Information Sciences最新文献

{"title":"Large-scale consensus in incomplete social network with non-cooperative behaviors and dimension reduction","authors":"Wenxiu Ma ,&nbsp;Jia Lv ,&nbsp;Xiaoli Tian ,&nbsp;Ondrej Krejcar ,&nbsp;Enrique Herrera-Viedma","doi":"10.1016/j.ins.2024.121563","DOIUrl":"10.1016/j.ins.2024.121563","url":null,"abstract":"<div><div>Obtaining a consensus solution is a formidable challenge for large-scale decision-making (LSDM) in a social network (SN). The reaching of large-scale consensus in SN is hindered by serious difficulties, including complex decision information, incomplete social relations, a multitude of decision-makers (DMs), and non-cooperative behaviors. This paper introduces a novel three-stage consensus framework that systematically addresses these challenges by data preprocessing, dimension reduction, and optimization modeling. Firstly, the cloud model is applied to convert the probabilistic linguistic information into numerical information, facilitating computational analysis. Meanwhile, an improved t-norm trust propagation method that incorporates the impact of opinion similarity is developed, ensuring the completeness of SN. Secondly, an improved Louvain algorithm is designed to divide large group into cohesive subgroups, enhancing the manageability of LSDM. On this basis, a three-stage consensus optimization that considers non-cooperative behaviors is proposed, which boasts threefold benefits: (i) Assures the synchronous achievement of local and global consensus. (ii) Implements self-adaptive management mechanism of non-cooperative behaviors. (iii) Provides acceptable adjusted opinions for subgroups and DMs. Finally, detailed numerical experiments and comparative analyses are given to demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121563"},"PeriodicalIF":8.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530745","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":"Hierarchical degradation-aware network for full-reference image quality assessment","authors":"Xuting Lan ,&nbsp;Fan Jia ,&nbsp;Xu Zhuang ,&nbsp;Xuekai Wei ,&nbsp;Jun Luo ,&nbsp;Mingliang Zhou ,&nbsp;Sam Kwong","doi":"10.1016/j.ins.2024.121557","DOIUrl":"10.1016/j.ins.2024.121557","url":null,"abstract":"<div><div>Full-Reference Image Quality Assessment (FR-IQA) algorithms excel in evaluating perceptual distortions by comparing reference and distorted images. However, as the severity and quantity of distortions in datasets increase, existing FR-IQA methods struggle to capture complex nonlinear perceptual features. This limitation results in reduced adaptability and inaccurate assessments for images with more severe or multiple distortions. Recognizing the importance of understanding image degradation mechanisms, we propose a novel hierarchical degradation-aware network (HDaN) method. First, by exploring the degradation mechanisms from the reference image to the distorted image, our degradation network matches distortions that align more closely with the human visual system (HVS). Next, we design a convertor to project the matched features into multiple spaces, creating multidimensional feature representations that more comprehensively capture the complexity of image distortions rather than being confined to a single feature space. Then, we calculate a similarity matrix between the distorted and mapped features, selecting the most similar (top-k) features for merging. Finally, a regression network maps the merged features to quality scores, providing the final quality prediction. The experimental results demonstrate that our proposed HDaN method outperforms traditional deep learning-based FR-IQA methods. Specifically, the HDaN shows higher PLCC and SROCC metrics on benchmark datasets, significantly improving over existing methods. Moreover, the method exhibits better adaptability to images with varying degrees and types of distortions, thereby greatly enhancing the overall performance of IQA.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121557"},"PeriodicalIF":8.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530651","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":"Graph-based stock prediction with multisource information and relational data fusion","authors":"Qiuyue Zhang ,&nbsp;Yunfeng Zhang ,&nbsp;Fangxun Bao ,&nbsp;Yang Ning ,&nbsp;Caiming Zhang ,&nbsp;Peide Liu","doi":"10.1016/j.ins.2024.121561","DOIUrl":"10.1016/j.ins.2024.121561","url":null,"abstract":"<div><div>With the application of multisource information in different fields, the combination of different types of information, such as numerical data and text information, has become a favourable choice for performing stock market analyses. Despite the rich information provided by multisource data, building structured relationships remains challenging. In addition, some market relationship-based analysis methods use a predefined graph structure as a stock relationship graph, which makes it impossible to sensitively aggregate attribute features, and these methods cannot dynamically update market relationships or relationship strengths. In this paper, we propose a novel dynamic attribute-driven graph attention network incorporating sentiment (AGATS) information, transaction data, and text data. Inspired by behavioural finance, we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through tensor fusion. In particular, real-time intramarket dependencies and key attribute information are captured with graph networks, enabling dynamic relationship and relationship strength updates. Experiments conducted on real datasets show that our model is capable of ourperforming previously developed methods in prediction and trading.