Information Processing & Management最新文献

Modeling heterogeneous normality in time series anomaly detection 时间序列异常检测中的异构正态性建模

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-01-28 DOI: 10.1016/j.ipm.2026.104644

Xiaohui Zhou , Yijie Wang , Hongzuo Xu , Yizhou Li

{"title":"Modeling heterogeneous normality in time series anomaly detection","authors":"Xiaohui Zhou , Yijie Wang , Hongzuo Xu , Yizhou Li","doi":"10.1016/j.ipm.2026.104644","DOIUrl":"10.1016/j.ipm.2026.104644","url":null,"abstract":"<div><div>Time series anomaly detection is crucial in many fields, where the objective is to identify unusual patterns by learning normality from sequential observations. However, existing methods typically treat the entire training data as a single, homogeneous normal class, which disregards the normal diversity caused by distribution shifts over time. As a result, these methods are forced to learn a single, complex decision boundary that must enclose all variations of normal behavior, making it difficult to precisely distinguish subtle anomalies hidden within the normal patterns. Therefore, this paper tackles this challenge by explicitly modeling heterogeneous normality, which allows for learning simpler, localized decision boundaries to separate anomalies. Specifically, we propose a novel approach that decomposes the heterogeneous class space into multiple normal classes, adopting a two-stage <em>coarse-to-fine</em> training paradigm: (1) a Mixture of Experts (MoE) framework assigns pseudo-labels by routing input features to specialized experts for prediction, approximating the latent sub-class structure; (2) enhanced features are generated based on pseudo-labels and feature space is refined via spectral decomposition, which contracts class boundaries and better exposes anomalies. Extensive experiments on 23 univariate datasets and 17 multivariate datasets show that our approach significantly outperforms state-of-the-art competitors by 2.55%-21.76% in VUS-PR, validating the importance of modeling heterogeneous normality in time series anomaly detection.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 5","pages":"Article 104644"},"PeriodicalIF":6.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081583","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}

引用次数: 0

A blockchain-based digital evidence management system: Integrating forensic procedures and multi-party authorization 基于区块链的数字证据管理系统：整合取证程序和多方授权

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-01-27 DOI: 10.1016/j.ipm.2026.104654

Yunji Park, Doowon Jeong

{"title":"A blockchain-based digital evidence management system: Integrating forensic procedures and multi-party authorization","authors":"Yunji Park, Doowon Jeong","doi":"10.1016/j.ipm.2026.104654","DOIUrl":"10.1016/j.ipm.2026.104654","url":null,"abstract":"<div><div>Current blockchain-based digital evidence systems provide strong technical integrity but fail to adequately address the procedural legitimacy required for court admissibility, frequently omitting judicial authorization workflows, differentiated handling of voluntary versus compulsory evidence, and transparent destruction protocols. To address these gaps, we propose B-DEMS, a blockchain-based digital evidence management system that integrates the full evidence lifecycle–from registration to court-authorized destruction–while encoding jurisdiction-specific legal requirements across South Korea, the United States, the European Union, and China. B-DEMS implements multi-party authorization, conditional decryption, and transaction-based disposal to ensure auditability and procedural compliance. Experimental evaluation across 1950 workflow executions demonstrated that B-DEMS achieved a maximum throughput of 10,890 TPS, representing 51–219% improvement over state-of-the-art systems, while maintaining stable scalability with latency increasing only 2.7-fold under a 5-fold peer expansion. Security analysis confirmed a 0% attack success rate across 300 adversarial attempts, and cross-border cooperation scenarios exhibited consistent adherence to jurisdiction-specific approval workflows. By aligning evidentiary procedures with a scalable blockchain architecture, B-DEMS provides a technically robust and procedurally compliant foundation for practical deployment in multi-agency and international investigative environments.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 5","pages":"Article 104654"},"PeriodicalIF":6.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081581","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}

引用次数: 0

SRCR: Faithful structured reasoning with curriculum reinforcement learning for explainable question answering SRCR：忠实的结构化推理与课程强化学习，用于可解释的问题回答

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-01-27 DOI: 10.1016/j.ipm.2026.104653

Yue Fan , Hu Zhang , Ru Li , Guangjun Zhang , Yujie Wang , Hongye Tan , Yuanlong Wang , Xiaoli Li , Jiye Liang

