Search · four archives
Search · four archives
15 papers · ranked by Valyu relevance
Alessandro Margara, Gianpaolo Cugola, Nicolò Felicioni, Stefano Cilloni
'Stefano Cilloni'] Data is a precious resource in today's society, and is generated at an unprecedented and constantly growing pace. The need to store, analyze, and make data promptly available to a multitude of users introduces formidable challenges in modern software platforms. These challenges radically impacted the…
Aneesh Khole, Atharva Thakar, Avadhoot Kulkarni, Hrithik Jadhav + 2 more
'Shreyas Shende' 'Varad Karajkhede'] Abstract— Computer systems have evolved over the years starting from sizable, single-user, slow, and expensive machines to multi-user, fast, cheaper, and small-sized machines. The use of multi-user computer networks has given rise to a new paradigm of computing known as Distributed…
Tsolak Ghukasyan, Vahagn Altunyan, Aram Bughdaryan, Tigran Aghajanyan + 3 more
This paper presents the Smart Distributed Data Factory (SDDF), an AI-driven distributed computing platform designed to address challenges in drug discovery by creating comprehensive datasets of molecular conformations and their properties. SDDF uses volunteer computing, leveraging the processing power of personal…
Wei Geng, Yulong Zhang, Dirk Kutscher, Abhishek Kumar + 2 more
'Pan Hui'] Information-Centric Networking (ICN), with its data-oriented operation and generally more powerful forwarding layer, provides an attractive platform for distributed computing. This paper provides a systematic overview and categorization of different distributed computing approaches in ICN encompassing…
Derya Malak, Mohammad Reza Deylam Salehi, Berksan Serbetci, Petros Elia + 2 more
'Petros Elia' 'Chintha Tellambura' 'Jun Chen'] The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably…
Patrick Finnerty, Yoshiki Kawanishi, Tomio Kamada, Chikara Ohta
In this article we present our relocatable distributed collections library. Building on top of the AGPAS for Java library, we provide a number of useful intra-node parallel patterns as well as the features necessary to support the distributed nature of the computation through clearly identified methods. In particular…
Tiffany Leung, Alissa Brauneck, Predrag Mitrovic, Aiguo Wang + 41 more
Background Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL)…
Nico Weber, Patrick Holzer, Tania Jacob, Enislay Ramentol
Federated Learning as a decentralized artificial intelligence (AI) solution solves a variety of problems in industrial applications. It enables a continuously self-improving AI, which can be deployed everywhere at the edge. However, bringing AI to production for generating a real business impact is a challenging task.…
Cláudia Brito, Pedro Ferreira, João Paulo
Breakthroughs in sequencing technologies led to an exponential growth of genomic data, providing unprecedented biological in-sights and new therapeutic applications. However, analyzing such large amounts of sensitive data raises key concerns regarding data privacy, specifically when the information is outsourced to…
Maxim Lippeveld, Daniel Peralta, Andrew Filby, Yvan Saeys
Due to high resolution and throughput of modern image cytometry platforms, morphologically profiling generated datasets poses a significant computational challenge. Here, we present Scalable Cytometry Image Processing (SCIP), an image processing software aimed at running on distributed high performance computing…
Christian Hausleitner, Heimo Mueller, Andreas Holzinger, Bastian Pfeifer
'Bastian Pfeifer'] The authors introduce a novel framework that integrates federated learning with Graph Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop methodologies. This advanced framework innovatively employs collaborative voting mechanisms on subgraphs within a Protein-Protein…
Raphaël Robidas, Claude Legault
Computational chemistry is an increasingly active field due to the improvement of computing resources and theoretical tools. However, its use remains usually limited to technically-inclined users due to the technical challenges of preparing, launching and analyzing calculations. In this context, we have developed…
Haftay Gebreslasie Abreha, Mohammad Hayajneh, Mohamed Adel Serhani, Matteo Anedda + 1 more
Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share…
Jing Chen, Lyuzhou Gao, Hongquan Sun, Siquan Yang + 4 more
'Yuting Wan' 'Kedi Wang' 'Len Gelman'] Computing power network (CPN) is designed to utilize multi-dimensional resources to complete computing tasks. However, in practical applications, the CPN architecture has difficulty in coordinating cross-domain heterogeneous resources, making it impossible to achieve the real-time…
Authors not listed
Machine learning models are transforming data-driven research across scientific disciplines, yet their deployment as accessible and reliable web services remains a significant challenge. We introduce the NERDD framework, a scalable, maintainable, and secure microservices platform designed to support the sustainable…