Future Generation Computer Systems-The International Journal of Escience最新文献_第7页

MMGCSyn: Explainable synergistic drug combination prediction based on multimodal fusion MMGCSyn：基于多模态融合的可解释协同药物联合预测

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-01 DOI: 10.1016/j.future.2025.107784

Yongqing Zhang , Hao Yuan , Yuhang Liu , Shuwen Xiong , Zhigan Zhou , Yugui Xu , Xinyu Mao , Meiqin Gong

{"title":"MMGCSyn: Explainable synergistic drug combination prediction based on multimodal fusion","authors":"Yongqing Zhang , Hao Yuan , Yuhang Liu , Shuwen Xiong , Zhigan Zhou , Yugui Xu , Xinyu Mao , Meiqin Gong","doi":"10.1016/j.future.2025.107784","DOIUrl":"10.1016/j.future.2025.107784","url":null,"abstract":"<div><div>Synergistic drug combinations are an effective solution for treating complex diseases. The main challenge is to improve the model performance of the unknown drug combination prediction task. Due to some drugs in the dataset being wholly excluded, it is difficult for the model to effectively extract the data features of these drugs, affecting the model’s accuracy and generalization ability. Unlike previous methods, we propose an interpretable synergistic drug combination prediction model, MMGCSyn, based on multimodal feature fusion. The process is as follows: First, given any (drug, drug, cell line) triple. For drug features, a graph attention network is used to extract drug molecular graph features, a deformable convolutional network is used to extract drug morgan fingerprint features and the spatial feature reconstruction module is used to suppress morgan fingerprint feature redundancy. Multi-layer MLP is used to extract the features of cell line features. Subsequently, feature fusion and prediction are performed through Transformer. We compared five existing methods on three drug combination datasets. The results show that MMGCSyn has achieved the best results and can effectively capture the chemical substructures of drug molecules.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107784"},"PeriodicalIF":6.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ARGO: Overcoming hardware dependence in distributed learning 在分布式学习中克服硬件依赖

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-28 DOI: 10.1016/j.future.2025.107778

Karim Boubouh , Amine Boussetta , Rachid Guerraoui , Alexandre Maurer

{"title":"ARGO: Overcoming hardware dependence in distributed learning","authors":"Karim Boubouh , Amine Boussetta , Rachid Guerraoui , Alexandre Maurer","doi":"10.1016/j.future.2025.107778","DOIUrl":"10.1016/j.future.2025.107778","url":null,"abstract":"<div><div>Mobile devices offer a valuable resource for distributed learning alongside traditional computers, encouraging energy efficiency and privacy through local computations. However, the hardware limitations of these devices makes it impossible to use classical SGD for industry-grade machine learning models (with a very large number of parameters). Moreover, they are intermittently available and susceptible to failures. To address these challenges, we introduce <span>ARGO</span>, an algorithm that combines adaptive workload schemes with Byzantine resilience mechanisms, as well as dynamic device participation. Our theoretical analysis demonstrates linear convergence for strongly convex losses and sub-linear convergence for non-convex losses, without assuming specific dataset partitioning (for potential data heterogeneity). Our formal analysis highlights the interplay between convergence properties, hardware capabilities, Byzantine impact, and standard factors such as mini-batch size and learning rate. Through extensive evaluations, we show that <span>ARGO</span> outperforms standard SGD in terms of convergence speed and accuracy, and most importantly, thrives when classical SGD is not possible due to hardware limitations.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107778"},"PeriodicalIF":6.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PIM-IoT: Enabling hierarchical, heterogeneous, and agile Processing-in-Memory in IoT systems PIM-IoT：在物联网系统中实现分层、异构和敏捷的内存处理

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-28 DOI: 10.1016/j.future.2025.107782

Kan Zhong , Qiao Li , Ao Ren , Yujuan Tan , Xianzhang Chen , Linbo Long , Duo Liu

