Pub Date : 2026-02-03DOI: 10.1016/j.future.2026.108414
Andreas Kouloumpris, Georgios L. Stavrinides, Maria K. Michael, Theocharis Theocharides
{"title":"A reliability- and latency-driven task allocation framework for workflow applications in the edge-hub-cloud continuum","authors":"Andreas Kouloumpris, Georgios L. Stavrinides, Maria K. Michael, Theocharis Theocharides","doi":"10.1016/j.future.2026.108414","DOIUrl":"https://doi.org/10.1016/j.future.2026.108414","url":null,"abstract":"","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"103 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110188","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}
Pub Date : 2026-02-03DOI: 10.1016/j.future.2026.108416
Shouxi Luo, Xiaoyu Yu, Huanlai Xing, Ke Li, Bo Peng
{"title":"Maximizing the Benefits of In-Network Aggregation With Joint Job Placement and Routing Control","authors":"Shouxi Luo, Xiaoyu Yu, Huanlai Xing, Ke Li, Bo Peng","doi":"10.1016/j.future.2026.108416","DOIUrl":"https://doi.org/10.1016/j.future.2026.108416","url":null,"abstract":"","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"194 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110190","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}
Pub Date : 2026-02-03DOI: 10.1016/j.future.2026.108409
Seyed Hossein Ahmadpanah
{"title":"Concord: A Scalable, Trace-Driven, and Reproducible Framework for Resilient Container Warming in Serverless IoT","authors":"Seyed Hossein Ahmadpanah","doi":"10.1016/j.future.2026.108409","DOIUrl":"https://doi.org/10.1016/j.future.2026.108409","url":null,"abstract":"","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"398 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110186","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}
Pub Date : 2026-02-03DOI: 10.1016/j.future.2026.108412
Rabi Shaw, Suman Majumder
{"title":"DQVeriChain: Distributed Quantum-State-Verified and DID-Based Self-Attentive Large Language Model for Criminal Tracking Using Blockchain","authors":"Rabi Shaw, Suman Majumder","doi":"10.1016/j.future.2026.108412","DOIUrl":"https://doi.org/10.1016/j.future.2026.108412","url":null,"abstract":"","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"8 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110192","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}
Pub Date : 2026-02-01DOI: 10.1016/j.future.2026.108410
Sekione Reward Jeremiah, ByungHyun Jo, Kim-Kwang Raymond Choo, Jong Hyuk Park
{"title":"Block-FDT: Blockchain-Enhanced Federated Learning Approach to Secure DT-Assisted IIoT Networks","authors":"Sekione Reward Jeremiah, ByungHyun Jo, Kim-Kwang Raymond Choo, Jong Hyuk Park","doi":"10.1016/j.future.2026.108410","DOIUrl":"https://doi.org/10.1016/j.future.2026.108410","url":null,"abstract":"","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"89 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110195","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}
Pub Date : 2026-01-25DOI: 10.1016/j.future.2026.108384
He Li, Jiajia Gui, Weihang Kong, Xingchen Zhang
Multi-view pedestrian detection aims to generate a bird’s-eye view occupancy map of pedestrians from multiple calibrated camera views. Multi-view methods offer advantages over single-view approaches: they can mitigate occlusions, expand scene coverage, and improve robustness. However, existing multi-view detection methods still face two critical challenges: mixing heterogeneous cross-view information in the fused representation and feature misalignment in the world coordinate system caused by various scales across views. To solve these issues, we develop a novel multi-view pedestrian detection framework that includes a residual mask fusion module and a cosine similarity-based passive sampler. Specifically, the residual mask fusion module enables adaptive feature selection and compensation across views, yielding an optimal fusion under geometric redundancy. Moreover, the cosine similarity-based passive sampler computes dynamic coordinate offsets by evaluating feature consistency. This reduces the impact of unavoidable biases introduced during projection. Experimental results on Wildtrack, MultiviewX and CityStreet demonstrate the effectiveness and reliability of the developed framework for multi-view pedestrian detection. Our code is available at https://github.com/guixiaojia/improve-shot.
