The paper presents the method of assessment of learning outcomes acquirement by students. The analysis is based on the results of the final matriculation exam in mathematics. For crisp and both types of fuzzy relations, cut scores (passing scores) can be defined along with the method of preparing rankings of students. The advantage of applying type 2 fuzzy relations is the lack of the necessity for experts to agree to one level (one number) of verification of learning outcomes by items created for the examination. Based on the results of the exam and experts’ knowledge, the decision support system for calculating the levels of learning outcomes acquirement, making decisions about passing the examination and preparing rankings of students, can be developed. Additionally, the rank reversal phenomenon does not burden the proposed method.
Compared with the robots, humans can learn to perform various contact tasks in unstructured environments by modulating arm impedance characteristics. In this article, we consider endowing this compliant ability to the industrial robots to effectively learn to perform repetitive force-sensitive tasks. Current learning impedance control methods usually suffer from inefficiency. This paper establishes an efficient variable impedance control method. To improve the learning efficiency, we employ the probabilistic Gaussian process model as the transition dynamics of the system for internal simulation, permitting long-term inference and planning in a Bayesian manner. Then, the optimal impedance regulation strategy is searched using a model-based reinforcement learning algorithm. The effectiveness and efficiency of the proposed method are verified through force control tasks using a 6-DoFs Reinovo industrial manipulator.
Affective computing studies and develops systems capable of detecting humans affects. The search for universal well-performing features for speech-based emotion recognition is ongoing. In this paper, a small set of features with support vector machines as the classifier is evaluated on Surrey Audio-Visual Expressed Emotion database, Berlin Database of Emotional Speech, Polish Emotional Speech database and Serbian emotional speech database. It is shown that a set of 87 features can offer results on-par with state-of-the-art, yielding 80.21, 88.6, 75.42 and 93.41% average emotion recognition rate, respectively. In addition, an experiment is conducted to explore the significance of gender in emotion recognition using random forests. Two models, trained on the first and second database, respectively, and four speakers were used to determine the effects. It is seen that the feature set used in this work performs well for both male and female speakers, yielding approximately 27% average emotion recognition in both models. In addition, the emotions for female speakers were recognized 18% of the time in the first model and 29% in the second. A similar effect is seen with male speakers: the first model yields 36%, the second 28% a verage emotion recognition rate. This illustrates the relationship between the constitution of training data and emotion recognition accuracy.
Information Technologies (IT) are most and most important factor in economical and social development of particular countries and of the whole world, therefore we often think and told about so called Information Society (IS) as a new form of socio-economical organization of the society. Most properties of IT are profitable for the people and most features of IS are positive. Nevertheless we can find also some problems arising because of too fast development of IT and some dangers connected with increasing dependability of present society on IT devices and services. In the paper selected problems connected with distance teaching and distance learning (so called elearning) are pointed out and considered. As a most important problem so called "information smog" is pointed. It is very troublesome at present and may be source of big problem in the future.
Equilibrium, disequilibrium and adaptation. The inspirations for spatial economics. This paper is a part of author’s long-term research project related to dynamics and evolution of space economy. In the attempts of theoretical reconstruction of these processes the notion of equilibrium plays an important role, as well as related notions: disequilibrium and adaptation. In the analysis of equilibrium the author drew on the concepts elaborated by the neoclassical school of economics. In the analysis of disequilibrium the concept of physics turned out to be fertilizing, namely the concept of dissipative structures and self-organisation. The concept of adaptation is elaborated in depth in biology. These three concepts have been applied in spatial economics long since. Further research is necessary however, to make these application more relevant to spatial economics, and in this way more fruitful.
Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.
A simple analog circuit is presented which can play a neuron role in static-model-based neural networks implemented in the form of an integrated circuit. Operating in a transresistance mode it is suited to cooperate with transconductance synapses. As a result, its input signal is a current which is a sum of currents coming from the synapses. Summation of the currents is realized in a node at the neuron input. The circuit has two outputs and provides a step function signal at one output and a linear function one at the other. Activation threshold of the step output can be conveniently controlled by means of a voltage. Having two outputs, the neuron is attractive to be used in networks taking advantage of fuzzy logic. It is built of only five MOS transistors, can operate with very low supply voltages, consumes a very low power when processing the input signals, and no power in the absence of input signals. Simulation as well as experimental results are shown to be in a good agreement with theoretical predictions. The presented results concern a 0.35 1m CMOS process and a prototype fabricated in the framework of Europractice.
