Future Generation Computer Systems-The International Journal of Escience最新文献_第6页

RGAnomaly: Data reconstruction-based generative adversarial networks for multivariate time series anomaly detection in the Internet of Things

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-10 DOI: 10.1016/j.future.2025.107751

Cheng Qian , Wenzhong Tang , Yanyang Wang

{"title":"RGAnomaly: Data reconstruction-based generative adversarial networks for multivariate time series anomaly detection in the Internet of Things","authors":"Cheng Qian , Wenzhong Tang , Yanyang Wang","doi":"10.1016/j.future.2025.107751","DOIUrl":"10.1016/j.future.2025.107751","url":null,"abstract":"<div><div>The Internet of Things encompasses a variety of components, including sensors and controllers, which generate vast amounts of multivariate time series data. Anomaly detection within this data can reveal patterns of behavior that deviate from normal operating states, providing timely alerts to mitigate potential serious issues or losses. The prevailing methodologies for multivariate time series anomaly detection are based on data reconstruction. However, these methodologies face challenges related to insufficient feature extraction and fusion, as well as instability in the reconstruction effectiveness of a single model. In this article, we propose RGAnomaly, a novel data reconstruction-based generative adversarial network model. This model leverages transformers and cross-attention mechanisms to extract and fuse the temporal and metric features of multivariate time series. RGAnomaly constructs a joint generator comprising an autoencoder and a variational autoencoder, which forms the adversarial structure with a discriminator. The anomaly score is derived from the combined data reconstruction loss and discrimination loss, providing a more comprehensive evaluation for anomaly detection. Comparative experiments and ablation studies on four public multivariate time series datasets demonstrate that RGAnomaly delivers superior performance in anomaly detection, effectively identifying anomalies in time series data within IoT environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107751"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394641","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

Enhancing federated averaging of self-supervised monocular depth estimators for autonomous vehicles with Bayesian optimization

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-10 DOI: 10.1016/j.future.2025.107752

Elton F. de S. Soares , Emilio Vital Brazil , Carlos Alberto V. Campos

{"title":"Enhancing federated averaging of self-supervised monocular depth estimators for autonomous vehicles with Bayesian optimization","authors":"Elton F. de S. Soares , Emilio Vital Brazil , Carlos Alberto V. Campos","doi":"10.1016/j.future.2025.107752","DOIUrl":"10.1016/j.future.2025.107752","url":null,"abstract":"<div><div>Recent research in computer vision for intelligent transportation systems has prominently focused on image-based depth estimation due to its cost-effectiveness and versatile applications. Monocular depth estimation methods, in particular, have gained attention for their reliance on a single camera, offering high versatility compared to binocular techniques requiring two fixed cameras. While advanced approaches leverage self-supervised deep neural network learning with proxy tasks like pose estimation and semantic segmentation, some overlook crucial requirements for real autonomous vehicle deployment. These include data privacy, reduced network consumption, distributed computational cost, and resilience to connectivity issues. Recent studies highlight the effectiveness of federated learning combined with Bayesian optimization in addressing these requirements without compromising model efficacy. Thus, we introduce BOFedSCDepth, a novel method integrating Bayesian optimization, federated learning, and deep self-supervision to train monocular depth estimators with better efficacy and efficiency than the state-of-the-art method on self-supervised federated learning. Evaluation experiments on KITTI and DDAD datasets demonstrate the superiority of our approach, achieving up to 40.1% test loss improvement over the baseline at the initial rounds of training with up to 33.3% communication cost reduction, linear computational cost overhead at the central server and no overhead at the autonomous vehicles.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107752"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394642","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

K-bisimulation: A novel approach for simplifying heterogeneous information networks

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-10 DOI: 10.1016/j.future.2025.107749

Yongjie Liang , Wujie Hu , Jinzhao Wu

{"title":"K-bisimulation: A novel approach for simplifying heterogeneous information networks","authors":"Yongjie Liang , Wujie Hu , Jinzhao Wu","doi":"10.1016/j.future.2025.107749","DOIUrl":"10.1016/j.future.2025.107749","url":null,"abstract":"<div><div>Heterogeneous information networks (HINs) are becoming increasingly important and widely used; however, fewer studies are focusing on the branch structures within HINs. Based on the commonalities of concurrent systems and heterogeneous information networks, as well as the significant application of bisimulation equivalence in concurrent systems, this article proposes k-bisimulation among nodes belonging to same node type, aiming to simplify the branching structure of that to obtain a cost-effective model, wherein the k is a positive integrate being closely related to the similarity degree of nodes. In this paper, we initially define the notion of k-bisimulation for nodes. Subsequently, we propose a computational method to identify k-bisimulation among nodes of same type in HINs. With the assistance of this method, we can derive a network that is approximately bisimular to the original one. Theoretical and practical analysis reveals that errors in connected paths between the original and bisimular networks are controllable. Experimental results indicate that, in comparison to the original network, the obtained network exhibits a reduction in the number of nodes and edges, while still preserve same or similar information.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107749"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386373","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

