Information Sciences最新文献_第8页

Bipartite formation control of second-order multi-agent systems with antagonistic interactions under dynamic event-triggered adaptive schemes 动态事件触发自适应方案下具有拮抗相互作用的二阶多代理系统的两方形成控制

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-31 DOI: 10.1016/j.ins.2024.121606

Zhenwei Liang , Ze Tang , Dong Ding , Jianwen Feng

引用次数: 0

A modified gamma process for RUL prediction based on data with time-varying heavy-tailed distribution 基于时变重尾分布数据的 RUL 预测修正伽马过程

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-31 DOI: 10.1016/j.ins.2024.121603

Daniel Kuzio , Radosław Zimroz , Agnieszka Wyłomańska

{"title":"A modified gamma process for RUL prediction based on data with time-varying heavy-tailed distribution","authors":"Daniel Kuzio , Radosław Zimroz , Agnieszka Wyłomańska","doi":"10.1016/j.ins.2024.121603","DOIUrl":"10.1016/j.ins.2024.121603","url":null,"abstract":"<div><div>Predicting remaining useful life (RUL) plays a critical role in condition-based maintenance (CBM). However, this task remains challenging as the collected data often has time-varying, non-homogenous properties (trend, variance) and exhibits non-Gaussian distributions. These aspects pose significant challenges to the classical approaches. To address these issues, we propose a modification of the gamma process (suitable for non-Gaussian data) to predict RUL for the degradation process with time-varying characteristics. Specifically, we use the Gompertz cumulative hazard function instead of the linear function used in the classical approach. The modified gamma process aims to outperform the classical one and other variants already used by exploiting its probabilistic nature to effectively manage the uncertainty in the degradation curve. In order to evaluate the effectiveness of the proposed approach, extensive experiments are performed on simulated data with both Gaussian and non-Gaussian distributions. In addition, the performance of the model is validated in real-world scenarios using two benchmark datasets. The results consistently demonstrate the effectiveness of the proposed approach for both simulated and real datasets.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121603"},"PeriodicalIF":8.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Fréchet and Gateaux gH-differentiability for interval valued functions of multiple variables 多变量区间值函数的 Fréchet 和 Gateaux gH 可微性

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-30 DOI: 10.1016/j.ins.2024.121601

Luciano Stefanini , Manuel Arana-Jiménez , Laerte Sorini

引用次数: 0

KNEG-CL: Unveiling data patterns using a k-nearest neighbor evolutionary graph for efficient clustering KNEG-CL：利用 k 近邻进化图揭示数据模式，实现高效聚类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-30 DOI: 10.1016/j.ins.2024.121602

Zexuan Fei , Yan Ma , Jinfeng Zhao , Bin Wang , Jie Yang

{"title":"KNEG-CL: Unveiling data patterns using a k-nearest neighbor evolutionary graph for efficient clustering","authors":"Zexuan Fei , Yan Ma , Jinfeng Zhao , Bin Wang , Jie Yang","doi":"10.1016/j.ins.2024.121602","DOIUrl":"10.1016/j.ins.2024.121602","url":null,"abstract":"<div><div>Existing clustering algorithms often struggle to handle datasets that are diverse and complex, largely due to a dependency on Euclidean distance. Accurately quantifying distances between data points also poses challenges in practical scenarios. Additionally, the curse of dimensionality in high-dimensional datasets also impacts the performance of clustering algorithms. This paper introduces an innovative approach to overcome these challenges: the <em>k</em>-nearest neighbor evolution graph (kNEG), an unweighted directed graph that evolves by incrementally adding directed edges as the value of <em>k</em> increases. This design captures intricate details such as data point density and the direction of density variation. We present kNEG-CL, a clustering algorithm derived from kNEG, which leverages vertex degree and edge directionality to intuitively cluster data. kNEG-CL is guided by two principles: using vertex degrees to identify density peaks, and assessing a balance of outgoing and incoming edges for subcluster merging. By identifying density peaks and utilizing a density-boosting search for initial partitioning, followed by a two-stage merging process, our algorithm achieves high clustering accuracy. Extensive testing across varied datasets demonstrates the superior performance of kNEG-CL, particularly in handling large-scale and high-dimensional data, highlighting its effectiveness in clustering accuracy and computational efficiency.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121602"},"PeriodicalIF":8.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