{"title":"PIM-IoT: Enabling hierarchical, heterogeneous, and agile Processing-in-Memory in IoT systems","authors":"Kan Zhong , Qiao Li , Ao Ren , Yujuan Tan , Xianzhang Chen , Linbo Long , Duo Liu","doi":"10.1016/j.future.2025.107782","DOIUrl":"10.1016/j.future.2025.107782","url":null,"abstract":"<div><div>The Internet of Things (IoT) is an emerging concept that senses the physical world by connecting various “things” and objects to the Internet. Conventional cloud-based IoT systems are unlikely to keep up with the diverse needs of IoT applications and have some issues, such as privacy and latency. Edge computing based IoT systems solve these issues by placing data processing and inference tasks near the data source. However, due to the increasing complexity of IoT applications, performing data processing and inference tasks in edge computing based IoT systems can lead to high energy consumption and latency.</div><div>Processing-in-Memory (PIM) is a promising solution to reduce the energy consumption of data processing and inference tasks by closely integrating computational logics with memory device. Therefore, in this paper, we propose <strong>PIM-IoT</strong>, a PIM architectures enabled IoT system to reduce the energy consumption. To accommodate various data processing tasks, we architect PIM-IoT as a hierarchical system that consists of 3 tiers: <em>sensing tier</em>, <em>gateway tier</em>, and <em>edge computing tier</em>. We first analyze the dataflow of typical IoT applications and map tasks to different tiers. To handle the data processing and inference tasks effectively in each tier, we then propose hierarchical, heterogeneous, and collaborative PIM architectures for each tier. Finally, we show how multi-tier can be co-optimized under latency and power constraints. To our knowledge, this is the first work to explore novel PIM architectures in IoT systems. Detailed analysis and experimental results show that PIM-IoT can achieve 5.6x performance improvement and 6x energy consumption reduction for IoT applications.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107782"},"PeriodicalIF":6.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated workload-aware quantized framework for secure learning in data-sensitive applications 联邦工作负载感知量化框架，用于数据敏感应用程序中的安全学习

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-28 DOI: 10.1016/j.future.2025.107772

Manu Narula , Jasraj Meena , Dinesh Kumar Vishwakarma

{"title":"Federated workload-aware quantized framework for secure learning in data-sensitive applications","authors":"Manu Narula , Jasraj Meena , Dinesh Kumar Vishwakarma","doi":"10.1016/j.future.2025.107772","DOIUrl":"10.1016/j.future.2025.107772","url":null,"abstract":"<div><div>Federated Learning (FL) emerged as a leading secure, distributed learning technology based on sharing insights instead of data. The privacy-ensuring capability of FL has enabled its extensive use in Data-Sensitive Applications like healthcare and finance. However, the transmitted insights are at risk of leakage as the security of the medium cannot be guaranteed and can lead to the inference of the user data. Quantization is sometimes used to change these transmitted values to provide security but at the cost of accuracy loss in global models. Coupled with client dropouts, this increases performance loss. In this paper, we propose a Federated Workload-Aware Framework with Linear Quantization (Fed-WALQ), which layers the quantization process with an active client-selection technique based on the sustainable workload of the clients. The framework minimizes the dropout rates and compensates for the loss due to quantization. Through numerical experiments compared against traditional FL and Quantization-enabled FL over multiple datasets, the Fed-WALQ shows improvements in security over the former and accuracy over the latter. The accuracy improvement varies with the complexities of the involved datasets, while a substantial drop in straggler node percentages is seen in all cases (up to 91.8% drop).</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107772"},"PeriodicalIF":6.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VeriTrac: Verifiable and traceable cross-silo federated learning VeriTrac：可验证和可跟踪的跨竖井联合学习

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-27 DOI: 10.1016/j.future.2025.107780