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121561"},"PeriodicalIF":8.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445333","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":"Learnable self-supervised support vector machine based individual selection strategy for multimodal multi-objective optimization","authors":"Xiaochuan Gao ,&nbsp;Weiting Bai ,&nbsp;Qianlong Dang ,&nbsp;Shuai Yang ,&nbsp;Guanghui Zhang","doi":"10.1016/j.ins.2024.121553","DOIUrl":"10.1016/j.ins.2024.121553","url":null,"abstract":"<div><div>Multimodal multi-objective optimization problem (MMOP) is a frontier research problem, which can provide decision makers with more choices without making trade-offs. Many multimodal multi-objective evolutionary algorithms (MMOEAs) have been proposed to solve MMOP. However, most MMOEAs tend to prioritize the objective dominance of individuals in the process of individual selection, and only individuals with the same objective dominance will be considered the diversity, which leads to the loss of many promising solutions. To solve the above problem, this paper proposes a learnable self-supervised support vector machine (SVM) based multimodal multi-objective optimization algorithm (SVMEA). Support vector machine can learn the knowledge about distinguishing the advantages and disadvantages of individuals from the data in the existing training set and select individuals, in which the objective dominance of individuals is as important as diversity. Moreover, a crowding distance calculation method based on Manhattan distance is designed. Compared with the traditional method using Euclidean distance to calculate crowding distance, it can better evaluate the diversity of individuals in the decision space and assist the selection of elite solutions. Experimental results show that the proposed SVMEA is competitive with seven other advanced MMOEAs on 34 benchmark problems and a practical application problem.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121553"},"PeriodicalIF":8.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445327","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 joint vehicular device scheduling and uncertain resource management scheme for Federated Learning in Internet of Vehicles","authors":"Jianghui Cai ,&nbsp;Bujia Chen ,&nbsp;Jie Wen ,&nbsp;Zhihua Cui ,&nbsp;Jinjun Chen ,&nbsp;Wensheng Zhang","doi":"10.1016/j.ins.2024.121552","DOIUrl":"10.1016/j.ins.2024.121552","url":null,"abstract":"<div><div>Federated learning (FL) offers an effective framework for the efficient process in vehicular edge computing. However, FL encompasses the process of distributing and uploading model parameters, which are inevitably transmitted in a wireless network environment. Some challenges in FL-assisted Internet of Vehicles (IoV) sceneries gradually emerging, such as data heterogeneity, concerned device resources, and unstable communication environment, which necessitate intelligent vehicle selection schemes that accelerate training efficiency. Based on these, we consider a new scenario, specifically an FL-assisted IoV system under uncertain communication conditions, and develop an interval many-objective vehicle selection and bandwidth allocation (IMoVSBA) joint optimization scheme. This scheme takes into account computation latency, energy consumption, server utilization, and data quality, while meeting multi-criteria resource optimization requirements. Among these, server utilization is a new objective designed specifically for this joint optimization problem. For the proposed problem, a novel interval many-objective evolutionary algorithm with individual comprehensive indicator to control the evolution direction (IMaOEACI) is designed. Simulation results demonstrate that this method outperforms other schemes in terms of accuracy, training cost, and server utilization, effectively improving training efficiency in wireless channel environments and reasonably utilizing bandwidth resources. It provides significant scientific value and application potential in the field of the IoVs.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121552"},"PeriodicalIF":8.