This paper presents a new approach to teaching programming to undergraduate computer science students. A dedicated Arduino board along with custom application programming interface (API) was introduced into programming classes with a view to strengthen students’ engagement and improving the attractiveness of the course. The students were presented with basic functionalities of the board, which gave them a possibility to accomplish their own projects—typically video games—without any background in electronics. The level of engagement of the participants was observed by the tutors during classes and also reviewed based on questionnaires filled by 347 first‐, second‐, and third‐year undergraduates. The results indicated that the proposed approach was well received by nearly 80%, while nearly 75% of the participants expressed a wish to continue their Computer Science education using Arduino.

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Abstract-Proper break taking during office work iss necessary to prevent musculoskeletal disorders and reduce the risk of heart disease. We present APOEW -an avatar for preventing continuous office work without taking breaks. APOEW is a system that uses a personalized robot avatar to encourage proper break behaviour during office work. The avatar signals the need for a break by stooping. The system was designed to be unobtrusive and blend well with the office environment. The avatars are customisable in order to enable users to design their work environment freely. We conducted a user study where we observed developers working in front of their computers next to the avatar. Preliminary results indicate it has no negative impact on the work environment and users are intrigued by the system. Moreover, a survey on attitude to our concept reveals interesting and positive feedback that will help to develop an APOEW system further.

The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prior knowledge with learning from examples, thus allowing sensor devices to be used for the successful execution of machine learning even when the volume of training data is highly limited, using compact underlying hardware. The proposed architecture comprises two interacting functional modules arranged in a homogeneous, multiple-layer architecture. The first module, referred to as the knowledge sub-network, implements knowledge in the Conjunctive Normal Form through a three-layer structure composed of novel types of learnable units, called L-neurons. In contrast, the second module is a fully-connected conventional three-layer, feed-forward neural network, and it is referred to as a conventional neural sub-network. We show that the proposed hybrid structure successfully combines knowledge and learning, providing high recognition performance even for very limited training datasets, while also benefiting from an abundance of data, as it occurs for purely neural structures. In addition, since the proposed L-neurons can learn (through classical backpropagation), we show that the architecture is also capable of repairing its knowledge.