Simon Kolmanič

Trade shows are key venues for presenting new products and research, yet physical exhibits can be costly and impractical. This study evaluates whether digital exhibitions utilizing VR and AR can effectively replace physical trade show presentations. Across three real-world experiments conducted at major international fairs, we deployed different digital exhibition formats and recorded visitor numbers, engagement ratios, and interaction durations. All exhibitions attracted substantially more visitors than the fairs’ average exhibitor-to-visitor ratios, exceeding them by 30 to 80%. In the first experiment, surveys captured user experience and success factors, while exhibitor interviews across all experiments provided insights into perceived impact. Results indicate that visitor interaction with digital content was the primary driver of engagement. By evaluating VR headsets, AR glasses, holographic projections, and mobile AR, we identified their respective strengths and limitations. Results indicate that well-designed digital exhibitions can serve as effective, cost-efficient alternatives to physical trade show presentations.

Knowledge graph embedding maps the semantics of entities and relations to a low-dimensional space by optimizing the vector distance between positive and negative triples. Traditional negative sampling techniques usually regard high-scoring triples as high-quality negative triples, but this not only easily introduces false negative triples, but also ignores important information in the graph structure. To address these issues, we propose an easily pluggable hierarchical knowledge graph embedding framework. High-quality corrupted entities are generated through semantic and structural information, and then margin estimation is used to generate high-quality negative triples, and the structural information of the entities is combined to perform link prediction on new facts. Experimental results show that our framework improves the performance of the original knowledge graph embedding model, in which the hierarchical subgraph negative sampling module outperforms other negative sampling techniques. The framework we proposed can be easily adapted to various knowledge graph embedding models and explain the prediction results.

Existing conversational recommender systems (CRS) use insufficient generality in incorporating external information using knowledge graphs. The recommendation module and generation module are loosely connected during model training and shallowly integrated during inference. A simple switching or copying mechanism is used to merge recommended items into generated responses. These problems significantly degrade the recommendation performance. To alleviate this problem, we propose a novel unified framework for collaboratively enhancing conversational recommendations using pre-trained language mod-els and knowledge graphs (CollRec). We use a fine-tuned pre-trained language model to efficiently extract knowledge graphs from conversational text descriptions, perform entity-based recommendations based on the generated graph nodes and edges, and fine-tune a large-scale pre-trained language model to generate fluent and diverse responses. Experimental results on the WebNLG 2020 Challenge dataset, ReDial dataset, and Reddit-Movie dataset show that our CollRec model significantly outperforms the state-of-the-art methods.

This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows–Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.

The specific task of vehicle re-identification is how to quickly and correctly match the same vehicle in different scenarios. In order to solve the problem of inter-class similarity and environmental interference in vehicle images in complex scenes, one fusion attention method is put forward based on the idea of obtaining the distinguishing features of details—the mechanism for the vehicle re-identification method. First, the vehicle image is preprocessed to restore the image’s attributes better. Then, the processed image is sent to ResNet50 to extract the features of the second and third layers, respectively. Then, the feature fusion is carried out through the two-layer attention mechanism for a network model. This model can better focus on local detail features, and global features are constructed and named SDLAU-Reid. In the training process, a data augmentation strategy of random erasure is adopted to improve the robustness. The experimental results show that the mAP and rank-k indicators of the model on VeRi-776 and the VehicleID are better than the results of the existing vehicle re-identification algorithms, which verifies the algorithm’s effectiveness.

The domestic population has paid increasing attention to ancient Chinese history and culture with the continuous improvement of people’s living standards, the rapid economic growth, and the rapid advancement of information science and technology. The use of information technology has been proven to promote the spread and development of historical culture, and it is becoming a necessary means to promote our traditional culture. This paper will build a knowledge graph of ancient Chinese history and culture in order to facilitate the public to more quickly and accurately understand the relevant knowledge of ancient Chinese history and culture. The construction process is as follows: firstly, use crawler technology to obtain text and table data related to ancient history and culture on Baidu Encyclopedia (similar to Wikipedia) and ancient Chinese history and culture related pages. Among them, the crawler technology crawls the semi-structured data in the information box (InfoBox) in the Baidu Encyclopedia to directly construct the triples required for the knowledge graph, crawls the introductory text information of the entries in Baidu Encyclopedia, and specialized historical and cultural websites (history Chunqiu.com, On History.com) to extract unstructured entities and relationships. Secondly, entity recognition and relationship extraction are performed on an unstructured text. The entity recognition part uses the Bidirectional Long Short-Term Memory-Convolutional Neural Networks-Conditions Random Field (BiLSTM-CNN-CRF) model for entity extraction. The relationship extraction between entities is performed by using the open source tool DeepKE (information extraction tool with language recognition ability developed by Zhejiang University) to extract the relationships between entities. After obtaining the entity and the relationship between the entities, supplement it with the triple data that were constructed from the semi-structured data in the existing knowledge base and Baidu Encyclopedia information box. Subsequently, the ontology construction and the quality evaluation of the entire constructed knowledge graph are performed to form the final knowledge graph of ancient Chinese history and culture.