Information Processing & Management最新文献

{"title":"Online learning from drifting capricious data streams with flexible Hoeffding tree","authors":"Ruirui Zhao ,&nbsp;Yaqian You ,&nbsp;Jianbin Sun ,&nbsp;João Gama ,&nbsp;Jiang Jiang","doi":"10.1016/j.ipm.2025.104221","DOIUrl":"10.1016/j.ipm.2025.104221","url":null,"abstract":"<div><div>Capricious data streams, marked by random emergence and disappearance of features, are common in practical scenarios such as sensor networks. In existing research, they are mainly handled based on linear classifiers, feature correlation or ensemble of trees. There exist deficiencies such as limited learning capacity and high time cost. More importantly, the concept drift problem in them receives little attention. Therefore, drifting capricious data streams are focused on in this paper, and a new algorithm DCFHT (online learning from Drifting Capricious data streams with Flexible Hoeffding Tree) is proposed based on a single Hoeffding tree. DCFHT can achieve non-linear modeling and adaptation to drifts. First, DCFHT dynamically reuses and restructures the tree. The reusable information includes the tree structure and the information stored in each node. The restructuring process ensures that the Hoeffding tree dynamically aligns with the latest universal feature space. Second, DCFHT adapts to drifts in an informed way. When a drift is detected, DCFHT starts training a backup learner until it reaches the ability to replace the primary learner. Various experiments on 22 public and 15 synthetic datasets show that it is not only more accurate, but also maintains relatively low runtime on capricious data streams.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104221"},"PeriodicalIF":7.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying potentially disruptive research via a comparative power-based large model","authors":"Shengzhi Huang ,&nbsp;Wei Lu ,&nbsp;Zhenzhen Xu ,&nbsp;Qikai Cheng ,&nbsp;Jinqing Yang ,&nbsp;Yong Huang","doi":"10.1016/j.ipm.2025.104207","DOIUrl":"10.1016/j.ipm.2025.104207","url":null,"abstract":"<div><div>Timely identification of potentially disruptive research is a significant research issue, since disruptive innovation in science transforms the existing paradigm and/or opens a new paradigm. This study proposes a comparative power-based large model that can promptly and accurately identify potentially disruptive research via comparative analysis of semantically-related papers. To this end, a self-constructed dataset was built by treating accumulated disruptive and consolidating citations as crowdsourced annotation data. We employed a range of machine learning models (MLs), deep learning models (DLs), and large language models (LLMs) to build classifiers. Our optimal model, Mistral-7B^+*, attains an impressive F1 score of 0.8210 and outperforms the best-performing ML and DL models by approximately 27.05 % and 14.03 %, respectively. Testing on 275 recently published biomedical papers further verifies its effectiveness. Additionally, we conduct comprehensive experiments to scrutinize the comparative power of the large model as well as the impact of the number and quality of comparative papers and distinct functional paragraphs within abstracts on identification performance. Our findings show that an appropriate number and quality of comparative papers can promote identification performance. Moreover, result-based paragraphs are the most important for identifying disruptive research, while method-based paragraphs are least important.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104207"},"PeriodicalIF":7.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A soft-masking continual pre-training method based on domain knowledge relevance","authors":"Lazhi Zhao ,&nbsp;Jianzhou Feng ,&nbsp;Huaxiao Qiu ,&nbsp;Chenghan Gu ,&nbsp;Haonan Qin","doi":"10.1016/j.ipm.2025.104224","DOIUrl":"10.1016/j.ipm.2025.104224","url":null,"abstract":"<div><div>Most existing continual learning (CL) methods primarily focus on reducing catastrophic forgetting. Although some approaches have achieved CF-free learning, they often treat parameter optimization across tasks as a conflicting process. This assumption not only inhibits the learning of new tasks but also limits knowledge transfer between tasks. To address this, we propose a soft-masking continual pre-training method based on domain knowledge relevance (DKR) within the framework of continual domain-adaptive pre-training. Unlike traditional methods, DKR does not fully regard parameter optimization as adversarial. Instead, it dynamically applied soft masks to model parameters based on the degree of relevance between domain knowledge. Moreover, we introduce an efficient importance calculation method based on dual interactions between input and weights, which accurately assesses the importance of parameters. Our experimental results on six public datasets demonstrate that our method significantly outperforms existing methods in terms of mitigating forgetting. Specifically, DKR achieves negative forgetting with improvements of 1.95% in macro-F1 and 1.52% in accuracy over Naive CL(NCL).