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530609","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":"Defending against backdoor attack on deep neural networks based on multi-scale inactivation","authors":"Anqing Zhang ,&nbsp;Honglong Chen ,&nbsp;Xiaomeng Wang ,&nbsp;Junjian Li ,&nbsp;Yudong Gao ,&nbsp;Xingang Wang","doi":"10.1016/j.ins.2024.121562","DOIUrl":"10.1016/j.ins.2024.121562","url":null,"abstract":"<div><div>Deep neural networks (DNNs) have excellent performance in various applications, especially for image classification tasks. However, DNNs also face the threat of backdoor attacks. Backdoor attacks embed a hidden backdoor into a model, after which the infected model can achieve correct classification on benign images, while incorrectly classify the images with the backdoor triggers as the target label. To obtain a clean model from a backdoor dataset, we propose a Kalman filtering based multi-scale inactivation scheme, which can effectively remove poison data in a poison dataset and obtain a clean model. Every sample in the suspicious training dataset will be judged by multi-scale inactivation and obtain a series of judging results, then data fusion is conducted using kalman filtering to determine whether it is a poison sample. To further improve the performance, a trigger localization and target determination based scheme is proposed. Extensive experiments are conducted to demonstrate the superior effectiveness of the proposed method. The results show that the proposed methods can remove poison samples effectively, and achieve greater than 99% recall rate, and the attack success rate of the retrained clean model is smaller than 1%.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121562"},"PeriodicalIF":8.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445330","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":"Obfuscation mechanism for simultaneous public event information release and private event information hiding in discrete event systems","authors":"Wei Duan ,&nbsp;Christoforos N. Hadjicostis ,&nbsp;Zhiwu Li","doi":"10.1016/j.ins.2024.121554","DOIUrl":"10.1016/j.ins.2024.121554","url":null,"abstract":"<div><div>In this paper, we study the problem of simultaneous public event information release and private event information hiding in discrete event systems modeled by partially observed non-deterministic finite automata, where the public and private event information is respectively defined as unobservable fault and secret events. The notion of <em>D-C-compossibility</em> is introduced to characterize whether a system simultaneously conceals the occurrences of secret events while releasing the occurrences of fault events. When such a property does not hold, we investigate its enforcement through a defensive function. This function takes each observable event of a system as input and generates a suitably modified event sequence at the system's output using event deletion, insertion, or substitution. Specifically, our focus is on prioritizing the concealment of the secret events while maximizing the detection of the fault events through the use of defensive functions. The notion of <em>D-C-enforceability</em> that refers to the capability of a defensive function to employ a strategy for manipulating observations of a system to enforce <em>D</em>-<em>C</em>-compossibility is proposed. That is, a <em>D</em>-<em>C</em>-enforcing defensive function ensures that all occurrences of fault events can be revealed while concealing all occurrences of secret events. A <em>D</em>-<em>C</em>-diagnoser construction is proposed to enumerate all feasible defensive actions following system behavior. By taking advantage of the <em>D</em>-<em>C</em>-diagnoser, we obtain a necessary and sufficient condition for <em>D</em>-<em>C</em>-enforceability, along with a corresponding obfuscation strategy for defensive functions.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121554"},"PeriodicalIF":8.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530820","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":"Minimal solutions of fuzzy relation equations via maximal independent elements","authors":"David Lobo ,&nbsp;Jesús Medina ,&nbsp;Timo Camillo Merkl ,&nbsp;Reinhard Pichler","doi":"10.1016/j.ins.2024.121558","DOIUrl":"10.1016/j.ins.2024.121558","url":null,"abstract":"<div><div>Fuzzy relation equations (FRE) are a useful formalism with a broad number of applications in different computer science areas. Testing if a solution exists and, if so, computing the unique greatest solution is straightforward. In contrast, the computation of minimal solutions is more complex. In particular, even in FRE with a very simple structure, the number of minimal solutions can increase exponentially. However, minimal solutions are immensely useful since, under mild conditions, they (together with the greatest solution) allow one to describe the entire space of solutions to an FRE. The main result of this work is a new method for enumerating the set of minimal solutions. It works by establishing a relationship between coverings of FRE and maximal independent elements of (hyper-)boxes. We can thus make efficient enumeration methods for maximal independent elements of (hyper-)boxes applicable also to our setting of FRE, where the operator considered in the composition of fuzzy relations only needs to preserve suprema of arbitrary subsets and infima of non-empty subsets. More specifically, we thus show that the enumeration of the minimal solutions of an FRE can be done with incremental quasi-polynomial delay.