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Mathematics
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IIHMSP: Intelligent Information Hiding and Multimedia Signal Processing
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IIHMSP: Intelligent Information Hiding and Multimedia Signal Processing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 2834

Special Issue Editors

Prof. Dr. Ruo-Wei Hung

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Wufeng, Taichung 413310, Taiwan
Interests: graph algorithms; linux system; applications for smart phones; security on IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Ling-Ju Hung

E-Mail Website
Guest Editor
Institute of Information and Decision Sciences, National Taipei University of Business, Taipei 10051, Taiwan
Interests: design and analysis of algorithms; robot interactive programming; digital exhibition design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We cordially invite you to submit your articles to the Special Issue of Mathematics entitled “IIHMSP: Intelligent Information Hiding and Multimedia Signal Processing”. This Special Issue will include papers from the IIHMSP 2025 conference. For information about the conference, please refer to https://iihmsp25.github.io. Multimedia technologies facilitate the creation of global information infrastructure for acquiring, storing, and communicating data in different forms. The proliferation of multimedia applications has raised challenges such as multimedia security and privacy, big data in multimedia, and intelligence in multimedia processing. Topics of interest for this Issue include, but are not limited to, the following: information hiding and security; multimedia signal processing and networking; bioinspired multimedia technologies and systems; and information and computer technology. We welcome authors of all related interests to contribute to this Special Issue.

Prof. Dr. Ruo-Wei Hung
Dr. Ling-Ju Hung
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information hiding and security
  • multimedia signal processing and networking
  • bioinspired multimedia technologies and systems
  • information and computer technology

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Published Papers (4 papers)

