Search Results (652)

Bipolar fuzzy sets provide an effective framework for representing both positive and negative aspects of information. The necessity of a mathematically rigorous and valid distance measure in bipolar fuzzy environments motivates us to introduce a new real-valued function on the set of bipolar fuzzy sets defined over both discrete and continuous universes of discourse. The proposed function is shown to define a valid metric on the set of bipolar fuzzy sets, as it satisfies all the metric axioms. The metric induced by the real-valued function is inspired by the Canberra distance, and it can effectively quantify the dissimilarity between bipolar fuzzy sets in a normalized and interpretable manner. The practical utility of the proposed metric is demonstrated in a pattern recognition problem, where it successfully recognizes an unknown pattern using known bipolar fuzzy patterns. Using the proposed metric, a bipolar fuzzy C-means clustering algorithm is developed for sentiment analysis. The time complexity of the aforementioned algorithm is also analysed. Experiments conducted on the IMDb Movie Review Dataset demonstrate that the proposed algorithm outperforms k-means, fuzzy C-means, and intuitionistic fuzzy C-means algorithms. The proposed bipolar fuzzy C-means algorithm achieves an accuracy of 90.04%, a precision of 90.51%, a recall of 89.01%, an F1-score of 89.75%, a Root mean square error of 0.1191, and a Silhouette score of 0.75. The findings establish that the proposed metric and the associated bipolar fuzzy clustering approach provide a robust and effective framework of handling sentiment data associated with simultaneous positive and negative opinions. Full article

In present recommendation system research, most approaches rely on analyzing the consumption behavior of large numbers of users to generate recommendations. However, this strategy requires extensive computational resources and often leads to considerable delays in producing recommendation results, which negatively affect the user experience. To overcome these limitations, we developed an innovative segmented data-based recommendation method for user region optimization, offering an effective alternative to traditional big-data recommendation strategies. The developed method divides the data into multiple smaller segments according to user regions and then performs specialized analysis within each segment. This segmentation substantially reduces computational time while simultaneously improving the relevance and accuracy of recommendations. By lowering computational complexity, the system is able to respond more rapidly to user requests, making more efficient use of computational resources without compromising recommendation quality. Through this segmented computation approach, the system shows faster response speeds and maintains high recommendation performance. Ultimately, the method provides new insights into optimizing recommendation systems and highlights promising directions for future improvements. Full article

Item-to-group recommendation identifies the most compatible user groups for a specific item provider to enable precision marketing, such as recommending fruit products to the most receptive consumer communities. Existing graph-based recommendations typically treat social relationships as static binary links, failing to capture variations in interaction intensity driven by user preferences. Moreover, these models largely overlook the structural relevance of intra-group connections, leading to unreliable group representations. To address these challenges, we propose the Preference-Weighted Neighbor-Aware Group Recommendation Network (PNGRN). Specifically, social edges are first reweighted using preference signals derived from historical user–item rating interactions, thereby suppressing socially connected but preference-inconsistent neighbors during aggregation. Structurally cohesive candidate groups are then identified via k-core decomposition, retaining only subgraphs where every member has at least k internal connections. A neighbor-aware graph convolutional network (GCN) module is further introduced to incorporate external social neighborhood features into group representations. This ensures that the learned group profiles reflect both internal structural stability and the external social context. Experiments on three real-world datasets demonstrate that PNGRN consistently outperforms competitive baselines across all evaluation metrics. Notably, on the MovieLens-1M dataset, PNGRN achieves up to a 9.85% improvement in Precision@20 and a 8.98% gain in NDCG@20. These results validate the necessity of coupling topological density with external social influence, and this work offer a scalable framework for precision group-targeted marketing. Full article