Yanxin Xu , Hua Zhang , Zhenyan Liu , Fei Gao , Lei Qiao

{"title":"VeriTrac: Verifiable and traceable cross-silo federated learning","authors":"Yanxin Xu , Hua Zhang , Zhenyan Liu , Fei Gao , Lei Qiao","doi":"10.1016/j.future.2025.107780","DOIUrl":"10.1016/j.future.2025.107780","url":null,"abstract":"<div><div>Cross-silo federated learning enables many clients to train a machine learning model collaboratively, while keeping the raw training data locally. It faces the risks of privacy leakage and malicious participants. In this paper, we introduce a new security risk that malicious clients may disrupt the training process of cross-silo federated learning by falsifying the verification evidences. The verification failure caused by this malicious behavior is not easily distinguishable from that caused by the malicious server falsifying the aggregated model. To address this issue, we design VeriTrac, the first privacy-preserving cross-silo federated learning scheme that supports verifiability and traceability. Before performing the aggregation, the server can utilize the non-private information of clients to verify messages submitted by them to avoid being framed. When the proportion of malicious clients is less than 50%, malicious participants causing the verification error can be traced. In addition, to verify the correctness of the aggregated models, a model vector with a verification factor is constructed and encrypted. The vector is confidential for the server, and the factor is part of the verification evidence and recoverable for clients. Security analysis shows that VeriTrac can guarantee the tracing of malicious participants and the data security of clients. Experimental evaluation shows that computation efficiency and communication efficiency of VeriTrac are acceptable.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107780"},"PeriodicalIF":6.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alleviating straggler impacts for data parallel deep learning with hybrid parameter update 基于混合参数更新的数据并行深度学习离散影响缓解

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-27 DOI: 10.1016/j.future.2025.107775

Hongliang Li , Qi Tian , Dong Xu , Hairui Zhao , Zhewen Xu

引用次数: 0

Entropy-based genetic feature engineering and multi-classifier fusion for anomaly detection in vehicle controller area networks 基于熵的遗传特征工程和多分类器融合的车辆控制器区域网络异常检测

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-27 DOI: 10.1016/j.future.2025.107779

Mohammad Fatahi , Danial Sadrian Zadeh , Behzad Moshiri , Otman Basir

{"title":"Entropy-based genetic feature engineering and multi-classifier fusion for anomaly detection in vehicle controller area networks","authors":"Mohammad Fatahi , Danial Sadrian Zadeh , Behzad Moshiri , Otman Basir","doi":"10.1016/j.future.2025.107779","DOIUrl":"10.1016/j.future.2025.107779","url":null,"abstract":"<div><div>Technological advances in mobile computing, wireless communications, and remote sensing have provided the foundation for expanding and improving intelligent transportation systems (ITS), making modern vehicles susceptible to cyberattacks due to their evolved functionality and connectivity. In-vehicle networks, such as controller area networks (CAN), are highly vulnerable to attacks due to the lack of security architecture. Considering the temporal and spatial aspects of attacks and the need to develop lightweight models, this study develops a flexible and lightweight anomaly detection model for CAN bus with normal and sensitive duty cycles. To achieve optimal performance and consider spatio-temporal information, the feature space is optimized by extracting new features based on a two-parameter genetic algorithm (2P-GA) and Shannon entropy. Next, a synergistic combination of different supervised machine learning classifiers based on the ordered weighted averaging (OWA) operators is leveraged to optimize the results and achieve better performance. Also, to show the effectiveness of the proposed method in the present study, a comprehensive and unique comparative analysis with previous works and state-of-the-art models is presented. The results show that the proposed framework achieves the highest performance in terms of accuracy and F1-score and the lowest computational cost compared with previous works.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107779"},"PeriodicalIF":6.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic FPGA reconfiguration for scalable embedded artificial intelligence (AI): A co-design methodology for convolutional neural networks (CNN) acceleration 可扩展嵌入式人工智能（AI）的动态FPGA重构：卷积神经网络（CNN）加速的协同设计方法

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-26 DOI: 10.1016/j.future.2025.107777

Jalil Boudjadar , Saif Ul Islam , Rajkumar Buyya

{"title":"Dynamic FPGA reconfiguration for scalable embedded artificial intelligence (AI): A co-design methodology for convolutional neural networks (CNN) acceleration","authors":"Jalil Boudjadar , Saif Ul Islam , Rajkumar Buyya","doi":"10.1016/j.future.2025.107777","DOIUrl":"10.1016/j.future.2025.107777","url":null,"abstract":"<div><div>In recent years, FPGA platforms have shown significant potential for accelerating artificial intelligence (AI) applications, particularly in Embedded AI. While various studies have explored adaptive AI deployment on FPGAs, there remains a gap in methodologies fully integrating software adaptability with FPGA hardware reconfigurability. This article presents a novel end-to-end co-design methodology for deploying adaptable and scalable Convolutional Neural Networks (CNNs) on FPGA platforms. The framework enhances computational performance and reduces latency by dynamically modifying hardware acceleration units by combining CNN architecture adaptability with dynamic partial reconfiguration of FPGA hardware. The proposed methodology enables automated synthesis and runtime customization of both hardware accelerators and CNN architectures, eliminating the need for iterative synthesis. This approach has been implemented and tested on a Xilinx XC7020 FPGA board for a CNN-based image classifier, achieving superior computation performance (0.68s/image) and accuracy (97%) compared to state-of-the-art alternatives.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107777"},"PeriodicalIF":6.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A distributed monitoring architecture for JointCloud computing JointCloud计算的分布式监控架构

