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We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility. [ABSTRACT FROM AUTHOR]

Association rules are an important problem in data mining. Massively increasing volume of data in real life databases has motivated researchers to design novel and incremental algorithms for association rules mining. In this paper, we propose an incremental association rules mining algorithm that integrates shocking interestingness criterion during the process of building the model. A new interesting measure called shocking measure is introduced. One of the main features of the proposed approach is to capture the user background knowledge, which is monotonically augmented. The incremental model that reflects the changing data and the user beliefs is attractive in order to make the over all Knowledge Discovery in Databases (KDD) process more effective and efficient. We implemented the proposed approach and experiment it with some public datasets and found the results quite promising. [ABSTRACT FROM AUTHOR]

With the increasing popularity of Facebook, a new social phenomenon connected with its extensive use has appeared: namely, Facebook intrusion. The main aim of the study was to examine the mediating role of loneliness in the relation between friendships and Facebook intrusion. We analyzed data from five countries: Poland (N = 399), Slovakia (N = 266), Syria (N = 475), Ecuador (N = 327), and Malaysia (N = 241). The total sample consisted of 1731 participants who had Facebook accounts; 61% of the participants were women. We administered the Facebook Intrusion Scale, the Loneliness Scale, and the Friendship Scale. The results indicated differences in the role of loneliness and friendship in Facebook intrusion, depending on the country. Among young Polish people, friendship decreases loneliness and loneliness decreases Facebook intrusion. Whereas among Ecuadorians friendship decreases loneliness, while loneliness increases Facebook intrusion. [ABSTRACT FROM AUTHOR]