A Web of Things approach for learning on the Edge–Cloud Continuum

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-08 DOI: 10.1016/j.future.2025.107736

Luca Bedogni , Federico Chiariotti

{"title":"A Web of Things approach for learning on the Edge–Cloud Continuum","authors":"Luca Bedogni , Federico Chiariotti","doi":"10.1016/j.future.2025.107736","DOIUrl":"10.1016/j.future.2025.107736","url":null,"abstract":"<div><div>Internet of Things (IoT) devices provide constant, contextual data that can be leveraged to automatically reconfigure and optimize smart environments. Artificial Intelligence (AI) and deep learning techniques are tools of increasing importance for this, as Deep Reinforcement Learning (DRL) can provide a general solution to this problem. However, the heterogeneity of scenarios in which DRL models may be deployed is vast, making the design of universal plug-and-play models extremely difficult. Moreover, the real deployment of DRL models on the Edge, and in the IoT in particular, is limited by two factors: firstly, the computational complexity of the training procedure, and secondly, the need for a relatively long exploration phase, during which the agent proceeds by trial and error. A natural solution to both these issues is to use simulated environments by creating a Digital Twin (DT) of the environment, which can replicate physical entities in the digital domain, providing a standardized interface to the application layer. DTs allow for simulation and testing of models and services in a simulated environment, which may be hosted on more powerful Cloud servers without the need to exchange all the data generated by the real devices. In this paper, we present a novel architecture based on the emerging Web of Things (WoT) standard, which provides a DT of a smart environment and applies DRL techniques on real time data. We discuss the theoretical properties of DRL training using DTs, showcasing our system in an existing real deployment, comparing its performance with a legacy system. Our findings show that the implementation of a DT, specifically for DRL models, allows for faster convergence and finer tuning, as well as reducing the computational and communication demands on the Edge network. The use of multiple DTs with different complexities and data requirements can also help accelerate the training, progressing by steps.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107736"},"PeriodicalIF":6.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394644","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

Hierarchical risk parity: Efficient implementation and real world analysis

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-07 DOI: 10.1016/j.future.2025.107744

Dario Deković , Petra Posedel Šimović

{"title":"Hierarchical risk parity: Efficient implementation and real world analysis","authors":"Dario Deković , Petra Posedel Šimović","doi":"10.1016/j.future.2025.107744","DOIUrl":"10.1016/j.future.2025.107744","url":null,"abstract":"<div><div>In this paper, we present an efficient implementation of the Hierarchical Risk Parity (HRP) portfolio optimization algorithm. HRP was designed to allocate portfolio weights by building a hierarchical tree of asset clusters and reducing risk through inverse variance allocation across the clusters. Our implementation improves the performance of the original algorithm by reducing its time complexity and making it more suitable for real-time systems. We evaluate the performance of our implementation on various constituents of the S&P 500 index, a market-capitalization-weighted index of 500 leading publicly traded companies in the U.S., using historical price data from 2005 to 2023. We compare the out-of-sample risk-adjusted returns of the HRP algorithm to those of a simple 1/N allocation method and find that the 1/N method outperforms HRP across all experimental setups. However, the HRP generated portfolios had a lower standard deviation by approximately 1% across all experimental setups. These results show that HRP can be of great use in generating portfolios when risk is the primary concern.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107744"},"PeriodicalIF":6.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378304","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

Real-time unsupervised video object detection on the edge

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-06 DOI: 10.1016/j.future.2025.107737

Paula Ruiz-Barroso, Francisco M. Castro, Nicolás Guil

{"title":"Real-time unsupervised video object detection on the edge","authors":"Paula Ruiz-Barroso, Francisco M. Castro, Nicolás Guil","doi":"10.1016/j.future.2025.107737","DOIUrl":"10.1016/j.future.2025.107737","url":null,"abstract":"<div><div>Object detection in video is an essential computer vision task. Consequently, many efforts have been devoted to developing precise and fast deep-learning models for this task. These models are commonly deployed on discrete and powerful GPU devices to meet both frame rate performance and detection accuracy requirements. Furthermore, model training is usually performed in a strongly supervised way so that samples must be previously labelled by humans using a slow and costly process. In this paper, we develop a real-time implementation for unsupervised object detection in video employing a low-power device. We improve typical approaches for object detection using information supplied by optical flow to detect moving objects. Besides, we use an unsupervised clustering algorithm to group similar detections that avoid manual object labelling. Finally, we propose a methodology to optimize the deployment of our resulting framework on an embedded heterogeneous platform. Thus, we illustrate how all the computational resources of a Jetson AGX Xavier (CPU, GPU, and DLAs) can be used to fulfil frame rate, accuracy, and energy consumption requirements. Three different data representations (FP32, FP16 and INT8) are studied for the pipeline networks in order to evaluate the impact of all of them in our pipeline. Obtained results show that our proposed optimizations can improve up to <span><math><mrow><mn>23</mn><mo>.</mo><mn>6</mn><mo>×</mo></mrow></math></span> energy consumption and <span><math><mrow><mn>32</mn><mo>.</mo><mn>2</mn><mo>×</mo></mrow></math></span> execution time with respect to the non-optimized pipeline without penalizing the original mAP (59.44). This computational complexity reduction is achieved through knowledge distillation, using FP16 data precision, and deploying concurrent tasks in different computing units.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107737"},"PeriodicalIF":6.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350726","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

