Information Sciences最新文献

{"title":"Hybrid rotation self-supervision and feature space normalization for class incremental learning","authors":"Wenyi Feng ,&nbsp;Zhe Wang ,&nbsp;Qian Zhang ,&nbsp;Jiayi Gong ,&nbsp;Xinlei Xu ,&nbsp;Zhilin Fu","doi":"10.1016/j.ins.2024.121618","DOIUrl":"10.1016/j.ins.2024.121618","url":null,"abstract":"<div><div>Class incremental learning has made great progress in solving the problem of catastrophic forgetting through knowledge distillation method and sample playback method. However, the existing class incremental learning methods still face the problems of limited feature representation and lack of normalized feature space, which makes them perform poorly in long-term incremental tasks. To address the above problems in class incremental learning, we propose a non-exemplar based method named Hybrid Rotation with Feature Space Normalization (HRFSN). Firstly, a novel self-supervised method called Hybrid Rotation Self-supervision (HRS) is designed to overcome the problem of limited features. HRS uses random positive samples to perform rotation prediction tasks, and makes the feature extractor learn more rich feature expression ability through complex rotation prediction tasks. Secondly, to make the learned features more generalized, Feature Space Normalization (FSN) is introduced to constrain the feature value to a normal distribution, which is well matched with HRS. Experimental results on benchmark datasets such as CIFAR-100 and Tiny-Imagenet show that our approach significantly outperforms mainstream incremental learning methods and achieves comparable performance compared to the state-of-the-art methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121618"},"PeriodicalIF":8.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658124","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}

{"title":"A many-objective evolutionary algorithm based on indicator selection and adaptive angle estimation","authors":"Qian Wang ,&nbsp;Qinghua Gu ,&nbsp;Qing Zhou ,&nbsp;Naixue Xiong ,&nbsp;Di Liu","doi":"10.1016/j.ins.2024.121608","DOIUrl":"10.1016/j.ins.2024.121608","url":null,"abstract":"<div><div>Currently, real-world complex engineering problems often have more than three objectives that need to be optimized simultaneously. Existing algorithms for solving them mostly rely on the shape of the Pareto front to maintain diversity, which makes it difficult to effectively balance the convergence and diversity of the solutions. To address the above problems, a many-objective evolutionary algorithm based on indicator selection and adaptive angle estimation (MaOEA-ISAE) is proposed in this paper. Firstly, an environmental selection strategy based on a unit vector indicator is presented to split the retention of dominant individuals into two phases to balance the diversity and convergence of the population. Then, to preserve the diversity of the population, an adaptive angle estimation strategy based on Pareto front curvature prediction is developed to select individuals with good diversity in the dominant region. Besides, the algorithm has a simple evolutionary process, and no additional parameters are involved. Finally, the proposed MaOEA-ISAE is compared with seven other representative many-objective evolutionary algorithms on test instances of 3–20 objectives with different Pareto front shapes and real-world water resource management problem. The experimental results show that the proposed algorithm has an overall win rate of about more than 60 %, indicating a strong competitiveness on problems with different Pareto fronts.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121608"},"PeriodicalIF":8.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658122","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}

{"title":"Local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on local fuzzy self information","authors":"Zhaowen Li ,&nbsp;Run Guo ,&nbsp;Ning Lin ,&nbsp;Tao Lu","doi":"10.1016/j.ins.2024.121613","DOIUrl":"10.1016/j.ins.2024.121613","url":null,"abstract":"<div><div>The advent of the era of big data is accompanied by the generation of large-scale data of various types. Extracting the potential value and rules from such data has always been a challenge. Due to various external and internal factors, it is commonplace for large-scale data to exhibit the phenomenon of missing limited labels. In addressing a large-scale mixed information system with limited label missing (LSMDISLML), local neighborhood rough set model (LNRS-model) is typically employed. However, the identical neighborhood radius is often used by such model when confronted with numerical attributes, which could potentially attenuate the classification capability of the data. Local fuzzy rough set model (LFRS-model) can overcome this point. This paper studies local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on LFRS-model via local fuzzy self information and overlap degree function. First, leveraging the statistical distribution of data as a foundation, fuzzy relations on the entire sample set are established, which has the advantage of being able to use different fuzzy similarity radii to calculate similarity, thereby adapting to different data distributions. Subsequently, the samples with missing labels are discarded as they constitute a small proportion of the entire sample set and have little impact on overall performance of dataset. The limited computing resources and storage space are focused on the sample set with complete labels (denoted as target set). Thereafter, based on the target set, local fuzzy <em>λ</em>-upper and lower approximations are defined, and LFRS-model is constructed. This model not only reduces processing time and sources of error in large-scale data but also improves data quality and enhances the reliability of the experimental results. Then, local fuzzy <em>λ</em>-self information is introduced and used to design a local fuzzy rough attribute reduction algorithm in a LSMDISLML. Furthermore, a overlap degree function is introduced to evaluate and reorder the attributes based on their importance, prioritizing the elimination of redundant attributes with high overlap and low importance from the preordered attribute set. This strategy effectively improves the efficiency of obtaining the optimal subset. Finally, a series of experiments are carried out. The experiment results demonstrate that the designed algorithm exhibits excellent performance in classification tasks and outlier detection tasks, surpassing existing four algorithms.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121613"},"PeriodicalIF":8.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658186","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}