The expanding capabilities of deep learning-based media synthesis have intensified concerns regarding the authenticity of digital content and the reliability of forensic analysis tools. In response to these challenges, this work introduces DeepFakeX, a collection of 800 synthetically generated videos available under controlled access for research purposes. The dataset encompasses four distinct categories of AI-driven synthesis: facial identity replacement, audio track substitution, neural voice cloning, and combined audiovisual alteration. Unlike existing deepfake datasets that predominantly focus on facial synthesis, DeepFakeX covers a broader range of manipulation modalities, reflecting the diversity of synthetic media encountered in real-world settings. All deepfakes were generated using state-of-the-art, publicly available tools. Standardized post-processing procedures were applied to each video to ensure uniformity in terms of quality, duration and encoding format. DeepFakeX also emphasizes diversity in gender, age, ethnicity, and language. Video contexts span speeches, informational videos, movie clips, news broadcasts, and interviews that reflect content scenarios commonly encountered in real-world online environments. The dataset includes videos in both English and Urdu. The dataset’s quality and structural variability were assessed through visual and audio analyses using the Structural Similarity Index Measure (SSIM), Mel-Frequency Cepstral Coefficients (MFCCs), and Principal Component Analysis (PCA). The evaluation results revealed substantial variability within each manipulation category, along with clearly distinguishable patterns specific to each modality. DeepFakeX has been developed to facilitate rigorous and transparent research in deepfake detection, cross-modal forensic analysis, and AI-driven media forensics. It is hosted on Zenodo under controlled access for research use. Full article

Conventional recommender systems often rely on shallow collaborative signals, which limits their performance under sparse and popularity-skewed conditions. To address this, we propose a knowledge-aware framework that combines an item hypergraph induced by user interaction histories, a top-k user similarity graph, and one-hop, relation-aware knowledge-graph aggregation. The hypergraph branch learns high-order item co-occurrence representations, which are aggregated into initial user vectors and then refined through user similarity propagation. On the item side, user-conditioned relation attention aggregates one-hop KG neighbors to produce semantic item representations. User and item representations are fused by an MLP scorer, and a lightweight popularity-aware post-scoring adjustment can optionally be applied to moderate head-item dominance. Experiments on MovieLens-1M, Last.FM and Book-Crossing show strong performance among the compared baselines in AUC, ACC, and Recall@K. Full article

Background/Objectives: Regular dialysis sessions impose a fixed schedule on the patient’s days and weeks; this can lead to negative emotions, low mood, helplessness, and a lack of control over their treatment, which significantly reduces the quality of life for these patients. The aim of this study was to assess the mood, level of illness acceptance, and quality of life among dialysis patients undergoing relaxation therapy using virtual reality (VR). Methods: Sixty hemodialysis (HD) patients were recruited for a single-arm study. A personal questionnaire as well as the AIS, PHQ-9, and KDQOL-36™ were used. After one month of the control period, 22 patients were analyzed and then continued with one month of VR relaxation therapy consisting of 360° scenarios or 2D landscape movies. Finally, 16 patients were analyzed for the outcomes during dialysis, three times a week. Results: The data analysis showed a small significant increase in AIS scores after the VR therapy. In the PHQ-9, slight significant reductions in scores were observed at the end of VR therapy. Analysis of the Physical Component Summary (PCS), but not for Mental Component Summary (MCS), results showed statistically significant increases after VR therapy. Conclusions: The study group of dialysis patients showed small but significant improvements in mood, disease acceptance, and quality of life. The VR therapy intervention may be a useful complementary tool to comprehensive treatment and rehabilitation for hemodialysis patients, but multi-center studies are needed for a larger group of patients. Full article

