Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360 video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360 video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360 video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360 sequences. The following factors' influences were also analyzed: assessment methodology, sequence duration, Head-Mounted Display (HMD) device, uniform and non-uniform coding degradations, and simulator sickness assessment methods. The obtained results have demonstrated the validity of Absolute Category Rating (ACR) and Degradation Category Rating (DCR) for subjective tests with 360 videos, the possibility of using 10-second videos (with or without audio) when addressing quality evaluation of coding artifacts, as well as any commercial HMD (satisfying minimum requirements). Also, more efficient methods than the long Simulator Sickness Questionnaire (SSQ) have been proposed to evaluate related symptoms with 360 videos. These results have been instrumental for the development of the ITU-T Recommendation P.919. Finally, the annotated dataset from the tests is made publicly available for the research community.

Recommender system is a popular tool that aims to provide personalized suggestions to user about items, products, services, and so on. Recommender system has effectively been used in online social networks, especially the location-based social networks for providing suggestions for interesting places known as POIs (points-of-interest). Popular recommender systems explore historical data to learn users’ preferences and, subsequently, they recommend locations to an active user. This strategy faces a major problem when a new POI or business evolves in a city. New business has no historical user experience data. Thus, a recommender system fails to gather enough knowledge about the new businesses, resulting in ignoring them during recommendations. This scenario is popularly known as a cold-start POI problem. Users never get recommendations of the new businesses in a city even though they can be relevant to a user. Also, from a business owner’s perspective, such a recommendation strategy does not help its reachability among users. Therefore, it is important for a recommender system to remain updated with new businesses in a city and ensure that all relevant POIs are recommended to a user irrespective of their lifetime. A POI recommendation approach is proposed in this work that can effectively handle the new businesses, or the cold-start POI problem, in a city. We crowdsource descriptions of cold-start POIs from various online social networks. The reviews of users are exploited here to learn the inherent features at the existing POIs and the new crowdsourced POIs. Finally, the proposed approach recommends top- K POIs consisting of the existing and new POIs. We perform experiments on the real-world Yelp dataset, which is one of the largest available data resources containing details on a wide range of businesses, users, and reviews. The proposed approach is compared with four existing POI recommendation approaches. The obtained results show that our approach outperforms others in handling cold-start POIs.

This paper was accepted for publication in the journal Knowledge-Based Systems and the definitive published version is available at http://dx.doi.org/10.1016/j.knosys.2016.09.017.The proliferation of GPS enabled devices has led people to share locations both consciously and unconsciously. Large spatio-temporal data comprising of shared locations and whereabouts are now being routinely collected for analysis. As user movements are generally driven by their interests, so mining these mobility patterns can reveal commonalities between a pair of users. In this paper, we present a framework for mining the published trajectories to identify patterns in user mobility. In this framework, we extract the locations where a user stays for a period of time popularly known as stay points. These stay points help to identify the interests of a user. The statistics of pattern and check-in distributions over the GPS data are used to formulate similarity measures for finding K-nearest neighbors of an active user. In this work, we categorize the neighbors into three groups namely strongly similar, closely similar and weakly similar. We introduce three similarity measures to determine them, one for each of the categories. We perform experiments on a real-world GPS log data to find the similarity scores between a pair of users and subsequently find the effective K-neighbors. Experimental results show that our proposed metric outperforms existing metrics in literature

Check-in facility in a location-based social network (LBSN) enables people to share location information as well as real-life activities. Analysing these historical series of check-ins to predict the future locations to be visited has been very popular in the research community. However, it has been found that people do not intend to share the privately visited locations and activities in a LBSN. Research into extrapolating unchecked locations from historical data is limited. Knowledge of hidden locations can have a wide range of benefits to society. It may help the investigating agencies in identifying possible places visited by a suspect, a marketing company in selecting potential customers for targeted marketing, for medical representatives in identifying areas for disease prevention and containment, etc. In this paper, we propose an Associative Location Prediction Model (ALPM), which infers privately visited unchecked locations from a published user trajectory. The proposed ALPM explores the association between a user’s checked-in data, the Hidden Markov Model and proximal locations around a published check-in for predicting the unchecked or hidden locations. We evaluate ALPM on real-world Gowalla LBSN dataset for the users residing in Beijing, China. Experimental results show that the proposed model outperforms the existing state-of-the-art work in the literature.

In this contribution, a model for revealing the presence of autism spectrum disorder by exploiting visual information is developed. This condition is characterized by a deficit in social behaviour and nonverbal interactions such as specific facial expressions, reduced eye contact, and body gestures. Advancements in multimedia technologies can help in understanding symptoms for early detection of the disorder. In the proposed model, both the image content and the viewing behaviour are used for defining relevant features to be used in a machine learning-based classifier. A training phase is realized by taking multiple images and scanpaths representing the viewing behaviour of persons affected and not by the disorder. The influence of specific objects in the scene is considered. Finally, the number of fixations towards centre of the scene and duration for which a subject looked at the central area is also considered. A decision tree based classifier is used for training the model. The achieved results show that by taking into account the semantic and image features extracted from content, fixation, and center-bias, it is possible to estimate the presence of autism spectrum disorder. The results obtained in the performed experiments are promising even if they show room for improvement.

Omnidirectional, or 360°, cameras are able to capture the surrounding space, thus providing an immersive experience when the acquired data is viewed using head mounted displays. Such an immersive experience inherently generates an illusion of being in a virtual environment. The popularity of 360° media has been growing in recent years. However, due to the large amount of data, processing and transmission pose several challenges. To this aim, efforts are being devoted to the identification of regions that can be used for compressing 360° images while guaranteeing the immersive feeling. In this contribution, we present a saliency estimation model that considers the spherical properties of the images. The proposed approach first divides the 360° image into multiple patches that replicate the positions (viewports) looked at by a subject while viewing a 360° image using a head mounted display. Next, a set of low-level features able to depict various properties of an image scene is extracted from each patch. The extracted features are combined to estimate the 360° saliency map. Finally, bias induced during image exploration and illumination variation is fine-tuned for estimating the final saliency map. The proposed method is evaluated using a benchmark 360° image dataset and is compared with two baselines and eight state-of-the-art approaches for saliency estimation. The obtained results show that the proposed model outperforms existing saliency estimation models.