{"title":"Blending is all you need: Data-centric ensemble synthetic data","authors":"Alex X. Wang ,&nbsp;Colin R. Simpson ,&nbsp;Binh P. Nguyen","doi":"10.1016/j.ins.2024.121610","DOIUrl":"10.1016/j.ins.2024.121610","url":null,"abstract":"<div><div>Deep generative models have gained increasing popularity, particularly in fields such as natural language processing and computer vision. Recently, efforts have been made to extend these advanced algorithms to tabular data. While generative models have shown promising results in creating synthetic data, their high computational demands and the need for careful parameter tuning present significant challenges. This study investigates whether a collective integration of refined synthetic datasets from multiple models can achieve comparable or superior performance to that of a single, large generative model. To this end, we developed a Data-Centric Ensemble Synthetic Data model, leveraging principles of ensemble learning. Our approach involved a data refinement process applied to various synthetic datasets, systematically eliminating noise and ranking, selecting, and combining them to create an augmented, high-quality synthetic dataset. This approach improved both the quantity and quality of the data. Central to this process, we introduced the Ensemble <em>k</em>-Nearest Neighbors with Centroid Displacement (EKCD) algorithm for noise filtering, alongside a density score for ranking and selecting data. Our experiments confirmed the effectiveness of EKCD in removing noisy synthetic samples. Additionally, the ensemble model based on the refined synthetic data substantially enhanced the performance of machine learning models, sometimes even outperforming that of real data.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121610"},"PeriodicalIF":8.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

{"title":"Predicting cardiac infarctions with reinforcement algorithms through wavelet transform applications in healthcare","authors":"M. Pradeep ,&nbsp;Debnath Bhattacharyya ,&nbsp;Dinesh Kumar Anguraj ,&nbsp;Tai-hoon Kim ,&nbsp;Kingsley A Ogudo ,&nbsp;Moulana Mohammed","doi":"10.1016/j.ins.2024.121513","DOIUrl":"10.1016/j.ins.2024.121513","url":null,"abstract":"<div><div>Cardiovascular pathology must requires various conditions influenced by the myocardial infarction,sorted using a reinforcement algorithm with distinctive notches in the corona arterial impact ratio.The order in a dataset might consist of 306 factors and 17 chronic impute characteristics that arise with a bardic canicular conglomerate factor within the overall sanguiferous conglomerate ratio and denote the factor measure of carnage thrust,triglyceride measure, and bosom gait ratings of conglomerate rehabilitators.When existing system order with a probability variation of 86.04% in the contour ratio convexity measure,a miniature factor of conjecture with an extent notation of 85.82%,and a cabalistic miniature exposed for the heat map.‘In a proposed system by developing the decrepit search with an order measure of an anecdotal concrete factor with carnal movement,one can access the hazard factor of myocardial infarction in the anecdotal concrete factor when raising the abrupt cardiovascular method with the release of 4.96.When improving the measure of carnage thrust with 0.95,outright regime benefaction in the tranquil ratio is 2641 with a ratio variation of 23.6 by improving the triglyceride measure,and destitute factor variation is 623 with a factor variation of 18.4 when correcting the bosom gait ratings measure.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121513"},"PeriodicalIF":8.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658036","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}

{"title":"Distributed Least Product Relative Error estimation for semi-parametric multiplicative regression with massive data","authors":"Yuhao Zou ,&nbsp;Xiaohui Yuan ,&nbsp;Tianqing Liu","doi":"10.1016/j.ins.2024.121614","DOIUrl":"10.1016/j.ins.2024.121614","url":null,"abstract":"<div><div>Distributed systems have been widely used for massive data analysis, but few studies focus on multiplicative regression models. We consider a communication-efficient surrogate likelihood method using the Least Product Relative Error criterion for semi-parametric multiplicative models on massive datasets. The non-parametric component is efficiently handled via B-spline approximation. We derive the asymptotic properties for both parametric and non-parametric components, while the SCAD and adaptive Lasso penalty functions are developed and their oracle properties for variable selection are validated. Simulation studies and an application to an energy prediction dataset are used to demonstrate the effectiveness and practical utility of the proposed method.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121614"},"PeriodicalIF":8.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658034","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}