In this work, an amide extractant was employed to purify Mo(VI) from chloride media, with particular emphasis on the extraction behavior of impurities and their migration during the extraction and scrubbing stages. The effects of hydrochloric acid concentration, extractant concentration, phase ratio, and temperature on Mo(VI) extraction were examined to clarify the extraction equilibrium and kinetics. Under the optimized conditions, a high extraction efficiency of 93.13% was achieved in a single stage. The loaded organic phase was subsequently purified by hydrochloric acid scrubbing, effectively removing co-extracted impurities while maintaining minimal Mo loss. Efficient stripping of Mo(VI) was realized using an ammonia solution with a stripping efficiency of 98.47%. FT-IR and ESI-MS analyses revealed that Mo(VI) was extracted as a protonated molybdenum oxychloride species interacting with the amide extractant through hydrogen bonding. Density functional theory calculations further confirmed the favorable interaction between the protonated molybdenum species and the carbonyl oxygen of the amide extractant. Thermodynamic analysis indicated that the extraction process was exothermic, with an enthalpy change of −22.17 kJ/mol. These findings provide mechanistic insight into the amide extraction of molybdenum from chloride systems and offer practical guidance for the purification of low-purity molybdenum products. Full article

Movie genre classification is a significant challenge in narrative analysis, as traditional methods often fail to capture complex structural relationships within movie stories. This study introduces the Intra-Cluster Weighted Movie Network (ICWMN), a novel framework designed to improve classification by using intra-movie relationships through Graph Neural Networks (GNNs). We constructed a large-scale dataset of 1631 movie character networks using an automated pipeline comprising web scraping, regular expressions, and fine-tuned BERT models for entity recognition. To address the computational limitations of fully connected models, we partition ICWMN into clusters and establish edges only between the k-most similar nodes using the K-Nearest Neighbor algorithm and various distance measures, such as the Laplacian and NetLSD. XGBoost is applied to optimize high-dimensional node feature vectors. Experimental results demonstrate outstanding performance, with the Graph Attention Network (GAT) emerging as the top-performing architecture, resulting in classification accuracies that peak at $95.00\%$ on our 1631-movie dataset and an exceptional $97.30\%$ on the 773-movie Moviegalaxies dataset. These findings confirm that prioritizing spectral properties and cluster-based network topologies significantly improve the precision and stability of genre classification compared to state-of-the-art methods. Full article

The proliferation of AI-generated content and personalized AI systems has sharpened two fundamental and related computational problems: the progressive erosion of authentic identity in AI-mediated representations, and the growing difficulty of distinguishing human-originated from AI-generated behavioral and textual streams. This paper proposes a rigorous computational framework in which digital identity is formalized as a holomorphic function of complex time T = (a + ib) ∈ ℂ, where the real component Re$\left(T\right)$ encodes chronological progression and the imaginary component Im$\left(T\right)$ spans a continuum from episodic memory ($Im\left(T\right) < 0$) through the present moment ($Im\left(T\right) = 0$) to prospective imagination ($Im\left(T\right) > 0$). We argue that holomorphicity—enforced via Cauchy–Riemann regularization during CTNN learning (Proposition 1)—provides a theoretically grounded encoding of identity coherence, and discuss its advantages over alternative mathematical choices, including Lipschitz continuity, ${\mathit{C}}^{\mathbf{\infty }}$ smoothness, piecewise analytic functions, and stochastic models. Under four explicit Assumptions 1–4 covering the Markovian structure and fixed context window of current LLM architectures, we establish via Lemmas 1 and 2 and Theorem 1 that AI-generated behavioral trajectories exhibit structural limitations in satisfying the Cauchy–Riemann conditions at temporal depths characteristic of human biographical memory—limitations that do not arise for human trajectories learned under CTNN regularization. Building on this result, we introduce the Human–AI Authenticity Discriminant (HAAD), a theoretically grounded classifier with a fully specified calibration algorithm and sensitivity analysis ($\mathit{\kappa }$ ΔAUROC ≤ 0.04 over ±30% perturbation). Five metrics—TCS, ISI, PAS, GAS, and HAAD—are derived analytically from the holomorphic structure. The algorithmic framework is instantiated on four real-world datasets: MovieLens 25M, the Pushshift Reddit corpus, the Stack Overflow Data Dump, and the LIAR dataset. On the LIAR benchmark, TDT-HAAD achieves AUROC = 0.82 (95% CI: [0.79, 0.85]), exceeding a RoBERTa-based LLM detector baseline (AUROC = 0.75, DeLong p < 0.01); an ablation study supports the structural contribution of each component. A credibility harvesting signature is detectable 45.3 ± 12.1 days before standard temporal models reach statistical significance. Full article