</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104224"},"PeriodicalIF":7.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EKLI-Attention: An integrated attention mechanism for classifying citizen requests in government‒citizen interactions","authors":"Junpeng Zhang ,&nbsp;Qian Geng ,&nbsp;Jian Jin","doi":"10.1016/j.ipm.2025.104237","DOIUrl":"10.1016/j.ipm.2025.104237","url":null,"abstract":"<div><div>Various regions in China mainland have implemented government‒citizen interaction boards on their government portals. Government staff assign citizen requests from these boards to departments for responses. With increasing request volume and departmental complexity, manual classification is excessively time-consuming and labor-intensive. The study of automatic classification for citizen requests has become more essential. Citizen requests contain governmental terms and limited text content, making them a typical example of short texts. In this study, an integrated attention mechanism model named EKLI-Attention (external knowledge and label information) is proposed to classify citizen requests by introducing external knowledge, such as relevant government matters and administrative region information, with labels corresponding to government departments. Particularly, a single-head cross-attention mechanism is designed to integrate text features with label information and generate an updated label feature representation, whereas a multihead self-attention mechanism is employed to integrate external knowledge to generate an updated text representation. Finally, multi-head cross-attention and two-stage convolution combine the updated label and text representations to generate the final classification. In the case study, two datasets containing over 84,000 citizen requests from Beijing and Shenzhen are investigated. The models are found to outperform the baseline models in various evaluation metrics, demonstrating their effectiveness and robustness. The application of Focal Loss improves the macro F1 score by 3.47 % and 4.04 % on the two datasets. It improves the efficiency of government agencies by ensuring that requests are routed to the correct departments efficiently. Moreover, it provides a valuable technical reference for short text classification.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104237"},"PeriodicalIF":7.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grouped top-down reasoning with hierarchical window transformer for visual grounding","authors":"Liuwu Li ,&nbsp;Zhuoming Zheng ,&nbsp;Yuqi Bu ,&nbsp;Cantao Wu ,&nbsp;Shubin Huang ,&nbsp;Qingbao Huang ,&nbsp;Yi Cai","doi":"10.1016/j.ipm.2025.104222","DOIUrl":"10.1016/j.ipm.2025.104222","url":null,"abstract":"<div><div>Visual grounding, which localizes objects in images based on natural language descriptions, requires effective processing of multi-scale visual inputs to capture both fine-grained details and global context for complex and diverse scenes. However, existing transformer-based methods face significant challenges when handling such inputs, including computational complexity that scales quadratically with spatial dimensions and difficulties in effectively aligning cross-scale information. To address these limitations, we propose Grouped Top-Down Reasoning with Hierarchical Window Transformer (GTD-HWT) with two key innovations: (1) a multi-scale input reconstruction strategy that partitions and reconstructs multi-scale inputs into hierarchically structured shorter sequences, effectively preserving both coarse and fine-grained information while reducing computational costs, and (2) a dual multi-head attention mechanism that enables semantic reasoning through parallel inter-window attention for coarse-grained understanding and subsequent intra-window attention for fine-grained refinement guided by coarse-grained priors. Extensive experiments on RefCOCO, RefCOCO+, and RefCOCOg benchmarks demonstrate that our method achieves significant improvements over state-of-the-art approaches in both referring expression comprehension and segmentation tasks.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104222"},"PeriodicalIF":7.