{"title":"Dynamic multi-objective optimization based on classification response of decision variables","authors":"Jianxia Li,&nbsp;Ruochen Liu,&nbsp;Ruinan Wang","doi":"10.1016/j.ins.2024.121611","DOIUrl":"10.1016/j.ins.2024.121611","url":null,"abstract":"<div><div>In recent years, many dynamic multi-objective optimization algorithms (DMOAs) have been proposed to address dynamic multi-objective optimization problems (DMOPs). Most existing DMOAs treat all decision variables uniformly and respond to them in an identical manner. This paper proposes a dynamic multi-objective optimization algorithm based on the classification response of decision variables (CRDV-DMO). Firstly, CRDV-DMO categorizes the decision variables into convergence variables and diversity variables. Different decision variables adopt distinct response strategies. The response strategy of diversity variable (RSDV) uses Latin hypercube sampling to generate the diversity variables of the new environment. For each dimensional convergence variable, the response strategy of convergence variable (RSCV) first evaluates whether the basic center prediction strategy (CPS) yields positive feedback or negative feedback, further determining the predictability of that dimensional convergence variable. RSCV then decides to either use the basic CPS to generate the convergence variable for that dimension or to retain that dimensional convergence variable from the current environment, based on the predictability of that dimensional convergence variable. The proposed algorithm is extensively studied through comparison with several advanced DMOAs, demonstrating its effectiveness in dealing with the benchmark DMOPs and the parameter-tuning problem of the PID controller on a dynamic system.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121611"},"PeriodicalIF":8.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658023","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}

{"title":"HVASR: Enhancing 360-degree video delivery with viewport-aware super resolution","authors":"Pingping Dong,&nbsp;Shangyu Li,&nbsp;Xinyi Gong,&nbsp;Lianming Zhang","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":"691 ","pages":"Article 121609"},"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}

{"title":"Matrix representation of the graph model for conflict resolution based on intuitionistic preferences with applications to trans-regional water resource conflicts in the Lancang–Mekong River Basin","authors":"Dayong Wang ,&nbsp;Xiaoying Lai ,&nbsp;Dhaarna ,&nbsp;Xiaowei Wen ,&nbsp;Yejun Xu","doi":"10.1016/j.ins.2024.121615","DOIUrl":"10.1016/j.ins.2024.121615","url":null,"abstract":"<div><div>With the continual growth in global demand for water resources, the governance of trans-regional water resources has become an important field of cooperation between countries, and conflicts caused by competition for water resources have become a focal issue in international relations. The root causes of trans-regional water resource conflicts include unclear ownership of water resources and the limited rationality of the parties involved. The water rights trading model has proven to play a stabilizing role in resolving trans-regional water resource conflicts versus the traditional negotiation model. To demonstrate the rationality and theoretical strength of the water rights trading model in allocating trans-regional water resources in uncertain environments, this paper proposes a novel matrix representation of intuitionistic stability definitions through a graph model for conflict resolution (GMCR). First, decision-makers’ (DMs’) intuitionistic preferences, unilateral movements, and intuitionistic unilateral improvements in a GMCR with two DMs are represented in a matrix. The joint unilateral movements and joint intuitionistic unilateral improvements for a coalition in a GMCR with multiple DMs are also represented. Next, according to the logical forms of intuitionistic stability definitions, four stability definitions in a GMCR with intuitionistic preferences were redefined to enable matrix representations of graph models with either two DMs or multiple DMs. Finally, we analyzed the state transitions and strategies of all stakeholders in trans-regional water resource conflicts in the Lancang-Mekong River Basin within the intuitionistic graph model. The contributions of this paper are twofold. First, in terms of theoretical research, this article enriches and develops the GMCR and proposes a set of definitions of matrix intuitionistic stability. Second, in terms of practical application, the intuitionistic stability analysis results can provide a reference for water rights trading to solve trans-regional water resource conflicts in uncertain environments and transform trans-regional water resource governance systems.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121615"},"PeriodicalIF":8.1,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658132","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}

{"title":"Wavelet structure-texture-aware super-resolution for pedestrian detection","authors":"Wei-Yen Hsu ,&nbsp;Chun-Hsiang Wu","doi":"10.1016/j.ins.2024.121612","DOIUrl":"10.1016/j.ins.2024.121612","url":null,"abstract":"<div><div>This study aims to tackle the challenge of detecting pedestrians in low-resolution (LR) images by using super-resolution techniques. The proposed Wavelet Structure-Texture-Aware Super-Resolution (WSTa-SR) method is a novel end-to-end solution that enlarges LR images into high-resolution ones and employs Yolov7 for detection, effectively solving the problems of low detection performance. The LR image is first decomposed into low and high-frequency sub-images with stationary wavelet transform (SWT), which are then processed by different sub-networks to more accurately distinguish pedestrian from background by emphasizing pedestrian features. Additionally, a high-to-low information delivery mechanism (H2LID mechanism) is proposed to transfer the information of high-frequency details to enhance the reconstruction of low-frequency structures. A novel loss function is also introduced that exploits wavelet decomposition properties to further enhance the network’s performance on both image structure reconstruction and pedestrian detection. Experimental results show that the proposed WSTa-SR method can effectively improve pedestrian detection.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121612"},"PeriodicalIF":8.1,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594067","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}