Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

Visual analysis of sensor logs in smart spaces: Activities vs. situations

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS

Measurements, Models, Systems and Design

531 s.

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

Regression between headmaster leadership, task load and job satisfaction of special education integration program teacher

Managing school is a daunting task for a headmaster. This responsibility is exacerbated when it involves the Special Education Integration Program (SEIP). This situation requires appropriate and effective leadership in addressing some of the issues that are currently taking place at SEIP such as task load and job satisfaction. This study aimed to identify the influence of headmaster leadership on task load and teacher job satisfaction at SEIP. This quantitative study was conducted by distributing 400 sets of randomized questionnaires to SEIP teachers across Malaysia through google form. The data obtained were then analyzed using Structural Equation Modeling (SEM) and AMOS software. The results show that there is a significant positive effect on the leadership of the headmaster and the task load of the teacher. Likewise, the construct of task load and teacher job satisfaction has a significant positive effect. However, for the construct of headmaster leadership and teacher job satisfaction, there was no significant positive relationship. This finding is very important as a reference to the school administration re-evaluating their leadership so as not to burden SEIP teachers and to give them job satisfaction. In addition, the findings of this study can also serve as a guide for SEIP teachers to increase awareness of the importance of managing their tasks. This study also focused on education leadership in general and more specifically on special education leadership