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latitude-oriented hierarchical enhancement network for omnidirectional image super-resolution","authors":"Xin Wang ,&nbsp;Jinkai Li ,&nbsp;Jinxing Li ,&nbsp;Shiqi Wang ,&nbsp;Yong Xu","doi":"10.1016/j.ipm.2025.104217","DOIUrl":"10.1016/j.ipm.2025.104217","url":null,"abstract":"<div><div>Omnidirectional image super-resolution (ODISR) holds significant application potential in various industrial scenarios, such as virtual reality and autonomous driving. However, most existing super-resolution methods focus on standard 2D images and yield unsatisfactory ODISR performance, because omnidirectional images (ODIs) typically adopt the equirectangular projection (ERP) format, suffering from serious geometric distortion and differentiated texture features related to the latitude. In this paper, we propose a novel latitude-oriented hierarchical enhancement network (LOHE-Net) for ODISR, which allows features at different latitudes to obtain hierarchical enhancement. Specifically, we first exploit a hierarchical enhancement unit to divide an ERP feature map into different sub-regions according to the latitude and then perform distinct enhancement for these sub-regions, which can effectively address the differentiation of texture features, adapt the geometric distortion, and derive high-frequency information across latitudes in ERP ODIs. Subsequently, we introduce a distillation and spatial enhancement unit to progressively extract important information and further refine it in the spatial domain, boosting the representation ability with low computational cost. Extensive quantitative and qualitative experiments validate the superior ODISR performance and computational efficiency of our LOHE-Net.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104217"},"PeriodicalIF":7.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Half a Century of Information Processing & Management: A bibliometric retrospective","authors":"Mohammad Sadegh Khorshidi,&nbsp;José M. Merigó,&nbsp;Ghassan Beydoun","doi":"10.1016/j.ipm.2025.104238","DOIUrl":"10.1016/j.ipm.2025.104238","url":null,"abstract":"<div><div>Established in 1963 under the title Information Storage and Retrieval, the journal adopted its current name, Information Processing &amp; Management (IPM), in 1975, reflecting a broadening scope aligned with computational and cognitive developments in information science. This study uses data from Web of Science and Scopus databases to deliver a longitudinal, multi-perspective bibliometric and science mapping analysis of IPM’s evolution from 1963 to 2023. Employing co-citation analysis, bibliographic coupling, keyword co-occurrence, and thematic mapping via VOSviewer and Bibliometrix, the analysis delineates the structural, conceptual, and topical transformation of the journal content. Co-citation networks uncover foundational cores in information retrieval, relevance theory, and evaluation methodologies, while also revealing temporal shifts toward natural language processing, deep learning, and social media analytics. Bibliographic coupling identifies coherent intellectual clusters centered on GNN-based recommendation systems, blockchain-secured infrastructures, and sentiment-aware retrieval frameworks. Keyword co-occurrence and topic evolution trajectories illustrate the journal’s recent pivot toward transformer models, misinformation detection, ethical AI, and interdisciplinary convergence across cognitive science, machine learning, and computational linguistics. Regional co-word analysis underscores epistemological diversity and geographic differentiation across North America, Europe, and East Asia. Productivity and influence metrics highlight the ascent of East Asian institutions and the emergence of globally distributed citation impact. Finally, SciVal-based topic and topic cluster analyses reveal the journal’s role in advancing highly cited research (as measured by FWCI) in areas such as ABSA, multi-view clustering, and health informatics. This work not only charts IPM’s conceptual landscape and disciplinary diffusion but also provides actionable intelligence on the journal’s strategic positioning within the broader information and computational sciences.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104238"},"PeriodicalIF":7.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Management of evaluation processes and creation of authentication metrics: Artificial intelligence-based fusion framework","authors":"Dragan Korać ,&nbsp;Boris Damjanović ,&nbsp;Dejan Simić ,&nbsp;Cong Pu","doi":"10.1016/j.ipm.2025.104233","DOIUrl":"10.1016/j.ipm.2025.104233","url":null,"abstract":"<div><div>While the literature extensively covers various authentication systems, management of evaluation processes and creation of authentication metrics remain significant information challenges for researchers. To overcome this complex challenge, we present a taxonomy of research processes based on fusion and fuzzy strategies and give an overview and comparison of related studies. Specifically, we develop an artificial intelligence-based fusion framework (<em>f_f</em>) incorporating Mamdani-type fuzzy rules and key user factors: security, privacy, and trust. Its uniqueness and innovation lie in the application of trapezoidal functions to describe these factors as key input metric values. Moreover, we are