{"title":"SRCR: Faithful structured reasoning with curriculum reinforcement learning for explainable question answering","authors":"Yue Fan , Hu Zhang , Ru Li , Guangjun Zhang , Yujie Wang , Hongye Tan , Yuanlong Wang , Xiaoli Li , Jiye Liang","doi":"10.1016/j.ipm.2026.104653","DOIUrl":"10.1016/j.ipm.2026.104653","url":null,"abstract":"<div><div>Existing explainable question answering methods based on structured reasoning lack effective modeling of logical dependencies between steps and underutilize the potential of intermediate conclusions in structured reasoning. To address these challenges, we propose SRCR, a faithful <strong>S</strong>tructured <strong>R</strong>easoning method based on <strong>C</strong>urriculum <strong>R</strong>einforcement learning. Specifically, we propose an easy-to-difficult reverse structured curriculum that gradually slides the initial state of reasoning from end to beginning, which fully captures the complex dependencies of multi-step reasoning. Moreover, we treat fact selection and deductive generation as a unified process and construct a faithfulness reward function to mine faithful reasoning steps during the model learning and exploring phases. Experimental results on the structured reasoning datasets EntailmentBank and STREET demonstrate that SRCR achieves state-of-the-art performance in factual accuracy and intermediate conclusion correctness, surpassing previous methods by 8.0% and 2.0%, respectively. Moreover, SRCR also improves answer accuracy by 2.6% to 8.3%, and extensive analysis shows that SRCR can generate more faithful structured explanations.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 5","pages":"Article 104653"},"PeriodicalIF":6.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081585","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}

引用次数: 0

Beyond traditional search: New characteristics of online health information seeking about chronic disease with Gen AI 超越传统搜索：使用人工智能搜索慢性病在线健康信息的新特点

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-02-03 DOI: 10.1016/j.ipm.2026.104668

Zhenyi Tang , Yang Su , Preben Hansen , Pengyi Zhang

{"title":"Beyond traditional search: New characteristics of online health information seeking about chronic disease with Gen AI","authors":"Zhenyi Tang , Yang Su , Preben Hansen , Pengyi Zhang","doi":"10.1016/j.ipm.2026.104668","DOIUrl":"10.1016/j.ipm.2026.104668","url":null,"abstract":"<div><div>Chronic health information seeking is vital for patient well-being and self-management, yet how AI aids this process remains unclear. To explore how users interact with Gen AI tools when seeking and evaluating chronic health information, this study conducted an experiment involving 60 participants, collecting 757 user-generated dialogues related to three chronic conditions. We developed a complementary coding framework integrating analysis of user behavior and AI’s response to understand user-AI interactions and their impact on chronic disease information seeking, evaluation and adoption. The results show that while user interactions with Gen AI share similarities with traditional searches, they also exhibit distinct characteristics: 1) User-input prompts are more detailed, with longer sentences and more terms to specify information needs. Users also adopt more diverse reformulation strategies, often informed or inspired by AI feedback. 2) Some users communicate with AI as if interacting with a human, and the AI often responds with emotionally supportive, human-like replies. 3) Users spend less time but engage in more conversation turns, as the AI provides clear, well-structured responses that maintain dialogue flow and adapt to user intent, thereby enhancing retrieval efficiency and encouraging continued interaction. 4) While most users evaluate AI-generated content heuristically and rarely seek external verification, encountering factual inaccuracies or low-credibility responses can reduce their willingness to adopt the AI's output. These findings offer a more holistic understanding of human-AI interaction in online health information seeking and provide valuable guidance for optimizing system design, enhancing algorithm literacy, and improving health management practices.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 5","pages":"Article 104668"},"PeriodicalIF":6.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190717","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}

引用次数: 0

From exposure to followers: A stock-and-flow closed-loop framework of creator dynamics 从曝光到追随者：创造者动态的库存-流量闭环框架

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-02-09 DOI: 10.1016/j.ipm.2026.104677