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121558"},"PeriodicalIF":8.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530608","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":"A distance-based network activity correlation framework for defeating anonymization overlays","authors":"Ugo Fiore,&nbsp;Francesco Palmieri","doi":"10.1016/j.ins.2024.121559","DOIUrl":"10.1016/j.ins.2024.121559","url":null,"abstract":"<div><div>As the effectiveness of modern Internet-based anonymization infrastructures grows, law enforcement agencies are experiencing a progressive erosion of their surveillance capabilities. This can severely undermine their efforts to prevent and investigate various types of unlawful activities, potentially increasing the impunity of organized criminal networks. Balancing the legitimate privacy needs of individuals with the imperative to maintain public safety and combat criminal behavior in the digital world remains a complex tradeoff for both policymakers and technologists who need to find a systematic and reliable way to link the traffic traces associated with criminal activities to their anonymized origins. Accordingly, this paper presents a simple but very effective de-anonymization approach capable of associating traffic traces captured at the edge of the overlay infrastructures, in correspondence with the true origins, to those captured in correspondence with the destinations. The approach is based on determining the minimum-distance pairs within a complete bipartite graph in which the traffic traces are the nodes. Experiments with different distance functions, applied in varied ways, show that the resulting framework appears to be a promising solution that is scalable and easily deployable on real-life network equipment.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121559"},"PeriodicalIF":8.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445328","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":"EsmamDS: A more diverse exceptional survival model mining approach","authors":"Renato Vimieiro ,&nbsp;Juliana Barcellos Mattos ,&nbsp;Paulo S.G. de Mattos Neto","doi":"10.1016/j.ins.2024.121549","DOIUrl":"10.1016/j.ins.2024.121549","url":null,"abstract":"<div><div>In this work we present an Ant Colony Optimization heuristic to find subgroups with exceptional behavior in time-to-event data. The area of time-to-event or survival data analysis has its basis in statistics, where the main goal is to predict <em>if</em> and <em>when</em> an event will happen. In other words, the main goal in survival analysis has long been to build global models able to predict the time for the occurrence of an event. Nevertheless, very often predictive models are used to compare stratified data in order to evaluate whether a variable is associated or not with the outcome. For instance, patients might be stratified according to a treatment variable (placebo or not) to compare models (survival curves) and decide on the effectiveness of the treatment. Although this is an effective approach if the variable of interest is already known, it does not provide an alternative for the cases where specialists do not know how to stratify the data, that is, if they do not know which variable could be related to the outcome. Our approach targets exactly this. Our method seeks combinations of variables that are associated, i.e. describe, subgroups of individuals with unexpected or exceptional survival curves. In this sense, we complement the literature with a descriptive approach that is able to find and characterize those groups for specialists. Our method is based on the framework of exceptional model mining. It improves on a preliminary version presented in a conference. The main enhancement was to redesign our heuristic to retrieve interesting and diverse subgroups while minimizing three aspects of redundancy: coverage; description; and model. Our second extension regards how the quality function is applied. We now allow users to control whether the quality measure compares subgroups against the population, or against individuals that do not satisfy the descriptive rule. Third, we conduct further experiments to compare the performance of our approach to state of the art algorithms with real world benchmark data sets. Finally, we also present a case study showing a possible application of our method in the bioinformatics/health domain.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121549"},"PeriodicalIF":8.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530822","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}