conducted in a simulated 2D urban scenario that mimics nodes' mobility and communications, showing promising results
RoamML: Distributed Machine Learning at the Tactical Edge
No full textMachine learning is an effective methodology for enabling real-time data-driven decision-making in tactical scenarios, but its application in such scenarios raises many challenges due to data volume, unpredictable connectivity, and infrastructural challenges in edge environments. Furthermore, the need to perform training operations on remote powerful computing nodes might not be suited for tactical edge networks that often lack high bandwidth links, thus causing critical delays in assessing relevant information for decision-making. To overcome these challenges and enable machine learning at the tactical edge, this paper presents RoamML, a novel distributed continual learning approach tailored explicitly for the tactical edge. Built upon the foundational principle that “moving the model is usually much cheaper than transferring extensive datasets”, RoamML seamlessly performs training operations by traversing network nodes, according to the data gravity concept. As RoamML encounters new data at each node, it continually trains on the encountered data to ensure that RoamML maintains an up-to-date and accurate model without transferring data to a centralized entity. Experimental results comparing RoamML with a baseline centralized machine learning solution show the potential of the proposed approach, which is capable of closely matching the accuracy of the baseline method
Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments
No full textConventional Congestion Control (CC) algorithms, such as TCP Cubic, struggle in tactical environments as they misinterpret packet loss and fluctuating network performance as congestion symptoms. Recent efforts, including our own MARLIN, have explored the use of Reinforcement Learning (RL) for CC, but they often fall short of generalization, particularly in competitive, unstable, and unforeseen scenarios. To address these challenges, this paper proposes an RL framework that leverages an accurate and parallelizable emulation environment to reenact the conditions of a tactical network. We also introduce refined RL formulation and performance evaluation methods tailored for agents operating in such intricate scenarios. We evaluate our RL learning framework by training a MARLIN agent in conditions replicating a bottleneck link transition between a Satellite Communication (SATCOM) and an UHF Wide Band (UHF) radio link. Finally, we compared its performance in file transfer tasks against Transmission Control Protocol (TCP) Cubic and the default strategy implemented in the Mockets tactical communication middleware. The results demonstrate that the MARLIN RL agent outperforms both TCP and Mockets under different perspectives and highlight the effectiveness of specialized RL solutions in optimizing CC for tactical network environments
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Deep Reinforcement Learning for Communication Networks
No full textThis research explores optimizing communication tasks with (Multi-Agent) Reinforcement Learning (RL/MARL) in Point-to-Point and Group Communication (GC) networks. The study initially applied RL for Congestion Control in networks with dynamic link properties, yielding competitive results. Then, it focused on the challenge of effective message dissemination in GC networks, by framing a novel game-theoretic formulation and designing methods to solve the task based on MARL and Graph Convolution. Future research will deepen the exploration of MARL in GC. This will contribute to both academic knowledge and practical advancements in the next generation of communication protocols
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