From the early Middle Ages, but mostly the late twelfth century, the love story involving Tristan and Isolde (also Yseut) attracted much attention, originating in the Celtic world but fully developed first by the Old French poet Béroul (ca. 1160) and Thomas of England, of Britain, or of Brittanny, around 1170. It was rendered into virtually every European language since then and has also appealed to artists and musicians throughout time. We know, for example, of tiles, tapestry, sculptures, paintings, musical tunes, manuscript illuminations, and other visual representations of the intense but highly problematic relationship between these two young people. In essence, while Yseult is married to the King of Cornwall, Mark/Marke, a love potion, a metaphorical symbol of their deep feelings, bonds her with Tristan for the rest of their lives (a limited number of years in the earlier versions). Ultimately, at least in most versions, they are destined to die because of their love, which is incompatible with the social norms of their time, and this Romantic theme has hence also played a huge role in the nineteenth and twentieth centuries, and is perhaps highly important also today, as expressed by modern movies and music engaging with this love story. This study first traces in rough brushstrokes the history of the reception of this literary theme from the twelfth to the twenty-first century; then, it returns to the various medieval versions to investigate the critical issues contained in this highly popular story, which has never lost its relevance and attraction for audiences throughout time. Since the focus rests on the history of reception, less on comparative literature, the main tradition to be traced will be the German one. Full article

Alexithymia has been associated with deficits in social cognition, although findings are inconsistent and often limited by methodological constraints. This study aimed to clarify this relationship using ecologically valid and traditional standardized measures across multiple social-cognitive domains. A total of 163 adults from the general population completed a series of measures, including the Toronto Alexithymia Scale (TAS-20), Questionnaire of Cognitive and Affective Empathy (QCAE), Reading the Mind in the Eyes Test (RMET), Movies for the Assessment of Social Cognition (MASC), and Amsterdam Dynamic Facial Expression Set—Bath Intensity Variations (ADFES-BIV). Results of hierarchical regression analyses revealed that alexithymia facets significantly predicted performance on affective and cognitive empathy (QCAE), and Theory of Mind (MASC total and “No ToM” scores). The only exceptions were affective Theory of Mind (RMET) and recognition of others’ emotions (ADFES-BIV), for which none of the alexithymia facets emerged as significant predictors. The findings suggest that alexithymia is associated with poorer performance in cognitive and affective empathy and contextual Theory of Mind, whereas no significant association emerged for emotion recognition. The results suggest that integrating dynamic and context-rich tasks may be useful for detecting subtle social-cognitive difficulties in individuals with alexithymic traits. Full article

Accurate biomolecule quantification at ultralow concentrations remains a major challenge because conventional ensemble assays report population averaged signals and therefore lose sensitivity in low-abundance regimes. Single molecule fluorescence counting can overcome this limitation by converting emission into discrete digital events, but practical implementation is often hindered by manual inspection, limited reproducibility, and the complexity of machine learning based analysis. Here, we present a simple and general analytical framework for rapid single molecule detection based on a deterministic threshold algorithm that exploits the temporal signature of fluorescence blinking. The method operates directly on time resolved fluorescence image stacks, applies median filter-based noise suppression, and identifies candidate single molecule events from consecutive frame-to-frame intensity transitions without the need for training data or model fitting. Applied to Alexa Fluor 488, Alexa Fluor 647, and Rhodamine Red–X datasets, the approach reproduced the concentration dependent trends observed by manual counting, while providing more standardized detection under weak signal and high background conditions. Dye specific operating thresholds yielded robust counting behavior and preserved approximately linear concentration dependent response across the tested range. Compared with manual analysis, which required inspection of only selected grid regions, the automated workflow processed full movie stacks and reduced analysis time from ~3 h to ~20 min per concentration, corresponding to an approximately 9-fold gain in efficiency. These results establish an interpretable, computationally lightweight, and experimentally adaptable strategy for fluorescence single molecule counting that can support rapid diagnostics and provide a practical foundation for future extensions in automated localization, clustering, and real time molecular analysis. Full article

