Information and Communication Technologies in Tourism

A display apparatus includes an image processor which processes an image to display an image content; a display unit which displays thereon an image content that is processed by the image processor; and a controller which displays a user interface (UI) menu including a plurality of UI items to search the image content, as one of a two-dimensional (2D) layout by which the plurality of UI items are arranged in a 2D manner, and a three-dimensional (3D) layout by which the plurality of UI items are arranged in a 3D manner , and changes displaying the one of the layouts to display the other of the layouts according to a user's command while maintaining a continuity of the arrangement of the plurality of UI items. Accordingly, search for an image content can be efficiently performed by using a UI menu.

Display apparatus and control method thereof

More and more people read the news online, e.g., by visiting the websites of their favorite newspapers or by navigating the sites of news aggregators. However, the abundance of news information that is published online every day through different channels can make it challenging for readers to locate the content they are interested in. The goal of News Recommender Systems (NRS) is to make reading suggestions to users in a personalized way. Due to their practical relevance, a variety of technical approaches to build such systems have been proposed over the last two decades. In this work, we review the state-of-the-art of designing and evaluating news recommender systems over the last ten years. One main goal of the work is to analyze which particular challenges of news recommendation (e.g., short item life times and recency aspects) have been well explored and which areas still require more work. Furthermore, in contrast to previous surveys, the paper specifically discusses methodological questions and today’s academic practice of evaluating and comparing different algorithmic news recommendation approaches based on accuracy measures.

News recommender systems – Survey and roads ahead

In this paper, we study the problem of constructing private classifiers using decision trees, within the framework of differential privacy. We first present experimental evidence that creating a differentially private ID3 tree using differentially private low-level queries does not simultaneously provide good privacy and good accuracy, particularly for small datasets.

In search of better privacy and accuracy, we then present a differentially private decision tree ensemble algorithm based on random decision trees. We demonstrate experimentally that this approach yields good prediction while maintaining good privacy, even for small datasets. We also present differentially private extensions of our algorithm to two settings: (1) new data is periodically appended to an existing database and (2) the database is horizontally or vertically partitioned between multiple users.

A Practical Differentially Private Random Decision Tree Classifier

Advances in multimedia data acquisition and storage technology have led to the growth of very large multimedia databases. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging problem. This challenge has opened the opportunity for research in Multimedia Data Mining (MDM). Multimedia data mining can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. The motivation for doing MDM is to use the discovered patterns to improve decision making. MDM has therefore attracted significant research efforts in developing methods and tools to organize, manage, search and perform domain specific tasks for data from domains such as surveillance, meetings, broadcast news, sports, archives, movies, medical data, as well as personal and online media collections. This paper presents a survey on the problems and solutions in Multimedia Data Mining, approached from the following angles: feature extraction, transformation and representation techniques, data mining techniques, and current multimedia data mining systems in various application domains. We discuss main aspects of feature extraction, transformation and representation techniques. These aspects are: level of feature extraction, feature fusion, features synchronization, feature correlation discovery and accurate representation of multimedia data. Comparison of MDM techniques with state of the art video processing, audio processing and image processing techniques is also provided. Similarly, we compare MDM techniques with the state of the art data mining techniques involving clustering, classification, sequence pattern mining, association rule mining and visualization. We review current multimedia data mining systems in detail, grouping them according to problem formulations and approaches. The review includes supervised and unsupervised discovery of events and actions from one or more continuous sequences. We also do a detailed analysis to understand what has been achieved and what are the remaining gaps where future research efforts could be focussed. We then conclude this survey with a look at open research directions.

https://www.comp.nus.edu.sg/~cs5342/readings/mm-datamining.pdf

Multimedia data mining: state of the art and challenges

Camera-equipped unmanned aerial vehicles (UAVs), or “drones,” are a recent addition to standard audiovisual shooting technologies. As drone cinematography is expected to further revolutionize media production, this paper presents an overview of the state-of-the-art in this area, along with a brief review of current commercial UAV technologies and legal restrictions on their deployment. A novel taxonomy of UAV cinematography visual building blocks, in the context of filming outdoor events where targets (e.g., athletes) must be actively followed, is additionally proposed. Such a taxonomy is necessary for progress in intelligent/autonomous UAV shooting, which has the potential of addressing current technology challenges. Subsequently, the concepts and advantages inherent in multiple-UAV cinematography are introduced. The core of multiple-UAV cinematography consists in identifying different combinations of multiple single-UAV camera motion types, assembled in meaningful sequences. Finally, based on the defined UAV/camera motion types, tools for managing a partially autonomous, multiple-UAV fleet from the director’s point of view are presented. Although the overall focus is on cinematic coverage of sports events, the majority of our contributions also apply in different scenarios, such as movies/TV production, newsgathering, or advertising.

