Information Sciences最新文献

HVASR: Enhancing 360-degree video delivery with viewport-aware super resolution HVASR：利用视口感知超级分辨率加强 360 度视频传输

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-04 DOI: 10.1016/j.ins.2024.121609

{"title":"HVASR: Enhancing 360-degree video delivery with viewport-aware super resolution","authors":"","doi":"10.1016/j.ins.2024.121609","DOIUrl":"10.1016/j.ins.2024.121609","url":null,"abstract":"<div><div>In recent years, 360-degree videos have gained significant traction due to their capacity to provide immersive experiences. However, the adoption of 360-degree videos substantially escalates bandwidth demands, necessitating approximately four to ten times more bandwidth than traditional video formats do. This presents a considerable challenge in maintaining high-quality videos in environments characterized by limited bandwidth or unstable networks. A trend has emerged where client-side computational power and deep neural networks are employed to enhance video quality while mitigating bandwidth requirements within contemporary video delivery systems. These approaches segment a video into discrete chunks and apply super resolution (SR) models to each segment, streaming low-resolution (LR) chunks alongside their corresponding SR models to the client. Although these methods enhance both video quality and transmission efficiency for conventional videos, they impose greater computational resource demands when applied to 360-degree content, thereby constraining widespread implementation. This paper introduces an innovative method called HVASR for 360-degree videos that leverages viewport information for more precise segmentation and minimizes model training costs as well as bandwidth requirements. Additionally, HVASR incorporates a viewport-aware training strategy that is aimed at further enhancing performance while reducing computational expenses. The experimental results demonstrate that HVASR achieves an average utility increase ranging from 12.46% to 40.89% across various scenes.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594109","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

Wavelet structure-texture-aware super-resolution for pedestrian detection 用于行人检测的小波结构-纹理感知超分辨率

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-04 DOI: 10.1016/j.ins.2024.121612

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

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

{"title":"KNEG-CL: Unveiling data patterns using a k-nearest neighbor evolutionary graph for efficient clustering","authors":"","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":null,"pages":null},"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

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

{"title":"A learning-based artificial bee colony algorithm for operation optimization in gas pipelines","authors":"","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":null,"pages":null},"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

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

{"title":"Privacy-preserving MTS anomaly detection for network devices through federated learning","authors":"","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":null,"pages":null},"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

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

{"title":"EMD-based ultraviolet radiation prediction for sport events recommendation with environmental constraint","authors":"","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":null,"pages":null},"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

Detecting fuzzy-rough conditional anomalies 检测模糊粗糙条件异常

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-28 DOI: 10.1016/j.ins.2024.121560

{"title":"Detecting fuzzy-rough conditional anomalies","authors":"","doi":"10.1016/j.ins.2024.121560","DOIUrl":"10.1016/j.ins.2024.121560","url":null,"abstract":"<div><div>The purpose of conditional anomaly detection is to identify samples that significantly deviate from the majority of other samples under specific conditions within a dataset. It has been successfully applied to numerous practical scenarios such as forest fire prevention, gas well leakage detection, and remote sensing data analysis. Aiming at the issue of conditional anomaly detection, this paper utilizes the characteristics of fuzzy rough set theory to construct a conditional anomaly detection method that can effectively handle numerical or mixed attribute data. By defining the fuzzy inner boundary, the subset of contextual data is first divided into two parts, i.e. the fuzzy lower approximation and the fuzzy inner boundary. Subsequently, the fuzzy inner boundary is further divided into two distinct segments: the fuzzy abnormal boundary and the fuzzy main boundary. So far, three-way regions can be obtained, i.e., the fuzzy abnormal boundary, the fuzzy main boundary, and the fuzzy lower approximation. Then, a fuzzy-rough conditional anomaly detection model is constructed based on the above three-way regions. Finally, a related algorithm is proposed for the detection model and its effectiveness is verified by data experiments.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538568","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