the first to incorporate trust as an independent comparative factor and provide a comparison of traditional and modern authentication methods, including artificial intelligence (AI), electroencephalogram (EEG), electrocardiographic (ECG), and photoplethysmogram (PPG) methods. Also, we use a workflow diagram to define the topological relationships among user factors and authentication factors, clarifying the role of fusion in multi-factor authentication (MFA) approaches. In comparison to other similar frameworks implemented solely for traditional methods, the proposed <em>f_f</em> yields better and more realistic quantification metric results. In addition, we present and discuss the key mathematical differences between one-factor authentication (1FA) and MFA, aiming to shed light on issues such as complexity and bias. Lastly, the developed <em>f_f</em> not only advances MFA metrics by introducing modern authentication methods such as AI, EEG, ECG, and PPG but also paves the way for future research on how and why AI algorithms need to be incorporated into information processing and the creation of strong MFA solutions.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104233"},"PeriodicalIF":7.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cooking with context: Leveraging context for procedural question answering","authors":"Alexander Frummet,&nbsp;David Elsweiler,&nbsp;Udo Kruschwitz","doi":"10.1016/j.ipm.2025.104212","DOIUrl":"10.1016/j.ipm.2025.104212","url":null,"abstract":"<div><div>Conversational agents struggle to answer questions during complex tasks such as do-it-yourself (DIY) projects and cooking due to difficulties in understanding task context and user information needs. This study examines the efficacy of integrating conversational and task context in query and document representations to enhance question answering (QA) performance in cooking tasks. We evaluated three document representations with increasing granularity on two task-based QA datasets with a total sample size of 6217 question–answer pairs: full recipe documents (document-based), segmented recipes by cooking steps (step-based), and detailed task structures (task-based). The results show step- and task-based representations outperform traditional document-based approaches by 10% on average (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>). Task-based representations provide superior performance for fact-based needs (e.g., ingredients, time, equipment) in most cases, while step-based representations better address competence needs (e.g., preparation, cooking techniques). Simple conversational history prepending of two to three turns yielded the best performance, improving results by up to 24% over no context. These results emphasise the importance of selecting a representation that matches the structure of the surrounding task in order to enhance QA performance.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104212"},"PeriodicalIF":7.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal Neighbor Sequence-based Interpretable Spammer Groups Detection on E-commerce platform","authors":"Ning Li ,&nbsp;Shujuan Ji ,&nbsp;Yingtong Dou ,&nbsp;Dickson K.W. Chiu ,&nbsp;Qi Zhang ,&nbsp;Yongquan Liang ,&nbsp;Yongshan Wei","doi":"10.1016/j.ipm.2025.104177","DOIUrl":"10.1016/j.ipm.2025.104177","url":null,"abstract":"<div><div>Organized spammer groups collaborate to manipulate reviews for illicit gains, posing significant challenges to online platforms. This paper introduces a Temporal Neighbor Sequence-based Interpretable Spammer Group Detection method called TNSGD. First, we filter high-suspicious reviewers to reduce node complexity in the co-review temporal network, optimizing the detection process. Second, a co-review temporal network is constructed using these filtered reviewers, generating temporal neighbor sequences that capture temporal aggregation and relational features to form candidate groups. These candidate groups are then classified using group spam indicators and heuristic conditions to delineate the final spammer groups. TNSGD surpasses baseline methods with notable improvements in Precision and F1 scores, including enhancements of 4% and 3% for Amazon and 39% and 31% for Yelp, respectively. Additionally, TNSGD significantly reduces computational complexity to 1/85th and 1/7th. Furthermore, we provide interpretations of TNSGD from two perspectives: model and result. We devise a transparent detection process for model interpretation to ensure each step has a clear physical significance. For result interpretation, we offer interpretable visualizations of the temporal–spatial and evolutionary characteristics of the detected spammer groups, providing valuable insights for refining future detection models.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104177"},"PeriodicalIF":7.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}