Future Generation Computer Systems-The International Journal of Escience最新文献_第2页

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

UFIDSF: An undersampling approach based on feature importance and double side filter for imbalanced data classification

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-02 DOI: 10.1016/j.future.2025.107750

Ming Zheng , Fei Wang , Xiaowen Hu , Liangchen Hu , Qingying Yu , Xiaoyao Zheng

{"title":"UFIDSF: An undersampling approach based on feature importance and double side filter for imbalanced data classification","authors":"Ming Zheng , Fei Wang , Xiaowen Hu , Liangchen Hu , Qingying Yu , Xiaoyao Zheng","doi":"10.1016/j.future.2025.107750","DOIUrl":"10.1016/j.future.2025.107750","url":null,"abstract":"<div><div>Imbalanced data has the potential to detrimentally impact the efficacy of machine learning algorithms. If imbalanced data is not effectively processed, it will have a great impact on the classification results and reduce the reliability and practicability of modeling, so it has received widespread attention. From the past few decades to the present, various methods have emerged to solve the problem of imbalance data classification. The most common method is to start from the data level and realize data balance by resampling method. However, it remains a challenge to ensure that more valuable data is learned during the resampling process. Therefore, this study proposes an undersampling framework (UFIDSF) based on feature importance and double side filter. The first novelty of this framework is the use of double side filter to filter noise data in both majority and minority class samples. The second novelty is the projection of data samples into one dimension. UFIDSF is realized by calculating the distance between the feature of each dimension of the sample and its nearest neighbor and combining the feature importance. Experiments were conducted on 30 common benchmark imbalanced datasets, comparing the performance of 10 methods across four classifiers. Experimental results show that the proposed UFIDSF is effective and stable, and can significantly improve the adverse effects of machine learning algorithms on imbalanced data.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107750"},"PeriodicalIF":6.2,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143321950","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

Edge-cloud solutions for big data analysis and distributed machine learning - 2

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-01 DOI: 10.1016/j.future.2025.107745

Loris Belcastro , Jesus Carretero , Domenico Talia

{"title":"Edge-cloud solutions for big data analysis and distributed machine learning - 2","authors":"Loris Belcastro , Jesus Carretero , Domenico Talia","doi":"10.1016/j.future.2025.107745","DOIUrl":"10.1016/j.future.2025.107745","url":null,"abstract":"<div><div>In recent years, edge-cloud solutions have gained widespread adoption for efficiently collecting and analyzing IoT-generated data across various domains like urban mobility, healthcare, and smart cities. These solutions integrate resources from edge to cloud to support real-time processing and analysis tasks, reducing latency and network congestion. Big data analysis within this paradigm involves sophisticated techniques for distributed data processing, enabling applications such as predictive maintenance and smart grid management. Nevertheless, carrying out big data analysis within the edge-cloud presents several challenges, including data privacy and security, interoperability, scalability, and energy efficiency. Addressing these challenges is imperative for providing efficient and scalable solutions for data-intensive applications like federated learning, social data analysis, smart city services, and text mining. The special issue concludes with 27 scientific papers, divided into two parts for a streamlined editorial process. This editorial, as part two, presents 12 rigorously peer-reviewed papers, complementing the 15 papers covered in the previous editorial.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107745"},"PeriodicalIF":6.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143321952","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

P2-TaskMP: Privacy-Preserving Task Allocation Optimization Based on Mobility Prediction

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-01 DOI: 10.1016/j.future.2025.107720

Zhidong Xie , Tao Peng , Wei You , Guojun Wang , Qin Liu , Entao Luo

{"title":"P2-TaskMP: Privacy-Preserving Task Allocation Optimization Based on Mobility Prediction","authors":"Zhidong Xie , Tao Peng , Wei You , Guojun Wang , Qin Liu , Entao Luo","doi":"10.1016/j.future.2025.107720","DOIUrl":"10.1016/j.future.2025.107720","url":null,"abstract":"<div><div>The emergence of Mobile Crowd Sensing (MCS) has provided a new paradigm for data sensing. An effective task allocation can ensure the stability and efficiency of the system in MCS. In this paper, we propose a privacy-preserving multi-objective, multi-task allocation optimization scheme, P2-TaskMP (Privacy-Preserving Task Allocation Optimization based on Mobility Prediction), to solve the multi-objective optimization task assignment problem while preserving users’ privacy. The scheme evaluates participants’ task completion capabilities by introducing mobility prediction based on fuzzy logic, which then informs task pre-allocation to form the initial population, unlike traditional methods that initialize populations randomly. To address potential privacy leaks of participants’ spatiotemporal information during mobility prediction, we employ differential privacy techniques to add Laplace noise to participants’ historical trajectory records, achieving adequate privacy protection. P2-TaskMP achieves Pareto-optimal solutions using the NSGA-II-DE (Non-dominated Sorting Genetic Algorithm II with Differential Evolution) algorithm and realizes satisfactory results with fast solution speed for large-scale task allocation problems. Simulations conducted on two real-world datasets demonstrate that our proposed method achieves higher accuracy, and the task allocation algorithm performs better than the compared algorithms in maximizing task completion rate and minimizing cost.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107720"},"PeriodicalIF":6.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378368","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

Achieving efficient and accurate privacy-preserving localization for internet of things: A quantization-based approach

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-01 DOI: 10.1016/j.future.2025.107740

Guanghui Wang , Xueyuan Zhang , Lingfeng Shen , Shengbo Chen , Fei Tong , Xin He , Wenyao Li

{"title":"Achieving efficient and accurate privacy-preserving localization for internet of things: A quantization-based approach","authors":"Guanghui Wang , Xueyuan Zhang , Lingfeng Shen , Shengbo Chen , Fei Tong , Xin He , Wenyao Li","doi":"10.1016/j.future.2025.107740","DOIUrl":"10.1016/j.future.2025.107740","url":null,"abstract":"<div><div>Privacy-preserving localization is an important enabling technology for location-based applications on the Internet of Things (IoT). Existing work utilizes encryption or noise-adding mechanism to develop privacy-preserving methods during the localization process. However, these methods still face the challenge of simultaneously achieve localization accuracy, privacy preservation and communication efficiency. To address the challenge, in this paper, a novel quantization-based privacy-preserving localization (QPPL) algorithm is proposed to estimate the target’s location with accuracy, privacy preservation and communication efficiency at the same time. Firstly, the location information is quantized, i.e., deviate the location data, to preserve the private location information during the localization process. With the quantization on the location information, the data scale is compressed to reduce communication cost and improve localization efficiency. Then, to improve the localization accuracy, an optimal weight allocation scheme is designed to aggregate the location estimates from the heterogeneous anchor devices. By minimizing the weighted sum of squared quantization errors of all anchor devices, a closed form optimal weight allocation scheme is derived by using convex optimization theory. Finally, through theoretical analysis, we prove the accuracy, privacy preservation and efficiency of the QPPL algorithm. Experimental evaluation demonstrates that QPPL has superior performance compared with existing methods.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107740"},"PeriodicalIF":6.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348210","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