A variety of algorithms allows gesture recognition in video sequences. Alleviating the need for interpreters is of interest to hearing impaired people, since it allows a great degree of self-sufficiency in communicating their intent to the non-sign language speakers without the need for interpreters. State-of-theart in currently used algorithms in this domain is capable of either real-time recognition of sign language in low resolution videos or non-real-time recognition in high-resolution videos. This paper proposes a novel approach to real-time recognition of fingerspelling alphabet letters of American Sign Language (ASL) in ultra-high-resolution (UHD) video sequences. The proposed approach is based on adaptive Laplacian of Gaussian (LoG) filtering with local extrema detection using Features from Accelerated Segment Test (FAST) algorithm classified by a Convolutional Neural Network (CNN). The recognition rate of our algorithm was verified on real-life data.
The ideas of pluralism, their various theoretical developments and ideological concretizations, as well as their promotion and the attempts at implementing them in social practice, constitute a current signum temporis. Pedagogical reflection seems to be particularly sensitive to the issue of pluralism, to its understanding and practising, to multidimensional references of pluralism to the world of values. This especially concerns the values and conflicts of values which are close to various forms of educational activity. What is considered – more or less critically – in pedagogical reflection are different aspects and consequences of the idea of pluralism concerning the currently existing ideas. Simultaneously, the multitude of the ideas of pluralism is taken into account – the ideas which refer to the broadly treated sphere of pedagogical activities and institutions. Pedagogical reflection also considers the threats which co-occur with pluralism or are aimed against it and which are carried by pluralism itself, e.g. in the sphere of education. An expert in the contemporary pedagogical thought and practice, Bogusław Śliwerski, asks: “Will we manage to save the world of pedagogical thought, the pedagogy open to difference, to pluralism (not to be mistaken for another illness which is relativism)?”. By confronting pluralistic perspectives of pedagogy with current ideological and social challenges, he makes this question one of the leading issues in pedagogical and metapedagogical studies. What seems to be heard in this question as well is the appeal to save the world of pedagogical thought as an open world characterized by pluralism, doing this through honest reasoning conducted from different standpoints and perspectives. The assumption of this question comprises the axiologically consolidated belief that it is worth “to save the world of pedagogical thought, the pedagogy open to pluralism”. This is also an inspiration to undertake the (presented in this text) thought concerning the pluralistic perspectives of pedagogy and various faces of pluralisms in the critical recognition of metapedagogical reflection in the case of the Polish pedagogical thought after 1989.
The article is devoted to the problem of voice signals recognition means introduction in the system of distance learning. The results of the conducted research determine the prospects of neural network means of phoneme recognition. It is also shown that the main difficulties of creation of the neural network model, intended for recognition of phonemes in the system of distance learning, are connected with the uncertain duration of a phoneme-like element. Due to this reason for recognition of phonemes, it is impossible to use the most effective type of neural network model on the basis of a multilayered perceptron, at which the number of input parameters is a fixed value. To mitigate this shortcoming, the procedure, allowing to transform the non-stationary digitized voice signal to the fixed quantity of mel-cepstral coefficients, which are the basis for calculation of input parameters of the neural network model, is developed. In contrast to the known ones, the possibility of linear scaling of phoneme-like elements is available in the procedure. The number of computer experiments confirmed expediency of the fact that the use of the offered coding procedure of input parameters provides the acceptable accuracy of neural network recognition of phonemes under near-natural conditions of the distance learning system. Moreover, the prospects of further research in the field of development of neural network means of phoneme recognition of a voice signal in the system of distance learning is connected with an increase in admissible noise level. Besides, the adaptation of the offered procedure to various natural languages, as well as to other applied tasks, for instance, a problem of biometric authentication in the banking sector, is also of great interest.