SNCD: A fast and scalable distributed near-miss code clone detector for big code based on partial index

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-06 DOI: 10.1016/j.future.2025.107743

Liming Yang , Jun Zhao , Rulin Xie, Yi Ren, Jianbo Guan, Bao Li, Jun Ma, Yusong Tan

{"title":"SNCD: A fast and scalable distributed near-miss code clone detector for big code based on partial index","authors":"Liming Yang , Jun Zhao , Rulin Xie, Yi Ren, Jianbo Guan, Bao Li, Jun Ma, Yusong Tan","doi":"10.1016/j.future.2025.107743","DOIUrl":"10.1016/j.future.2025.107743","url":null,"abstract":"<div><div>A number of techniques have been proposed over the years to detect clones for improving software maintenance, reusability or security. However, there is still a lack of language agnostic approaches with code granularity flexibility for near-miss clone detection in big code in scale. It is challenging to detect near-miss clones in big code across large scale source repositories with hundreds of millions of lines of code (MLOC) or more. The main reason is that it requires more computing and memory resources as the scale of the source code increases. In particular, near-miss clone detection is more difficult and need more resources. In this paper, we present SNCD, a fast and scalable distributed clone detection approach. It overcomes single node CPU and memory resource limitation with MapReduce and HDFS by scalable distributed parallelization. Furthermore, it is partial index based and optimized with multi-threading strategy which further improve the efficiency. It can not only detect Type-1 and Type-2 clones but can also discover the most computationally expensive Type-3 clones for large repositories. Meanwhile, it works for both function and file granularities, and it supports many different programming languages. Experimental results show that SNCD scales better for big code with the size of code in terms of lines of code increases compared to existing clone detection techniques, with recall and precision comparable to state-of-art approaches. With BigCloneBench and the Mutation Framework, two recent and widely used benchmarks, SNCD achieves both high recall and precision, which is competitive with other existing tools.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107743"},"PeriodicalIF":6.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378369","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

A quantization-based technique for privacy preserving distributed learning

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-04 DOI: 10.1016/j.future.2025.107741

Maurizio Colombo , Rasool Asal , Ernesto Damiani , Lamees M. AlQassem , Al Anoud Almemari , Yousof Alhammadi

引用次数: 0

Separation and optimization of encryption and erasure coding in decentralized storage systems

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-04 DOI: 10.1016/j.future.2025.107739

Marcell Szabó , Ákos Recse , Róbert Szabó , Dávid Balla , Markosz Maliosz

{"title":"Separation and optimization of encryption and erasure coding in decentralized storage systems","authors":"Marcell Szabó , Ákos Recse , Róbert Szabó , Dávid Balla , Markosz Maliosz","doi":"10.1016/j.future.2025.107739","DOIUrl":"10.1016/j.future.2025.107739","url":null,"abstract":"<div><div>Entering the cloud storage market requires a high upfront investment, thus it is dominated by a few players with existing capacity. Decentralized cloud storage solutions can disrupt the status quo by allowing businesses and individuals to sell their unused storage capacity, reducing the need for large upfront investments in service infrastructure. We show that network operators providing such service can significantly decrease the traffic volume carried on the transport network, which is essential when serving mobile users, while maintaining high data security by implementing our proposed solution, of leveraging controlled replication inside the core network. Upon data uploads encryption and erasure encoding are separated, with the latter moved inside the network, enabling the arbitrary replication of storable data pieces without straining the access network. We present simulation results, showing that the proposed method reduces traffic by 20% compared to the out-of-the-box solution. Moreover, we elaborate on optimal multi-proxy placements and even optimal storage node choosings in complex ISP networks, where deep data penetration is desired, by giving ILP optimization methods and results, achieving minimal overall network load and maximum data security.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107739"},"PeriodicalIF":6.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143321951","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

Review on LoRa backscatter technology

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-03 DOI: 10.1016/j.future.2025.107742

Siaka Konaté , Changli Li , Lizhong Xu

引用次数: 0