This study investigates multi-label movie genre prediction from user-written reviews in which textual content is inherently subjective and the movies reviewed naturally belong to multiple genres. To address extreme class imbalance and label sparsity in the IMDb Large Movie Review Dataset, 234 fine-grained genre labels are consolidated into 35 parent categories using a deterministic genre-mapping strategy. A unified experimental pipeline evaluates traditional feature-based models (TF-IDF vectorization with Logistic Regression and Linear SVM), a sequence-based BiLSTM with self-attention using GloVe embeddings, and transformer-based architectures (DistilBERT and RoBERTa) under consistent evaluation metrics. Experimental analyses indicate that transformer-based architectures outperform alternative approaches, with RoBERTa achieving the best performance (Macro-F1 = 0.518, Micro-F1 = 0.576). The results indicate that genre consolidation enhances robustness under long-tailed label distributions. Moreover, contextualized transformer representations better capture implicit and subjective cues. The results further clarify practical trade-offs between predictive performance and computational efficiency across model families. Full article

Rising antimicrobial resistance has revived global interest in phage therapy, yet its transition to standard clinical practice remains slow. This challenge is not solely due to a lack of efficacy. Instead, we face a fundamental conceptual barrier caused by an “evaluation mismatch.” Traditional regulations treat phages as static chemical molecules—like taking a “snapshot.” However, biologically, phages are dynamic, evolving populations—more like a living “movie.” In this review, we use Schrödinger’s cat metaphor to explain this reality: phage variability is not a defect, but an essential feature. To bridge this gap, we propose a Controlled Evolutionary Platform. By distinguishing between a fixed “Safety Core” and a fluctuating “Adaptive Periphery,” we can manage viral evolution rather than trying to stop it. Ultimately, to integrate phages into modern medicine, we must redefine “consistency”: shifting our focus from preserving a fixed genetic sequence to ensuring the reliable performance of population dynamics. Full article

This study examines how immersive narrative resources, whether technological–sensory, narrative–structural, or contextual, are deployed in contemporary blockbuster cinema and to what extent audiences recognize and value them in their evaluations. Using Avatar: Fire and Ash as a case study, the research follows a sequential mixed-methods design. In the first phase, a qualitative film analysis identifies eight types of cognitive immersion, drawing on established theoretical frameworks of narrative immersion. The second phase is quantitative and involves the computational analysis of 1133 valid reviews from Internet Movie Database (IMDb) through Natural Language Processing (NLP) techniques, including n-gram frequency analysis, Latent Dirichlet Allocation (LDA) topic modeling with 3 topics after perplexity minimization, and sentiment polarity analysis. The LDA model reveals three discursive clusters, experiential and emotional, technical and comparative, and critical, with the latter concentrated mostly in low-rated reviews. Text sentiment and numeric ratings show a moderate positive correlation (r = 0.53, p < 0.001), pointing to a general but imperfect alignment between the two modes of evaluation. Markers of content fatigue (nothing new, predictable, boring) appear in 25.1% of the reviews, yet a third of those are still rated 8 or higher. When cross-tabulating the immersion categories with audience language, phenomenological and affective dimensions such as Emotional Engagement (59.8%) and Haptic/Sensory Experience (59.1%) emerge as the most frequently discussed, while cinematographic techniques like Bracketing (2.6%) are barely mentioned. Taken together, the findings suggest that the franchise sustains its appeal through a form of embodied sensory engagement that operates largely independent of narrative novelty. Full article