Article
Computer Science, Information Systems
Pankaj Mishra, Ahmed Moustafa, Takayuki Ito
Summary: Open market environments are characterized by dynamic participants and uncertainties in supply and demand. Vendors aim to optimize their revenue by adjusting selling prices according to market demand. We propose a real-time pricing approach that uses a priority-based fairness mechanism to allocate resources in open market environments. Experimental results show that our approach outperforms existing methods in maximizing vendors' revenue.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. V. Chawla, Meng Jiang
Summary: This article introduces a novel framework called CoEvoGNN for modeling dynamic attributed graph sequences. The framework preserves the impact of earlier graphs on the current graph through embedding generation and utilizes temporal self-attention architecture to capture long-range dependencies. It optimizes model parameters jointly on attribute inference and link prediction tasks, enabling it to capture co-evolutionary patterns of attribute change and link formation.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Tianshu Wang, Peter Melchior
Summary: This study proposes a resource allocation strategy based on a bipartite graph neural network (GNN) to address concrete allocation problems in experimental and observational sciences. By training the GNN to optimize user-defined scientifically motivated objective functions, augmented by an infeasibility penalty, the method enables fast adjustment and deployment of allocation strategies, statistical analyses, and fully differentiable, science-driven solutions for resource allocation problems.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2022)
Article
Automation & Control Systems
Dan You, Oussama Karoui, Shouguang Wang
Summary: A new algorithm based on problem decomposition is proposed for the enumeration of minimal siphons in Petri nets. The experimental results show that the proposed algorithm consumes less computational time and memory compared to the existing algorithm.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Green & Sustainable Science & Technology
Devika Jay, K. S. Swarup
Summary: Modern power systems are becoming smarter and more competitive, with real-time pricing of active and reactive power considered an efficient energy management method. However, the procurement of reactive power through market mechanisms in real-time has not yet been implemented. This paper details the challenges faced in implementing reactive power markets and presents a review of existing mechanisms to address these challenges, as well as a framework for reactive power ancillary service in a smart grid.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Computer Science, Information Systems
Omar Abdulghafoor, Musbah Shaat, Ibraheem Shayea, Ahmad Hamood, Abdelzahir Abdelmaboud, Ashraf Osman Ibrahim, Fadhil Mukhlif, Herish Badal, Norafida Ithnin, Ali Khadim Lwas
Summary: This paper proposes a price-based power algorithm to reduce the computational complexity of resource allocation in cognitive radio networks. Compared to other frameworks, this algorithm reduces complexity and provides flexibility in controlling interference to primary network users. The proposed algorithm outperforms others in terms of performance, with a complexity of O(NM) + O(Nlog(N)).
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Information Systems
Ning Xie, Jixian Zhang, Xuejie Zhang, Weidong Li
Summary: This paper proposes a double auction model to address the issue of edge computing resource allocation in blockchain networks. The proposed mechanisms, TDAMB and DAMCV, have been shown to have good effects on edge computing resource allocation in blockchain networks.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Qi Zhang, Jie Gao
Summary: The study shows that the transferability of extended warranties is related to the manufacturer's optimal pricing decisions, with manufacturers more likely to offer transferable extended warranties when costs are low and non-transferable ones when costs are high. Additionally, manufacturers can use optional extended warranties to segment the market.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Economics
Devansh Jalota, Marco Pavone, Qi Qi, Yinyu Ye
Summary: This paper introduces a modified Fisher market model where agents may have additional linear constraints, and studies the properties of the resulting equilibria. To determine equilibrium prices, a budget-adjusted social optimization problem (BA-SOP) is introduced, whose optimal dual variables correspond to the equilibrium prices. To address the computational intensity and centralized knowledge requirement, a new class of distributed algorithms based on the Alternating Direction Method of Multipliers (ADMM) is proposed for computing equilibrium prices. The ADMM approach provides strong convergence guarantees and a general-purpose method for computing market equilibria for Fisher markets with homogeneous linear constraints and classical Fisher markets.
GAMES AND ECONOMIC BEHAVIOR
(2023)
Article
Engineering, Industrial
Hanxiao Zhang, Muxia Sun, Yan-Fu Li
Summary: This paper studies the reliability-redundancy allocation problem in multi-state flow networks, considering the minimization of cost or the maximization of reliability under resource constraints. An approximation scheme based on minimal cut is proposed to transform the problem, and feasibility guarantee and posterior check are conducted to ensure the quality of the solution. Numerical experiments show the tradeoff between accuracy and computational complexity, and the outperformance of the proposed method compared to a meta-heuristic algorithm.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Multidisciplinary
Yuexia Zhang, Ying Zhou
Summary: In this study, a resource allocation strategy based on tripartite graph is proposed to address the issues of low vehicle data transmission rate, poor service quality, and low service content delivery efficiency in vehicular social networks. The strategy establishes a mobile vehicular social networks model based on vehicle mobility and social similarity, and utilizes one-to-one stable matching to maximize content delivery efficiency and transmission rate. Simulation results show that the proposed strategy significantly improves content delivery efficiency and transmission rate compared to other strategies.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Jiadi Liu, Songtao Guo, Kai Liu, Liang Feng
Summary: Mobile edge computing (MEC) improves the performance of mobile applications by leveraging nearby servers to provide task offloading execution service. This article introduces a pricing mechanism using a market model and microeconomic theory to efficiently allocate limited resources in the MEC system.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Theory & Methods
Xuyang Ma, Du Xu, Katinka Wolter
Summary: This paper proposes a distributed Feedback-based Combinatorial Multi-unit Double Auction mechanism backed by blockchain to establish a cloud resource market that not only produces high social welfare but also motivates participants to provide high-quality service.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Hardware & Architecture
Sheng Chen, Baochao Chen, Xiaoyi Tao, Xin Xie, Keqiu Li
Summary: The paper proposes a resource allocation method based on online congestion-aware dynamic pricing, aimed at achieving load balancing and user satisfaction simultaneously, leveraging Lyapunov optimization technique to balance utility maximization and system stability.
JOURNAL OF SYSTEMS ARCHITECTURE
(2022)
Article
Computer Science, Hardware & Architecture
Razie Roostaei, Zahra Dabiri, Zeinab Movahedi
Summary: Mobile Edge Computing offers cloud computation capabilities at the edge of the mobile network, but efficiency is influenced by purchasing power and resource competition. A game-based distributed scheme is proposed to address challenges in resource allocation and pricing for computation offloading, considering incentives and conflicts between edge providers and Mobile Users (MUs).
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)