Card-based protocols perform secure computation using physical playing cards, and their efficiency is primarily evaluated by two metrics: the number of cards and the number of shuffles. In this paper, we present three new card-based protocols that reduce the number of cards. The first protocol supports any Boolean circuit and decreases the number of cards. The second protocol extends the first to a committed-format version, while the third is specialized for Boolean circuits without a branched input, achieving an even smaller number of cards. All the proposed protocols are based on Yao’s garbled-circuit framework, similar to previous single-shuffle card-based protocols. Our main contribution is a novel encoding method that represents the truth table of each Boolean gate with fewer cards than existing schemes. Consequently, our protocols require only six cards per gate in a single-shuffle execution, providing a significant improvement in efficiency over prior work. Furthermore, by incorporating the card-based free-XOR introduced by Manabe–Shinagawa, we can further reduce the number of cards in certain configurations.
Review summarization performs a significant reduction of lengthy user reviews to short summaries, specifying the major opinions about the product or service. Users face difficulty analyzing large volumes of unstructured online reviews. These challenges are overcome by introducing a comprehensive approach for graph-guided thematic review summarization that enhances the analysis of multi-domain reviews with personalization, interpretability, and efficiency as its core attributes. This research introduces text-derived distilling relational spectral graph-based segmentation (TDRSGS) with the Motorbike Courier Optimization algorithm (MCO), efficiently identifies and groups semantically similar review themes for accurate summarization. The large language model, primarily GPT-4, generates concise theme-level and global summaries while preserving semantic insights and reducing redundancy. The transformer engagement predictor (TEP) tailors summaries to fit the user’s preferences, thereby providing personalised and context-aware recommendations. An exact local interpretable model-agnostic explanation increases the quality of trust and user understanding. The experimental evaluation on a variety of datasets, including Amazon, Flipkart, Yelp, TripAdvisor, IMDB, Goodreads, and RateMDs, shows that the model’s performance is excellent, getting a BERTScore of 97.3, ROUGE-L of 98.4, and accuracy of 98.7
Traditional opinion analysis methods frequently reduce online discussions to simplistic categories, such as favor, against, and none. This oversimplification fails to capture the nuances of opinions, such as the intensity of feelings or the degree to which a stance leans toward favor or against. To address this limitation, a new method called SFLBiL is proposed, which leverages fuzzy logic and BiLSTM to create more nuanced categories for opinions. Specifically, token-based fuzzy matching scores between tweet pairs are transformed into linguistic memberships via fuzzy membership functions and inference rules, which are fused with glove word embeddings and passed through a BiLSTM network that jointly captures ordinal class structure and sequential context. This allows for a more accurate understanding of people’s views, especially in the context of ordered-class classification. The performance of the proposed model is evaluated on two new datasets and the benchmark SemEval-2016 TaskA dataset. The SFLBiL model achieves consistent improvements over state-of-the-art (SOTA) models, with accuracy and F1-score improvements of 4.65
Semantic communication is the key to enable 6G communication, from sending raw data to conveying meaningful information. In contrast, current models fail to process multimodal information in a structured, context-aware manner. To tackle this problem, the proposed Multi-modal Knowledge-Graph Guided Graph Transformer (MKG-GT) combines text and image modalities into a unified heterogeneous graph equipped with a prior knowledge graph. It employs a dual-stage fusion process that captures semantics and structural relationships with Graph Attention Networks (GAN) and Graph Kernel Attention Transformers (GKAT), respectively, and a lightweight semantic compression unit to encode the representation for efficient 6G transmission. Experiments on Flickr30K achieve 0.95 accuracy, 0.94 F1, and 0.97 macro F1, while improving the performance of the current baselines. The model’s semantic pipeline inference time is 1.05ms, supporting low-latency, real-time 6G applications with pre-computed features. Overall, the results show that graph reasoning, knowledge augmentation and compact semantic encoding are a strong method for next-generation multimodal communication.
Audiograms are used to diagnose auditory diseases and are depicted in a standardized manner using mostly identical threshold signs. This study proposes a novel method for detecting and standardizing audiometric thresholds using artificial intelligence, specifically DETR and YOLO models. We introduce a size-fixed rectangular frame and size-fixed bounding-box strategy tailored to the standardized characteristics of audiogram symbols, while comparing performance between YOLOv5 and DETR. The method comprises two main phases: training and detection. In the training phase, a dataset of audiograms is prepared by extracting frame rectangles from original audiogram images and resizing them to 416 × 416 pixels. Air-conduction and bone-conduction threshold signs are annotated using a fixed bounding-box size with the labelImg software. A DETR/YOLO model is then trained on this annotated dataset, leveraging transfer learning from a pre-trained model to enhance efficiency and accuracy. In the detection phase, the trained DETR/YOLO model is applied to original audiogram images. Frame rectangles are extracted and resized to 416 × 416 pixels before being subjected to inference. The error detection rate was as low as 0.3
