Tom 10 (2020): Zeszyt 1 (December 2020)

The paper focuses of to open the minds of pupils and turn them to reading, teacher librarians must prepare pupils, bring them face to face with reading, situations, themes from each reading instance, so that they can analyze, describe, freely express themselves, using the right words to convey their thoughts, feelings, emotions, and compare the information they came across while reading with their own experiences, the situations they’ve lived through, thus anchoring them in daily reality.

Mass digitisation of the collections held by cultural institutions -galleries, libraries, museums, archives, have made available a huge amount of historical, cultural, informational resources in digital format, which are more and more used in educational activities. This paper describes some innovative non- formal educational activities developed around the Europeana1914-1918 archive between 2014-2018, where searching in big digital archives, gaming and improving the digital skills were key aspects.

The Corona period created great challenges in the teaching and learning process. This presentation describes a research based on studying the subject of big data using virtual escape rooms. Due to the period it was decided that the final task in the Big Data course would be to build a virtual escape room that deals with a topic from the world of Big Data. The aim of the study was to examine how the understanding of various topics related to Big Data is sharpened when they are taught through virtual escape rooms. Each group of students chose a topic from the world of big data and built a virtual escape room around it. Peer learning was then performed, and each group edited rooms of other groups, so that each group learned about a variety of topics related to the big data world. By monitoring peer learning it was possible to assess the nature of the learning process experienced by the group. This unique learning allows for flexibility in terms of time and place in holding meetings. The interactions between the various group members contribute to enrichment the knowledge and the development of creative ideas.

According to the results of the study, this type of learning sharpened different skills among the participants - social, communicative and thinking. It also developed a deep understanding when the escape room practically demonstrates the topic of big data in an optimal way through a variety of representations. During the activity in the escape room there is created an interesting and original connection between the materials and the ideas, open to a variety of interpretations. Digital tools are used effectively and creatively that contribute to understanding the message (Kemp, 2018).

In this paper, we present a wide range of models based on less adaptive and adaptive approaches for a PoS tagging system. These parameters for the adaptive approach are based on the n-gram of the Hidden Markov Model, evaluated for bigram and trigram, and based on three different types of decoding method, in this case forward, backward, and bidirectional. We used the Brown Corpus for the training and the testing phase. The bidirectional trigram model almost reaches state of the art accuracy but is disadvantaged by the decoding speed time while the backward trigram reaches almost the same results with a way better decoding speed time. By these results, we can conclude that the decoding procedure it’s way better when it evaluates the sentence from the last word to the first word and although the backward trigram model is very good, we still recommend the bidirectional trigram model when we want good precision on real data.

We used an imperialist competitive algorithm to determine the parameters of a fuzzy controller of type Sugeno that would ensure a good unit step response of a second-order single-input and single-output automatic system.

In the 1950s, Hagelbarger’s Sequence Extrapolating Robot (SEER) and Shannon’s Mind-Reading Machine (MRM) were the state-of-the-art research results in playing the well-known “matching pennies” game. In our research we perform a software implementation for both machines in order to test the common statement that MRM, even simpler, beats SEER. Also, we propose a simple contextual predictor (SCP) and use it to compete with SEER and MRM. As expected, experimental results proves the claimed MRM superiority over SEER and even the SCP’s superiority over both SEER and MRM. At the end, we draw some conclusions and propose further research ideas, like the use of mixing models methods and the use of Hidden Markov Model for modelling player’s behaviour.

The extensive implementation of Internet of Things (IoT) solutions and its wide popularity to the public over the past decade enabled emergent applications that provide sophisticated, proactive health care solutions that can improve the quality of life of individuals. This work proposes an IoT architecture and implements a prototype solution that allows its users to improve their physical activity by collecting vital signs using wearables and other environmental and habitual information in order to monitor their activity and propose behaviors in a smart way that will allow them to achieve their preset goals. The focus is on the increased usability of the system employing refined solutions like voice recognition and smart visualization to enable its seamless use while offering an interoperable architecture that will enhance its flexibility. The prototype implementation offers a proof of concept evaluation of the proposed system, applying state of the art technologies and using existing hardware and popular gadgets.

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This paper presents a forecasting method of the electricity consumption and production in a household equipped with photovoltaic panels and a smart energy management system. The prediction is performed with a Long Short-Term Memory recurrent neural network. The datasets collected during five months in a household are used for the evaluations. The recurrent neural network is configured optimally to reduce the forecasting errors. The results show that the proposed method outperforms an earlier developed Multi-Layer Perceptron, as well as the Autoregressive Integrated Moving Average statistical forecasting algorithm.

During the previous years, the vehicle manufacturers have tried to equip their vehicles with as much technology as possible, making the driving experience for people easier than ever. Most of the modern vehicles come today with ADAS (Advanced Driver Assistance Systems) either for driving (E.g. Cruise Control, Blind Spot Warning) or Parking (E.g. Rear Ultrasonic Sensors, Rear View Camera). Since the vehicle come equipped with more technology, a major task in developing vehicle remains the integration of these ADAS system in the vehicle context with the other components. Since most of the components cope with each other on the vehicle level, some technologies are more affected by other components – such as the case of an ultrasound vehicle scanning system (Blind Spot Warning) and the Exhaust line that emits ultrasounds from the exhaust muffler. The aim of this paper is to study the influence of the exhaust line ultrasounds (ultrasounds that are emitted by the engine cycle and filtered in the exhaust line of the vehicle) over the detection performance of the Blind Spot Warning Ultrasound system. Since vehicles are sold with a wide variety of powertrains, the solution presented took into account also these differences between powertrains equipped. In order to test the solution, mock-ups of the vehicle were made in order to proof the robustness of the method.