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To ensure a certain degree of usability, a library website should be carefully designed, especially since end users constitute a multitude of people with different needs and demands. The focal objective of this research was to investigate how different types of end users (i.e. pupils, students, the working population, seniors and researchers) respond to a library website in terms of its effectiveness, efficiency and satisfaction, which together represent its usability. The answers were obtained by performing formal usability testing, including think-aloud protocol, log analysis and questionnaires. The results of the statistical analysis show that different groups of end users achieve different levels of effectiveness and efficiency, while there is no significant difference between groups in satisfaction level. The results also indicate that participants did not achieve the threshold for a usable website. Based on the identified weaknesses, researchers present recommendations for improving a website’s usefulness, especially for non-experienced users. This research has two main contributions: (1) the connection between the theoretical definition and practical use of ISO 9241-11 attributes and (2) a usability testing procedure with a measurement framework applicable for different types of users in a specific domain, which could be applied to other domains.

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Advancements in protocols, computing paradigms, and electronics have enabled the development of wireless sensor networks (WSNs) with high potential for various location-based applications in different fields. One of the most important topics in WSNs is the localization in environments with sensor nodes being scattered randomly over a region. Localization techniques are often challenged by localization latency, efficient energy consumption, accuracy, environmental factors, and others. The objective of this study was to improve the technique for detecting the nearest Bluetooth Low Energy sensor, which would enable the development of more efficient mobile applications for location advertising at fairs, exhibitions, and museums. The technique proposed in this study was based on the iBeacon protocol, and it was tested in a controlled room with three environmental settings regarding the density of obstacles, as well as in a real-world setting at the Expo Museum at Postojna in Slovenia. The results of several independent measures, conducted in the controlled room and in the real-world environment, showed that the proposed algorithm outperformed the standard algorithm, especially in the environments with a medium or high densities of obstacles. The results of this study can be used for the more effective planning of placing beacons in space and for optimizing the algorithms for detecting transmitters in mobile location-based applications that provide users with contextual information based on their current location.

To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.