UniBin: Assembly semantic-enhanced binary vulnerability detection without disassembly UniBin：无需反汇编即可进行装配语义增强型二进制漏洞检测

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-30 DOI: 10.1016/j.ins.2024.121605

Li Liu, Shen Wang, Xunzhi Jiang

{"title":"UniBin: Assembly semantic-enhanced binary vulnerability detection without disassembly","authors":"Li Liu, Shen Wang, Xunzhi Jiang","doi":"10.1016/j.ins.2024.121605","DOIUrl":"10.1016/j.ins.2024.121605","url":null,"abstract":"<div><div>The widespread reuse of open-source code amplifies the impact of vulnerabilities. Current vulnerability detection methods predominantly rely on binary code similarity comparisons, which involve disassembling to obtain assembly code or control flow graphs. These methods depend on specific disassembly tools and complex preprocessing, limiting their applicability and detection speed. This paper proposes UniBin, a vulnerability detection method based on the multi-layer Transformer encoder. By employing bidirectional LM, unidirectional LM, and sequence-to-sequence LM tasks on both binary and assembly code during the pre-training phase, UniBin learns richer semantic information from binary machine code, enabling efficient similarity comparison without disassembly and mitigating the limitations of disassembly. We cross-compile 55 widely used open-source C projects as datasets. After 52 hours of pre-training and 8 hours of fine-tuning, UniBin reaches an average accuracy of 98.3% in similarity detection across compilation conditions, outperforming the state-of-the-art method. For search tasks across optimization options with a pool size of 1000, the Recall@1 metric improves by 28.2% (from 67.9% to 87.1%). UniBin eliminates dependency on specific disassembly tools and improves end-to-end binary analysis speed by over 36%. In real-world vulnerability detection tasks, UniBin detects all vulnerability functions with the lowest false positive rate of 0.16%.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121605"},"PeriodicalIF":8.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 learning-based artificial bee colony algorithm for operation optimization in gas pipelines 一种基于学习的人工蜂群算法，用于优化天然气管道的运行

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-28 DOI: 10.1016/j.ins.2024.121593

Min Liu , Yundong Yuan , Aobo Xu , Tianhu Deng , Ling Jian

{"title":"A learning-based artificial bee colony algorithm for operation optimization in gas pipelines","authors":"Min Liu , Yundong Yuan , Aobo Xu , Tianhu Deng , Ling Jian","doi":"10.1016/j.ins.2024.121593","DOIUrl":"10.1016/j.ins.2024.121593","url":null,"abstract":"<div><div>The operation optimization of compressors is crucial for powering natural gas transportation and minimizing the gas consumption of the compressors themselves. In the literature, continuous control variables are typically discretized to cope with the curse of dimensionality by traditional dynamic programming methods and meta-heuristics, such as genetic algorithms and ant colony optimization. To provide a more accurate prediction, we developed a learning-based artificial bee colony (ABC) algorithm by integrating deep reinforcement learning. The merits of this innovation lie in two folds: 1) introduces function approximation to address challenges posed by the continuous state associated with gas consumption; and 2) improves the basic ABC's search capacity and reduces the risk of converging into local optima. Furthermore, the technology of multi-label classification is employed in the function approximation method to support the simultaneous optimal control of compressors in all stations, which can significantly enhance decision efficiency. Computational studies on real data demonstrate that the proposed method outperforms existing methods in the literature in terms of gas consumption.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121593"},"PeriodicalIF":8.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Observer-based event-triggered control for H∞ synchronization of complex networks 基于观测器的事件触发控制，实现复杂网络的 H∞ 同步

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-28 DOI: 10.1016/j.ins.2024.121589

