Inspired by the recent success of deep learning (DL) approaches in computer vision domain, this Letter proposes a framework to encode the sensor data into an image representation for the activity recognition task. The signal from sensors is encoded based on the Gramian Angular Field. The encoding technique increases the dimension of the data, captures a local temporal relationship in terms of temporal correlation between time intervals on the geometric interpretation, and can be easily applied to the pre‐trained DL architecture. The proposed framework is examined with respect to six popular sensor‐based activity recognition datasets. Using the authors’ framework, the results show that their approach outperforms most of the state‐of‐the‐art approaches.

The concept of connected things by involving emotional aspects has been raised as a new research issue which is known as "emotional IoT". The deeper interaction between object and human shows an importance to develop a system with either cognitive or affective capabilities such as emotion. While the existing works on real time emotion recognition mostly rely on facial data, there are a few works dealing with real time emotion recognition based on physiological data using pervasive devices. In this work, we propose a framework to recognize emotion based on human physiological signals using the pervasive wearable device. This framework opposed most of the works which employed sensors which are expensive and complex in arrangement. The challenge on using pervasive devices is the low accuracy due to the low sampling rate. The approach is implemented in an end-to-end soft real time emotion recognition system using smartphone and smartwatch devices. The performance of our system was evaluated under a common environment and proved the system applicability throughout everyday life.

In this study, the authors conducted research on the topic of decision support systems in determining the best teacher. The case study used is at SMK Negeri 1 Lima Puluh Batubara Regency, North Sumatra Province. In practice, the selection of the best teachers and achievers in these schools has not been carried out objectively or in other words, the selection of the best teachers is still carried out in a subjective way where the validation in its determination is not known for certain openness. So to overcome this problem, the authors propose to apply the calculation of the Decision Support System (DSS) in solving these problems. The method used in this study is the MOORA method and uses the criteria weighting method with Rank Order Centroid. The criteria used are five criteria consisting of Teaching Method (K1), Motivation and Innovation (K2), Responsibility (K3), Problem Solving (K4), and Insight and Creativity (K5). Then the alternative data samples used were as many as ten data from SMK Negeri 1 Lima Puluh. The test results obtained were Iswanto (A3) obtained the highest final preference value of 0.4182 and could be declared the best teacher with the first rank. Then it can be seen that the MOORA and Rank Order Centroid methods are able to provide recommendations for the best teacher decisions at SMK Negeri 1 Lima Puluh using objective calculations.