Automatic speech recognition (ASR) is an effective technique that can convert human speech into text format or computer actions. ASR systems are widely used in smart appliances, smart homes, and biometric systems. Signal processing and machine learning techniques are incorporated to recognize speech. However, traditional systems have low performance due to a noisy environment. In addition to this, accents and local differences negatively affect the ASR system’s performance while analyzing speech signals. A precise speech recognition system was developed to improve the system performance to overcome these issues. This paper uses speech information from jim-schwoebel voice datasets processed by Mel-frequency cepstral coefficients (MFCCs). The MFCC algorithm extracts the valuable features that are used to recognize speech. Here, a sparse auto-encoder (SAE) neural network is used to classify the model, and the hidden Markov model (HMM) is used to decide on the speech recognition. The network performance is optimized by applying the Harris Hawks optimization (HHO) algorithm to fine-tune the network parameter. The fine-tuned network can effectively recognize speech in a noisy environment.

Instructions written in human-language cause no perception problems for humans, but become a challenge when translating them into robot executable format. This complex translation process covers different phases, including instruction completion by adding obligatory information that is not explicitly given in humanoriented instructions. Robot action ontology is a common source of such additional information, and it is normally structured around a limited number of verbs, denoting robot specific action categories, each of them characterized by a certain action environment. Semi-manual action ontology building procedures are normally based on domain-specific human-language text mining, and one of the problems to be solved is the assignment of action categories for the obtained verbs. Verbs in English language are very polysemous, therefore action category, referring to different robot capabilities, can be determined only after comprehensive analysis of the verb's context. The task we solve is formulated as the text classification task, where action categories are treated as classes, and appropriate verb contextas classification instances. Since all classes are clearly defined, supervised machine learning paradigm is the best selection to tackle this problem. We experimentally investigated different context window widths; directions (context on the right, left, both sides of analyzed verb); and feature types (symbolic, lexical, morphological, aggregated). All statements were proved after exploration of two different datasets. The fact that all obtained results are above random and majority baselines allow us to claim that the proposed method can be used for predicting action categories. The best obtained results were achieved with Support Vector Machine method using window width of only 25 symbols on the right and bag-of-words as features. This exceeded random and majority baselines by more than 37% reaching 60% of accuracy.

The paper analyses the problems in selecting and integrating tools for delivering basic programming knowledge at the university level. Discussion and analysis of teaching the programming disciplines, the main principles of study programme design, requirements for teaching tools, methods and corresponding languages is presented, based on literature overview and author's experience. A pressure from labor market, students and other sources to emphasize practical skills over deeper, long-term programming concepts is described. A model of teaching introductory programming disciplines at a higher logical level, using C#, is presented as a summary of the accomplished analysis, and also taking into account the recommendations of the ACM (Association for Computing Machinery) association for typical teaching programs. Also, design principles for building introductory programming courses, aligned with such teaching approach, are presented. This model has already been trialed at Vytautas Magnus University.

Human beings can generalize from one action to similar ones. Robots cannot do this and progress concerning information transfer between robotic actions is slow. We have designed a system that performs action generalization for manipulation actions in different scenarios. It relies on an action representation for which we perform code-snippet replacement, combining information from different actions to form new ones. The system interprets human instructions via a parser using simplified language. It uses action and object names to index action data tables (ADTs), where execution-relevant information is stored. We have created an ADT database from three different sources (KUKA LWR, UR5, and simulation) and show how a new ADT is generated by cutting and recombining data from existing ADTs. To achieve this, a small set of action templates is used. After parsing a new instruction, index-based searching finds similar ADTs in the database. Then the action template of the new action is matched against the information in the similar ADTs. Code snippets are extracted and ranked according to matching quality. The new ADT is created by concatenating code snippets from best matches. For execution, only coordinate transforms are needed to account for the poses of the objects in the new scene. The system was evaluated, without additional error correction, using 45 unknown objects in 81 new action executions, with 80% success. We then extended the method including more detailed shape information, which further reduced errors. This demonstrates that cut & recombine is a viable approach for action generalization in service robotic applications.

Cognitive Radio technology is commonly seen as a promise to form the basis of the next largest breakthrough in the development of ubiquitous wireless broadband services. However, the disruptive nature and complexity of this technology raises a host of associated issues, including the open question on reasons for the slow progression of the innovation. This paper reviews different streams of literature on technology evolution to suggest that CR development can be best conceptualized as anticipatory (pre-)standardizing process at an early phase. At the backdrop of the absent obvious leader in CR development, the authors conduct a co-evolutionary analysis of the CR innovation context to reveal a stakeholders’ domain, which is best positioned to lead the further CR development. Finally, the authors conclude that the regulatory domain oversees some of the most crucial enabling factors that may decide the future of CR technology.