Jiarong Li , Jun-Guo Lu , Jinling Wang , Guoliang Zhang , Qing-Hao Zhang , Haijun Jiang

引用次数: 0

Privacy-preserving MTS anomaly detection for network devices through federated learning 通过联合学习为网络设备提供保护隐私的 MTS 异常检测

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-28 DOI: 10.1016/j.ins.2024.121590

Shenglin Zhang , Ting Xu , Jun Zhu , Yongqian Sun , Pengxiang Jin , Binpeng Shi , Dan Pei

{"title":"Privacy-preserving MTS anomaly detection for network devices through federated learning","authors":"Shenglin Zhang , Ting Xu , Jun Zhu , Yongqian Sun , Pengxiang Jin , Binpeng Shi , Dan Pei","doi":"10.1016/j.ins.2024.121590","DOIUrl":"10.1016/j.ins.2024.121590","url":null,"abstract":"<div><div>In the context of Maintenance-as-a-Service (MaaS), it is important for device vendors to develop multivariate time series (MTS) anomaly detection models that can accurately identify anomalies without compromising the privacy of customer enterprises' data. In this paper, we investigate the relationship between MTS data patterns and the parameters of unsupervised autoencoder (AE) models and show that they are highly consistent. Building on this insight, we propose a novel unsupervised federated learning (FL)-based framework called <em>OmniFed</em>, which cannot only address the heterogeneity of non-independent identically (non-iid) distributed data on different devices, but also achieve high-precision detection of device MTS anomalies while ensuring privacy. Specifically, <em>OmniFed</em> is initialized with an AE model and then trains local AE models on individual devices via federated learning. Finally, <em>OmniFed</em> clusters devices based on the parameters of the AE models and trains a cluster-specific MTS anomaly detection model using FL. Our experiments on two real-world datasets demonstrate that <em>OmniFed</em> achieves an F1-Score of 0.921, significantly higher than the best baseline method.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121590"},"PeriodicalIF":8.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-28 DOI: 10.1016/j.ins.2024.121591

AN. Surya, J. Vimala

引用次数: 0

EMD-based ultraviolet radiation prediction for sport events recommendation with environmental constraint 基于 EMD 的紫外线辐射预测，用于具有环境约束条件的体育赛事推荐

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-28 DOI: 10.1016/j.ins.2024.121592

Ping Liu , Yazhou Song , Junjie Hou , Yanwei Xu

{"title":"EMD-based ultraviolet radiation prediction for sport events recommendation with environmental constraint","authors":"Ping Liu , Yazhou Song , Junjie Hou , Yanwei Xu","doi":"10.1016/j.ins.2024.121592","DOIUrl":"10.1016/j.ins.2024.121592","url":null,"abstract":"<div><div>With the rising awareness of health and wellness, accurate ultraviolet (UV) radiation forecasts have become crucial for planning and conducting outdoor activities safely, particularly in the context of global sporting events arrangement and recommendation with definite constraint on environmental conditions. The dynamic nature of UV exposure, influenced by factors such as solar zenith angles, cloud cover, and atmospheric conditions, makes accurate UV radiation data forecasting difficult and challenging. To cope with these challenges, we present an innovative approach for predicting the UV radiation levels of a certain region during a certain time period using Empirical Mode Decomposition (EMD), a robust method for analyzing non-linear and non-stationary data. Our model is specifically designed for urban areas, where outdoor events are common, and integrates meteorological data with historical UV radiation records from specific geographic regions and time periods. The EMD-based model offers precise predictions of UV levels, essential for event organizers and city planners to make informed decisions regarding the scheduling, relocation and recommendation of events to minimize health risks associated with UV exposure. At last, the effectiveness of this model is validated through various experiments across different spatial and temporal contexts based on the Urban-Air dataset (recording 2,891,393 Air Quality Index data that cover four major Chinese cities), demonstrating its adaptability and accuracy under diverse environmental conditions.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121592"},"PeriodicalIF":8.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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