Large-scale graph processing poses challenges due to its size and irregular memory access patterns, causing performance degradation in common architectures, such as CPUs and GPUs. Recent research includes accelerating graph processing using Field ...

• Analysis of the power-law distribution in Real-World, large-scale graphs and its effect on the graph partitioning.
• Host and FPGA device communication analysis and optimization for large-scale graph computing.
• Investigation in an ...

Geo-distributed Bigdata processing is increasing day by day, resulting in the origins of data that are geographically distributed in different countries and hold datacenters (DCs) across the globe, and also the applications that use different ...

• An heuristic algorithm GResearch is proposed to choose the best clusters as global reducers.
• An heuristic scheduling algorithm Geo-MR is proposed to ensure the scheduling of only the relevant data to selected global reducers that ...

Quantum algorithms are often represented in terms of quantum circuits operating on ideal (logical) qubits. However, the practical implementation of these algorithms poses significant challenges. Many quantum algorithms require a substantial ...

• Quantum error correction adds significant qubit and runtime overhead.
• Our proposed framework compares classical and quantum algorithms effectively.
• The article focuses on quantum algorithms’ impact on cryptographic systems.
• ...

Cloud computing has enabled data-sharing to be more convenient than ever before. However, data security is a major concern that prevents cloud computing from being widely adopted. A potential solution to secure data-sharing in cloud computing is ...

• The paper introduces a certificateless proxy re-encryption scheme (CLPRE-CRF) that allows secure data transmission by transforming encrypted data without accessing the plaintext.
• It incorporates cryptographic reverse firewalls to ...

The utilization of Device-to-Device (D2D) communication among Narrowband Internet of Things (NB-IoT) devices offers significant potential for advancing intelligent healthcare systems due to its superior data rates, low power consumption, and ...

• Developed iRASH: a solution for smart healthcare using NB-IoT devices.
• Integrated D2D and NB-IoT to enhance coverage and connectivity.
• Ensured reliable healthcare data transmission and smart resource allocation.
• Integrated ...

In federated learning, non-iid data not only diminishes the performance of the global model but also gives rise to the fairness problem which manifests as an increase in the variance of the global model’s accuracy across clients. Fairness issues ...

• Introduce FedMC and FedMC+ as methods to mitigate performance degradation and fairness problem.
• Conduct a theoretical analysis of FedMC which converges to Pareto stationary.
• Experiments shows FedMC+ achieves an average 4.5% ...

Distributed inference is a promising solution for deploying Deep Neural Network (DNN) applications in resource-constrained edge environments. However, due to the complexity and variability of edge scenarios, efficiently completing DNN inference ...

• Static inference cannot adapt to the dynamic changes in the edge environment.
• Two basic scheduling strategies: Re-Partition and Complete-Push.
• Dynamic Push is the proposed 3-stages runtime scheduler.
• DIDS achieves a noticeable ...

Edge artificial intelligence (AI) is an emerging paradigm that leverages edge computing to pave the last-mile delivery of AI. To satisfy the increasing demand for high-performance computing and low latency of edge service, heterogeneous computing ...

The advancement of digitization and automation in Low Altitude Intelligent Networking (LAIN) is constrained by limited computational resources and the absence of a dedicated modal transformation mechanism, affecting the performance of latency-...

• Federal Learning approaches applicable to private cloud edge systems.
• Module for zero-shot denoising with detail preservation for SAR images.
• Cross-channel local focusing partially enhances picture feature characterization.
• ...

The past years have witnessed the success of a distributed learning system called Federated Learning (FL). Recently, asynchronous FL (AFL) has demonstrated its potential in concurrency compared to mainstream synchronous FL. However, the inherent ...

• Address challenges in fully asynchronous FL: trip asynchrony, local update drift, dynamic communication.
• Propose a 1-bit feedback mechanism to dynamically regulate client trips and match their capabilities.
• Present a sharpness-...

In the rapidly expanding field of IoT, data production has reached an unprecedented scale, providing valuable insights that accelerate decision-making processes. However, ensuring the privacy and security of this massive amount of data poses ...

• Introducing a Clustered FL approach to enhance privacy and security in handling IoT big data.
• Addressing CFL challenges like multi-distribution data, cluster similarity, and class imbalance.
• MDSPFL allows local datasets to follow ...

The prediction strategy is a key method for solving dynamic multi-objective optimization problems (DMOPs), particularly the commonly used linear prediction strategy, which has an advantage in solving problems with regular changes. However, using ...

• Using the linear prediction strategy only may result in the loss of population diversity.
• A dynamic particle swarm prediction strategy is used to increase the population diversity.
• An improved linear prediction strategy is used to ...

The increasing adoption of edge computing in rural areas is leading to a substantial rise in data generation, necessitating the need for development of advanced load balancing algorithms. This is particularly important in applications that ...

• SHIELD framework is designed to improve task management in rural edge computing infrastructures.
• The load distribution is performed while considering following factors – completion time, failure rate, resource utilisation, overhead, ...

Viruses, abundant across diverse environments, play pivotal roles in microbial ecosystems and impact human health. Traditional virus studies are limited by their reliance on culture cultivation, which has been mitigated by metagenomics. It ...

• TransGINmer is a composite model that incorporates multi-head attention mechanisms with graph isomorphic neural networks. It utilizes the multi-head attention mechanism to capture global dependencies and employs the graph isomorphic neural ...

Bicliques, as a prevalent graph pattern, are of particular interest in graph mining and social network analysis, especially for detecting illegal activities on e-commerce platforms due to their dense structure. Overcoming the challenge of ...

• Developing GPU framework for MBE in large networks, first to solve in real-world.
• GPU-accelerated MBE with efficient APIs for various data analysis needs.
• Our framework outperforms with up to 12x speedup on key graph datasets.

Blockchain has been widely used for secure transactions among untrusted parties, but the current design of blockchain does not provide sufficient privacy and security for the data on the chain, limiting its application in sensitive information ...

• Fully homomorphic encryption enables computation in ciphertext form.
• Approach merges blockchain with fully homomorphic encryption.
• Approach boosts blockchain scalability with new privacy-preserving scheme.
• Approach introduces ...

Due to the high cost of single-cell sequencing technology, understanding cell heterogeneity within tissues is crucial for elucidating the biological characteristics of complex tissues. Therefore, this study proposes a method for generating pseudo-...

• A novel attention mechanism-based method is developed for deconvoluting real bulk RNA-seq data.
• Restored the proportion of cell types in mouse kidneys using CONVdecov.
• There is a significant difference in the proportion of ...

As machine learning models are increasingly deployed in production, robust monitoring and detection of concept and covariate drift become critical. This paper addresses the gap in the widespread adoption of drift detection techniques by proposing ...

• Serverless-based architecture enables efficient data drift detection in ML systems.
• Drift detection should be a requirement in the development of ML deployment pipelines.
• An edge ML system can incorporate data drift detection ...

Workflow Management Systems used to automate the execution of scientific workflow applications on parallel and distributed computing platforms must make scheduling decisions at runtime. A large number of workflow scheduling algorithms have been ...

• Simulation-Driven Portfolio Scheduling implemented in Workflow Management Systems can afford large performance improvement over the traditional one-algorithm approach.
• This improvement is still significant in the presence of high ...

HPC-based applications often have complex workflows with many software dependencies that hinder their portability on contemporary HPC architectures. In addition, these applications often require extraordinary efforts to deploy and execute at ...

• We offer an automated method to create HPC-ready containers for software, ensuring portability across hardware architectures while preserving performance.
• We utilize Spack, Singularity, and Docker buildx for container creation, ...