{"title":"Multi-view pedestrian detection via residual mask fusion and cosine similarity-based passive sampler for video surveillance systems","authors":"He Li, Jiajia Gui, Weihang Kong, Xingchen Zhang","doi":"10.1016/j.future.2026.108384","DOIUrl":"https://doi.org/10.1016/j.future.2026.108384","url":null,"abstract":"Multi-view pedestrian detection aims to generate a bird’s-eye view occupancy map of pedestrians from multiple calibrated camera views. Multi-view methods offer advantages over single-view approaches: they can mitigate occlusions, expand scene coverage, and improve robustness. However, existing multi-view detection methods still face two critical challenges: mixing heterogeneous cross-view information in the fused representation and feature misalignment in the world coordinate system caused by various scales across views. To solve these issues, we develop a novel multi-view pedestrian detection framework that includes a residual mask fusion module and a cosine similarity-based passive sampler. Specifically, the residual mask fusion module enables adaptive feature selection and compensation across views, yielding an optimal fusion under geometric redundancy. Moreover, the cosine similarity-based passive sampler computes dynamic coordinate offsets by evaluating feature consistency. This reduces the impact of unavoidable biases introduced during projection. Experimental results on Wildtrack, MultiviewX and CityStreet demonstrate the effectiveness and reliability of the developed framework for multi-view pedestrian detection. Our code is available at <ce:inter-ref xlink:href=\"https://github.com/guixiaojia/improve-shot\" xlink:type=\"simple\">https://github.com/guixiaojia/improve-shot</ce:inter-ref>.","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"1 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047989","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}
Pub Date : 2026-01-24DOI: 10.1016/j.future.2026.108393
Sandra Catalán , Rafael Rodríguez-Sánchez , Carlos García Sánchez , Luis Piñuel Moreno
This paper evaluates the performance and energy efficiency of Apple processors across multiple ARM-based M-series generations and models (standard and Pro). The study is motivated by the increasing heterogeneity of Apple´s SoC architectures, which integrate multiple computing engines raising the scientific question of which hardware components are best suited for executing general-purpose and domain-specific computations such as the GEneral Matrix Multiply (GEMM). The analysis focuses on four key components: the Central Processing Unit (CPU), the Graphics Processing Unit (GPU), the matrix calculation accelerator (AMX), and the Apple Neural Engine (ANE).
The assessments use the GEMM as benchmark to characterize the performance of the CPU and GPU, alongside tests on AMX, which is specialized in handling large-scale mathematical operations, and tests on the ANE, which is specifically designed for Deep Learning purposes. Additionally, energy consumption data has been collected to analyze the energy efficiency of the aforementioned resources. Results highlight notable improvements in computational capacity and energy efficiency over successive generations. On one hand, the AMX stands out as the most efficient component for FP32 and FP64 workloads, significantly boosting overall system performance. In the M4 Pro, which integrates two matrix accelerators, it achieves up to 68% of the GPU’s FP32 performance while consuming only 42% of its power. On the other hand, the ANE, although limited to FP16 precision, excels in energy efficiency for low-precision tasks, surpassing other accelerators with over 700 GFLOPs/Watt under batched workloads.
This analysis offers a clear understanding of how Apple´s custom ARM designs optimize both performance and energy use, particularly in the context of multi-core processing and specialized acceleration units. In addition, a significant contribution of this study is the comprehensive comparative analysis of Apple’s accelerators, which have previously been poorly documented and scarcely studied. The analysis spans different generations and compares the accelerators against both CPU and GPU performance.
{"title":"A comparative performance and efficiency analysis of Apple’s M architectures: A GEMM case study","authors":"Sandra Catalán , Rafael Rodríguez-Sánchez , Carlos García Sánchez , Luis Piñuel Moreno","doi":"10.1016/j.future.2026.108393","DOIUrl":"10.1016/j.future.2026.108393","url":null,"abstract":"<div><div>This paper evaluates the performance and energy efficiency of Apple processors across multiple ARM-based M-series generations and models (standard and Pro). The study is motivated by the increasing heterogeneity of Apple´s SoC architectures, which integrate multiple computing engines raising the scientific question of which hardware components are best suited for executing general-purpose and domain-specific computations such as the GEneral Matrix Multiply (<span>GEMM</span>). The analysis focuses on four key components: the Central Processing Unit (CPU), the Graphics Processing Unit (GPU), the matrix calculation accelerator (AMX), and the Apple Neural Engine (ANE).</div><div>The assessments use the <span>GEMM</span> as benchmark to characterize the performance of the CPU and GPU, alongside tests on AMX, which is specialized in handling large-scale mathematical operations, and tests on the ANE, which is specifically designed for Deep Learning purposes. Additionally, energy consumption data has been collected to analyze the energy efficiency of the aforementioned resources. Results highlight notable improvements in computational capacity and energy efficiency over successive generations. On one hand, the AMX stands out as the most efficient component for FP32 and FP64 workloads, significantly boosting overall system performance. In the M4 Pro, which integrates two matrix accelerators, it achieves up to 68% of the GPU’s FP32 performance while consuming only 42% of its power. On the other hand, the ANE, although limited to FP16 precision, excels in energy efficiency for low-precision tasks, surpassing other accelerators with over 700 GFLOPs/Watt under batched workloads.</div><div>This analysis offers a clear understanding of how Apple´s custom ARM designs optimize both performance and energy use, particularly in the context of multi-core processing and specialized acceleration units. In addition, a significant contribution of this study is the comprehensive comparative analysis of Apple’s accelerators, which have previously been poorly documented and scarcely studied. The analysis spans different generations and compares the accelerators against both CPU and GPU performance.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"180 ","pages":"Article 108393"},"PeriodicalIF":6.2,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048040","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}
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