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Research

17 pages, 811 KB  
Article
A Hybrid Feature-Weighting and Resampling Model for Imbalanced Sentiment Analysis in User Game Reviews
by Thao-Trang Huynh-Cam, Long-Sheng Chen, Hsuan-Jung Huang and Hsiu-Chia Ko
Mathematics 2026, 14(8), 1273; https://doi.org/10.3390/math14081273 - 11 Apr 2026
Viewed by 406
Abstract
Sentiment analysis of online game reviews has increasingly become important in understanding player experiences and supporting data-driven game development. However, research in this domain has continuously faced two unresolved challenges: (1) the extreme imbalance between positive and negative feedback, and (2) the inefficiency [...] Read more.
Sentiment analysis of online game reviews has increasingly become important in understanding player experiences and supporting data-driven game development. However, research in this domain has continuously faced two unresolved challenges: (1) the extreme imbalance between positive and negative feedback, and (2) the inefficiency of existing feature-weighting schemes in capturing sentiment signals embedded in informal gaming discourses. Prior works demonstrated that negative feedback—though a few in number are highly influential—usually contain richer emotional content and longer textual structures; yet, prevailing classification models often perform poorly for these minorities (i.e., negative feedback). Numerous studies explored multimodal imbalance issues, class imbalance in cross-lingual ABSA (Aspect-Based Sentiment Analysis), reinforcement-learning-based architectures for imbalanced extraction tasks, and oversampling strategies like SMOTE (Synthetic Minority Over-sampling Technique) variants. Few investigations specifically addressed imbalanced sentiment classification in the contexts of online game reviews, where user-generated content exhibits unique lexical, structural, and emotional characteristics. To address these gaps, this study integrated TF-IDF (Term Frequency-Inverse Document Frequency), VADER (Valence Aware Dictionary and Sentiment Reasoner) lexicon features, and IGM (Inverse Gravity Moment) weightings with advanced oversampling methods such as ADASYN (Adaptive Synthetic Sampling Approach for Imbalanced Learning) and Borderline-SMOTE to improve the detection of minority sentiment classes. Ensemble models, including XGBoost (Extreme Gradient Boosting) and LightGBM (Light Gradient-Boosting Machine), were further employed to enhance the robustness of imbalance. Using a large-scale dataset of Steam game reviews, the proposed framework demonstrated substantial improvement in identifying negative sentiments, addressing a critical limitation in the existing computational game-analysis literature, and advancing the modeling for detecting the emotion-rich but imbalance-prone user feedback. Full article
(This article belongs to the Special Issue IIHMSP: Intelligent Information Hiding and Multimedia Signal Processing)
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41 pages, 699 KB  
Article
Mathematical Framework for Characterizing Emotional Individuality in Large Language Models: Temperature Control, Fuzzy Entropy, and Persona-Based Diversity Analysis
by Naruki Shirahama, Yuma Yoshimoto, Naofumi Nakaya and Satoshi Watanabe
Mathematics 2026, 14(7), 1224; https://doi.org/10.3390/math14071224 - 6 Apr 2026
Viewed by 504
Abstract
Evaluating emotional understanding in Large Language Models (LLMs) is challenging because assessments are subjective, ambiguous, multidimensional, and sensitive to controllable generation parameters. We developed a unified mathematical framework for characterizing LLM “emotional individuality” that integrates softmax sampling–temperature control (the decoding-time temperature parameter exposed [...] Read more.
Evaluating emotional understanding in Large Language Models (LLMs) is challenging because assessments are subjective, ambiguous, multidimensional, and sensitive to controllable generation parameters. We developed a unified mathematical framework for characterizing LLM “emotional individuality” that integrates softmax sampling–temperature control (the decoding-time temperature parameter exposed by the API and typically used to modulate output randomness during token generation), fuzzy set theory with Shannon-type fuzzy entropy, and persona-based cognitive diversity analysis. We evaluated 36 API-accessible LLMs from seven major vendors on Japanese literary texts, using four personas each assigned a sampling temperature (T∈{0.1,0.4,0.7,0.9}), yielding 4227/4320 trial responses (97.8% coverage), of which 4067/4227 contained valid numeric emotion scores (96.2%). Temperature controllability varied approximately 25-fold (κM∈[0.039,0.982]) with both positive and negative temperature–variance relationships across models. Because each sampling temperature is deterministically assigned to a persona in our design, κM should be interpreted as an operational temperature–variance association across persona conditions rather than an isolated causal temperature effect. The model-level mean fuzzy entropy ranged from approximately 0.40 to 0.66, and the numerical stability consistency scores ranged from approximately 0.548 to 0.780. We also observed text-dependent structure, including genre-specific variation in the Interest–Sadness relationship. For practitioners, the framework is most directly useful as a benchmark-design and model-screening template for structured emotion-scoring tasks; its empirical conclusions remain limited to the present Japanese literary, text-only setting. Full article
(This article belongs to the Special Issue IIHMSP: Intelligent Information Hiding and Multimedia Signal Processing)
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28 pages, 993 KB  
Article
Fair Domination on Extended Supergrid Graphs: Complexity, Linear-Time Algorithms, and ILP Formulations
by Ruo-Wei Hung
Mathematics 2026, 14(6), 947; https://doi.org/10.3390/math14060947 - 11 Mar 2026
Viewed by 427
Abstract
A dominating set of a graph is a subset of vertices such that every vertex is either contained in the set or adjacent to at least one vertex in it. A dominating set is called k-fair if each vertex not in this [...] Read more.
A dominating set of a graph is a subset of vertices such that every vertex is either contained in the set or adjacent to at least one vertex in it. A dominating set is called k-fair if each vertex not in this set is adjacent to exactly k vertices of the set. The domination and k-fair domination problems aim to find such sets of minimum cardinality. Both problems are NP-complete for general graphs, and the domination problem remains NP-complete on grid graphs, whereas the k-fair domination problem remains open on grid graphs. In this paper, we study the 1-fair and 2-fair domination problems on extended supergrid graphs, which generalize grid graphs and include both grid and supergrid graphs as subclasses. We prove that the 1-fair domination problem is NP-complete for these graph classes, even when restricted to planar graphs with maximum degree 4. On the positive side, for rectangular supergrid graphs, we present a linear-time algorithm for computing minimum 1-fair dominating sets. In addition, we formulate an integer linear programming (ILP) model to investigate the 1-fair and 2-fair dominations on small instances and introduce a restricted k-fair domination problem motivated by the experimental observations. Full article
(This article belongs to the Special Issue IIHMSP: Intelligent Information Hiding and Multimedia Signal Processing)
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16 pages, 1121 KB  
Article
A Residual Control Chart Based on Convolutional Neural Network for Normal Interval-Censored Data
by Pei-Hsi Lee
Mathematics 2026, 14(3), 423; https://doi.org/10.3390/math14030423 - 26 Jan 2026
Viewed by 503
Abstract
To reduce reliability testing time, experiments are often terminated at a predetermined time, producing right-censored lifetime data. Alternatively, when test samples are inspected at fixed intervals, failures are only observed within these intervals, resulting in interval-censored lifetime data. Although quality control methods for [...] Read more.
To reduce reliability testing time, experiments are often terminated at a predetermined time, producing right-censored lifetime data. Alternatively, when test samples are inspected at fixed intervals, failures are only observed within these intervals, resulting in interval-censored lifetime data. Although quality control methods for right-censored data are well established, relatively little attention has been given to interval-censored observations. Motivated by the success of residual control charts based on convolutional neural network (CNN) for right-censored data, this study extends the chart for monitoring normally distributed interval-censored lifetime data. Simulation results based on average run length (ARL) indicate that the proposed method outperforms the traditional exponentially weighted moving average (EWMA) chart in detecting decreases in mean lifetime. The findings also highlight the practical benefits of employing high- or low-order autoregressive CNN models depending on the magnitude of process shifts. Full article
(This article belongs to the Special Issue IIHMSP: Intelligent Information Hiding and Multimedia Signal Processing)
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