High-Level Multiple-UAV Cinematography Tools for Covering Outdoor Events

Improvements in digital technology have made possible the production and distribution of huge quantities of digital multimedia data. Tools for high-level multimedia documentation are becoming indispensable to efficiently access and retrieve desired content from such data. In this context, automatic genre classification provides a simple and effective solution to describe multimedia contents in a structured and well understandable way. We propose in this article a methodology for classifying the genre of television programmes. Features are extracted from four informative sources, which include visual-perceptual information (colour, texture and motion), structural information (shot length, shot distribution, shot rhythm, shot clusters duration and saturation), cognitive information (face properties, such as number, positions and dimensions) and aural information (transcribed text, sound characteristics). These features are used for training a parallel neural network system able to distinguish between seven video genres: football, cartoons, music, weather forecast, newscast, talk show and commercials. Experiments conducted on more than 100 h of audiovisual material confirm the effectiveness of the proposed method, which reaches a classification accuracy rate of 95%.

Parallel neural networks for multimodal video genre classification

Current Web technology has enabled the distribution of informative content through dynamic media platforms.In addition, the availability of the same content in the form of digital multimedia data has dramatically increased.Content-based, cross-media retrieval applications are needed to efficiently access desired information from this variety of data sources. This paper presents a novel approach for cross-media information aggregation, and describes a prototype system implementing this approach. The prototype adopts online newspaper articles and TV newscasts as information sources, to deliver a service made up of items including both contributions. Extensive experiments prove the effectiveness of the proposed approach in a real-world business context.

/pdf/a-generalised-cross-modal-clustering-method-applied-to-55lcfe06vl.pdf

A generalised cross-modal clustering method applied to multimedia news semantic indexing and retrieval

In this paper a mixed reality application is introduced; this application runs on Microsoft Hololens and has been designed to provide information on a city scale. The application was developed to provide information about historical buildings, thus supporting cultural outdoor tourism. The huge amount of multimedia data stored in the archives of the Italian public broadcaster RAI, is used to enrich the user experience. A remote application of image and video analysis receives an image flow by the user and identifies known objects framed in the images. The user can select the object (monument/building/artwork) for which augmented contents have to be displayed (video, text audio); the user can interact with these contents by a set of defined gestures. Moreover, if the object of interest is detected and tracked by the mixed reality application, also 3D contents can be overlapped and aligned with the real world.

https://google.iopscience.iop.org/article/10.1088/1757-899X/364/1/012048/pdf

Enhancing cultural tourism by a mixed reality application for outdoor navigation and information browsing using immersive devices

In this paper we investigate the problem of automatically identifying the genre of TV programmes. The approach here proposed is based on two foundations: Gaussian mixture models (GMMs) and artificial neural networks (ANNs). Firstly, we use Gaussian mixtures to model the probability distributions of low-level audiovisual features. Secondly, we use the parameters of each mixture model as new feature vectors. Finally, we train a multilayer perceptron (MLP), using GMM parameters as input data, to identify seven television programme genres. We evaluated the effectiveness of the proposed approach testing our system on a large set of data, summing up to more than 100 hours of broadcasted programmes.

Maurizio Montagnuolo

Papers

High-Level Multiple-UAV Cinematography Tools for Covering Outdoor Events

Parallel neural networks for multimodal video genre classification

A generalised cross-modal clustering method applied to multimedia news semantic indexing and retrieval

Enhancing cultural tourism by a mixed reality application for outdoor navigation and information browsing using immersive devices

Automatic Genre Classification of TV Programmes Using Gaussian Mixture Models and Neural Networks