Yushi Sun, Bo Sun

{"title":"From exposure to followers: A stock-and-flow closed-loop framework of creator dynamics","authors":"Yushi Sun, Bo Sun","doi":"10.1016/j.ipm.2026.104677","DOIUrl":"10.1016/j.ipm.2026.104677","url":null,"abstract":"<div><div>In the creator economy, platforms allocate exposure on feeds, and new followers arise largely from that exposure. What is missing is an estimable account-level closed-loop model that links platform-allocated post exposure to long-run follower accumulation. We develop an interpretable stock-and-flow framework with feedback: out-of-feed exposure increases with reach but saturates as the addressable audience is exhausted, unfolds over days after posting, converts into follower inflow, and is offset by proportional churn. The framework yields objects such as a viability threshold, a saturation level, and dynamic responses that separate timing from long-run levels, enabling diagnosis and planning. Using a panel of 1015 creators (794,051 account-days; 829,783 posts), we estimate the primitives via a low-dimensional pipeline and validate the implied dynamics in-sample and out-of-sample. On a strict 180-day holdout, the model attains a median MAPE of 0.020, comparable to strong forecasting baselines and econometric benchmarks. Subsample tests confirm portability across scales and platforms. By mapping traces to decision-relevant diagnostics of saturation, conversion, cadence, and churn, the framework supports design diagnostics and monitoring, and enables causal evaluations when exogenous variation is available.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 5","pages":"Article 104677"},"PeriodicalIF":6.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190569","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}

引用次数: 0

SPECTRA-Net: Spatiotemporal edge-preserving contextual reinforcement architecture for adaptive crowd behavior recognition 光谱网络：用于自适应人群行为识别的时空边缘保持上下文强化结构

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-01-27 DOI: 10.1016/j.ipm.2026.104647

Min Zhu , Dengyin Zhang

引用次数: 0

Exploring factors influencing open government data value realization in China: A mixed design using grounded theory, system dynamics, and questionnaire survey 中国政府开放数据价值实现的影响因素探讨：基于实证理论、系统动力学和问卷调查的混合设计

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-02-06 DOI: 10.1016/j.ipm.2026.104670

Yongqiang Sun , Zequan Luan , Jie Ma

{"title":"Exploring factors influencing open government data value realization in China: A mixed design using grounded theory, system dynamics, and questionnaire survey","authors":"Yongqiang Sun , Zequan Luan , Jie Ma","doi":"10.1016/j.ipm.2026.104670","DOIUrl":"10.1016/j.ipm.2026.104670","url":null,"abstract":"<div><div>As digital economy strategies advance and the data market gradually matures, the pathways for realizing the value of China’s government open data have undergone significant changes, with numerous emerging influencing factors. Previous research has primarily relied on portal operational data and static efficiency assessment methods, making it challenging to identify the evolutionary mechanisms and dynamic relationships among these factors. To bridge this gap and inform policy-making and theoretical advancements, we employ a hybrid design that combines grounded theory and system dynamics. Utilizing 33 semi-structured interviews and 317 questionnaire responses (292 valid), we construct causal loops and a stock-flow structure. A 60-month simulation analysis examines factors influencing open government data (OGD) value realization, focusing on: (a) influencing factors and feedback positions; (b) the ranking of factor strengths and directionality. Our findings reveal that increased data demand rates sustainably elevate the long-run equilibrium level of government open data value realization, while higher data depreciation rates reduce this equilibrium. This research advances the dynamic theory of OGD value realization, broadens insights into key drivers and inhibitors, and provides methodological support for implementing strategies such as prioritizing high-demand data releases, optimizing APIs and data rights confirmation processes, and enhancing storage security by mitigating data depreciation. Our findings indicate that enhancing OGD value cannot be achieved solely by increased accessibility or platform capabilities. Instead, it requires examining multifaceted feedback loops and synergistic interactions to uncover specific value generation mechanisms and identify bottlenecks.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 5","pages":"Article 104670"},"PeriodicalIF":6.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190661","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}

引用次数: 0

Haze-prior guided frequency-embedded attention learning for single-stage hazy-weather crowd counting 单阶段雾霾天气人群计数的雾先验引导嵌入频率注意力学习

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-02-06 DOI: 10.1016/j.ipm.2026.104671