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-26 DOI: 10.1016/j.future.2025.107773

Yadi Wu , Lina Wang , Rongwei Yu , Xiuwen Huang , Jiatong Liu

{"title":"A distributed monitoring architecture for JointCloud computing","authors":"Yadi Wu , Lina Wang , Rongwei Yu , Xiuwen Huang , Jiatong Liu","doi":"10.1016/j.future.2025.107773","DOIUrl":"10.1016/j.future.2025.107773","url":null,"abstract":"<div><div>JointCloud computing supports large-scale resource consolidation and collaboration among multiple cloud service providers to provide users with powerful performance and adequate services. In the face of exponential scaling of resources, monitoring is an indispensable part of effective resource management. Monitoring provides methods for reviewing and managing the performance status of JointCloud resources and services to better characterize the overall operating status of JointCloud system. However, the collaboration between cloud service providers and the scale of resources in JointCloud are dynamically changing, and it is not easy to perform monitoring in a flexible and scalable way. In order to cover all aspects related to resource monitoring in JointCloud environments, we propose a distributed monitoring architecture for JointCloud computing. The architecture focuses on the ability to obtain information, organizes monitoring components in a modular way, and supports on-demand startup to provide dynamic monitoring capabilities. The proposed distributed monitoring approach provides load balancing and fault tolerance services to ensure reliability and performance of monitoring. The architecture also considers the JointCloud quality of service (QoS) and designs a virtual resource orchestration approach aimed at improving the efficiency of resource utilization. We have developed a prototype architecture and presented experimental results to evaluate our design. The prototype architecture can be easily deployed in public or private JointCloud infrastructures for flexible and scalable monitoring. The evaluation results show that our architecture is feasible in terms of performance and scalability.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107773"},"PeriodicalIF":6.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solving online resource-constrained scheduling for follow-up observation in astronomy: A reinforcement learning approach 求解天文学后续观测的在线资源约束调度：一种强化学习方法

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-26 DOI: 10.1016/j.future.2025.107781

Yajie Zhang, Ce Yu, Chao Sun, Jizeng Wei, Junhan Ju, Shanjiang Tang

{"title":"Solving online resource-constrained scheduling for follow-up observation in astronomy: A reinforcement learning approach","authors":"Yajie Zhang, Ce Yu, Chao Sun, Jizeng Wei, Junhan Ju, Shanjiang Tang","doi":"10.1016/j.future.2025.107781","DOIUrl":"10.1016/j.future.2025.107781","url":null,"abstract":"<div><div>In the astronomical observation field, determining the allocation of observation resources of the telescope array and planning follow-up observations for targets of opportunity (ToOs) are indispensable components of astronomical scientific discovery. This problem is computationally challenging, given the online observation setting and the abundance of time-varying factors that can affect whether an observation can be conducted. This paper presents <span>ROARS</span>, a reinforcement learning approach for online astronomical resource-constrained scheduling. To capture the structure of the astronomical observation scheduling, we depict every schedule using a directed acyclic graph (DAG), illustrating the dependency of timing between different observation tasks within the schedule. Deep reinforcement learning is used to learn a policy that can improve the feasible solution by iteratively local rewriting until convergence. It can solve the challenge of obtaining a complete solution directly from scratch in astronomical observation scenarios, due to the high computational complexity resulting from numerous spatial and temporal constraints. A simulation environment is developed based on real-world scenarios for experiments, to evaluate the effectiveness of our proposed scheduling approach. The experimental results show that <span>ROARS</span> surpasses 5 popular heuristics, adapts to various observation scenarios and learns effective strategies with hindsight.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107781"},"PeriodicalIF":6.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0