Video walls are useful to display large size video content. Empowered video walls combine display functionality with computing power. Such video walls can display large scientific visualizations. If they can also display high-resolution video streamed over a network, they could enable distance collaboration over scientific data. We proposed several methods of network streaming of highresolution video content to a major type of empowered video walls, which is the SAGE2 system. For all methods, we evaluated their performance and discussed their scalability and properties. The results should be applicable to other web-based empowered video walls as well.
Similarity assessment between 3D models is an important problem in many fields including medicine, biology and industry. As there is no direct method to compare 3D geometries, different model representations (shape signatures) are developed to enable shape description, indexing and clustering. Even though some of those descriptors proved to achieve high classification precision, their application is often limited. In this work, a different approach to similarity assessment of 3D CAD models was presented. Instead of focusing on one specific shape signature, 45 easy-to-extract shape signatures were considered simultaneously. The vector of those features constituted an input for 3 machine learning algorithms: the random forest classifier, the support vector classifier and the fully connected neural network. The usefulness of the proposed approach was evaluated with a dataset consisting of over 1600 CAD models belonging to 9 separate classes. Different values of hyperparameters, as well as neural network configurations, were considered. Retrieval accuracy exceeding 99% was achieved on the test dataset.
In education, information and Communications Technologies mostly play the role of a medium of communication, as well as a means of imparting knowledge. ICT, however, is used less as a subject for student activity, i.e. a subject for students to learn, where they can operate the technology, as in robotics or mechantronics. Information technologies are also very rarely implemented in education as a way for students to build their identity and shape their attitudes towards their outside and inside worlds. In spite of this, in the history of educational technology there have been a number of researchers and educators who have promoted interesting ideas for implementing technologies as tools for human cognitive, affective, psychomotor and moral empowerment. Today such people are also present in education, however, they play unimportant roles on the periphery of formal education. This paper is a reminder of a number of ideas by theorists and researchers concerning the implementation of ICT, but mainly highlights the empowerment it gives students and its humanizing/humanitarian role.
The presented material is a concise report of the research on the ‘condition’ of initial teacher education provided at universities in Poland in accordance with ‘new’ Ministry standards of 17 January 2012. In the analysis of data collected from 30 universities in May 2015, we focused on models of the organisation of this part of teaching at universities, the ways of constructing the professional curricula, the role and place of practice in the learning processes and the strategy of assessment of the preparation for teaching. Our research result is not quite optimistic. Under the ‘new label’ of standards we still have quasi-traditional approach to initial teacher education. In acquiring the new professional competencies students do not get real support from their academic and school partners. They are not very interesting in building opportunities for transforming learning aiming at transforming teaching
There were two aims of the research. One was to enable more or less automatic confirmation of the known associations – either quantitative or qualitative – between technological data and selected properties of concrete materials. Even more important is the second aim – demonstration of expected possibility of automatic identification of new such relationships, not yet recognized by civil engineers. The relationships are to be obtained by methods of Artificial Intelligence, (AI), and are to be based on actual results from experiments on concrete materials. The reason of applying the AI tools is that in Civil Engineering the real data are typically non perfect, complex, fuzzy, often with missing details, which means that their analysis in a traditional way, by building empirical models, is hardly possible or at least can not be done quickly. The main idea of the proposed approach was to combine application of different AI methods in a one system, aimed at estimation, prediction, design and/or optimization of composite materials. The paradigm of the approach is that the unknown rules concerning the properties of concrete are hidden in experimental results and can be obtained from the analysis of examples. Different AI techniques like artificial neural networks, machine learning and certain techniques related to statistics were applied. The data for the analysis originated from direct observations and from reports and publications on concrete technology. Among others it has been demonstrated that by combining different AI methods it is possible to improve the quality of the data, (e.g. when encountering outliers and missing values or in clustering problems), so that the whole data processing system will be giving better prediction, (when applying ANNs), or the newly discovered rules will be more effective, (e.g. with descriptions more complete and – at the same time – possibly more consistent, in case of ML algorithms).
The genesis of both coherent structures and reactive flow control strategies is explored. Futuristic control systems that utilize mi-crosensors and microactuators together with artificial intelligence to target specific coherent structures in a transitional or turbulent flow are considered. Of possible interest to the readers of this journal is the concept of smart wings, to be briefly discussed early in the article.