Weihang Kong , Jienan Shen , Shaohua Li , Liangang Tong , He Li

{"title":"Haze-prior guided frequency-embedded attention learning for single-stage hazy-weather crowd counting","authors":"Weihang Kong , Jienan Shen , Shaohua Li , Liangang Tong , He Li","doi":"10.1016/j.ipm.2026.104671","DOIUrl":"10.1016/j.ipm.2026.104671","url":null,"abstract":"<div><div>Conventional two-stage hazy crowd counting suffers from error propagation between separate dehazing and counting pipelines, leading to degraded performance. To address this, we propose an end-to-end single-stage framework that jointly optimizes haze-invariant feature learning and crowd density estimation, achieving state-of-the-art accuracy through two key innovations. Frequency-embedded Hybrid Attention Aggregation (FHAA): This module uses frequency-domain attention to explore frequency features in hazy images, thereby enhancing key feature capture and improving feature learning. Experiments show it reduces Mean Absolute Error (MAE) by 34.48% compared to the model without it, proving its effectiveness in boosting performance. Haze-prior Guided Learning Mechanism: It explicitly models haze distortion, understands haze’s impact on images, and adaptively mitigates interference without manual dehazing annotations, reducing annotation cost and difficulty. Comparative experiments reveal a further 13.64% MAE reduction compared to the model without this mechanism, validating its anti-interference capability. The FHAA module focuses on key frequency features for crowd counting, suppressing haze noise and improving robustness in hazy weather. The haze-prior mechanism uses predicted haze distribution maps to adjust feature learning based on haze intensity, adapting to complex hazy scenes. To support research, we release two synthetic hazy crowd counting datasets at <span><span>https://github.com/312524/Hazy-CC-extended</span><svg><path></path></svg></span>. These datasets, with the same scale as Hazy-ShanghaiTechRGBD but higher haze densities, address the lack of haze-intensity diversity in existing benchmarks. Extensive ablation studies and performance comparisons on four datasets demonstrate the feasibility and superiority of our method for hazy-weather crowd counting.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 5","pages":"Article 104671"},"PeriodicalIF":6.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190665","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}

引用次数: 0

Defending LLMs against jailbreak attacks through representation offset detection 通过表示偏移检测保护llm免受越狱攻击

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-02-02 DOI: 10.1016/j.ipm.2026.104662

Shuo Liu , Xiang Cheng , Zhenzhong Zheng , Sen Su

引用次数: 0

The autonomy equation: How agentic AI reshapes trust and workload in routine productivity applications 自主方程式：人工智能如何重塑日常生产力应用中的信任和工作量

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-07-01 Epub Date: 2026-02-11 DOI: 10.1016/j.ipm.2026.104681

Angelo Geninatti Cossatin, Fabio Ferrero, Liliana Ardissono, Noemi Mauro

{"title":"The autonomy equation: How agentic AI reshapes trust and workload in routine productivity applications","authors":"Angelo Geninatti Cossatin, Fabio Ferrero, Liliana Ardissono, Noemi Mauro","doi":"10.1016/j.ipm.2026.104681","DOIUrl":"10.1016/j.ipm.2026.104681","url":null,"abstract":"<div><div>User experience and trust in AI-assisted technologies are key factors in controlling their adoption. We investigate these aspects in an Agentic AI platform that integrates routine productivity services and exhibits different levels of autonomy: a manual baseline that lacks AI-driven automation, an Agentic AI with medium autonomy that requires user confirmation before acting, and an Agentic AI with high autonomy that acts proactively for low-stakes tasks. The study, involving 230 participants with heterogeneous professional backgrounds, examines how autonomy of the system affects user activity, user workload, perceived support, and trust.</div><div>We found that both Agentic AI systems outperformed the baseline in user productivity. In task execution, they achieved a precision of over 82%, higher than the baseline’s 65%. The recall of the Agentic AI system with high autonomy was 63%. This denotes much higher throughput than the system without AI-driven automation (14%). The Agentic AI systems outperformed the baseline in workload reduction (NASA-TLX Aggregate score) with a statistically significant difference. Both AI-driven systems received equivalent or slightly higher trust than the baseline. However, the system with medium autonomy was the best at balancing productivity gains and user preferences for control. Specifically, the correlations between individual user characteristics (Desirability of Control and Propensity to Trust) and the resulting trust in the systems suggest that the influence of personal traits on system evaluation is least pronounced when automation is combined with explicit user intervention. These results encourage the adoption of user-controllable Agentic AI architectures in multitasking support.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 5","pages":"Article 104681"},"PeriodicalIF":6.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190671","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}

引用次数: 0