In the paper, we have analysed curricula of the several subjects in lower grades of the primary school in Republika Srpska. Blum's taxonomy was used in the analysis (cognitive and psycho-motor area) and SMART system of evaluating learning outcomes. There have been three subject areas included (the world around us, speech, expression, crating and rhythm, sport and music), in the first grade ten subjects (Serbian, Mathematics, Natural and Social Sciences, etc.), and pedagogical work with the class, which are being studied from the second to the fifth class. The aim of the analysis was to determine knowledge and skills which students should gain in the lower grades of the primary school. We have achieved this aim by determination of the outcomes adequacy based on the above stated criteria, levels of knowledge and skills, which are required, as well as estimation of how much each teaching unit requires certain level of students' knowledge and whether there is the trend of more requirements when passing one grade and entering the other. The results of the analysis show that students are required the lowest level of understanding and gaining (on average 60% of outcomes are on this level), and higher levels require only application (about 15%). This relation varies, depending on the subject. When we talk about skills, they are at the level of precision, and this is sufficient for this age. The rising requirements for students, through learning outcomes have not been observed, because from grade to grade the outcomes are on the same level. This means that we do not work sufficiently at the quality improvement, but on quantity improvement of knowledge.

Processes in product development are becoming more and more complex. A multitude of engineering disciplines are involved in the development of new products. New lean and agile methods arise. Pulse methodology is a lean deviation management methodology introduced by Scania in 2003, and since then many Swedish companies adapted it. It is based on synchronizing the company by frequently having short meetings (aka. pulse meetings) and visualizing the deviations using traffic coded magnets on whiteboards (aka. pulse boards). Even though the whiteboards lack providing efficient communication between distributed teams, still the users appreciate the simplicity of them. In this research, we presented a new methodology called hybrid pulse methodology, which solves the communication issues exist in the baseline methodology. We tested the methodology at the workshops we held in the companies, in simulated global meeting settings, using the demonstrator we developed for the methodology. This research contributes to the lean literature with a new methodology that ensures the synchronization of global organizations by providing efficient communication between distributed teams without damaging the baseline methodology.

Existing methods for security risk analysis typically estimate time, cost, or likelihood of success of attack steps. When the threat environment changes, such values have to be updated as well. However, the estimated values reflect both system properties and attacker properties: the time required for an attack step depends on attacker skill as well as the strength of a particular system component. In the TRESPASS project, we propose the separation of attacker and system properties. By doing so, we enable “plug-and-play” attacker profiles: profiles of adversaries that are independent of system properties, and thus can be reused in the same or different organisation to compare risk in case of different attacker profiles. We demonstrate its application in the framework of attack trees, as well as our new concept of attack navigators.

An important part of human intelligence is the ability to use language. Humans learn how to use language in a society of language users, which is probably the most effective way to learn a language from the ground up. Principles that might allow an artificial agents to learn language this way are not known at present. Here we present a framework which begins to address this challenge. Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori, and only high-level information about the format of the human interaction in the form of high-level goals of the interviewer and interviewee and a small ontology. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora.

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes.