Biblio

Conversational Intelligent Tutoring Systems (CITS) in learning environments are capable of providing personalized instruction to students in different domains, to improve the learning process. This interaction between the Intelligent Tutoring System (ITS) and the user is carried out through dialogues in natural language. In this study, we use an open source framework called Rasa to adapt the original button-based user interface of an algebraic/arithmetic word problem-solving ITS to one based primarily on the use of natural language. We conducted an empirical study showing that once properly trained, our conversational agent was able to recognize the intent related to the content of the student’s message with an average accuracy above 0.95.

The range of text analysis methods in the field of natural language processing (NLP) has become more and more extensive thanks to the increasing computational resources of the 21st century. As a result, many deep learning-based solutions have been proposed for the purpose of authorship attribution, as they offer more flexibility and automated feature extraction compared to traditional statistical methods. A number of solutions have appeared for the attribution of English texts, however, the number of methods designed for Hungarian language is extremely small. Hungarian is a morphologically rich language, sentence formation is flexible and the alphabet is different from other languages. Furthermore, a language specific POS tagger, pretrained word embeddings, dependency parser, etc. are required. As a result, methods designed for other languages cannot be directly applied on Hungarian texts. In this paper, we review deep learning-based authorship attribution methods for English texts and offer techniques for the adaptation of these solutions to Hungarian language. As a part of the paper, we collected a new dataset consisting of Hungarian literary works of 15 authors. In addition, we extensively evaluate the implemented methods on the new dataset.

Intrusion Detection System (IDS) is used to identify malicious traffic on the network. Apart from rule-based IDS, machine learning and deep learning based on IDS are also being developed to improve the accuracy of IDS detection. In this study, the public dataset CIC IDS 2017 was used in developing deep learning-based IDS because this dataset contains the new types of attacks. In addition, this dataset also meets the criteria as an intrusion detection dataset. The dataset was split into train data, validation data and test data. We proposed Bidirectional Long-Short Term Memory (LSTM) for building neural network. We created 24 scenarios with various changes in training parameters which were trained for 100 epochs. The training parameters used as research variables are optimizer, activation function, and learning rate. As addition, Dropout layer and L2-regularizer were implemented on every scenario. The result shows that the model used Adam optimizer, Tanh activation function and a learning rate of 0.0001 produced the highest accuracy compared to other scenarios. The accuracy and F1 score reached 97.7264% and 97.7516%. The best model was trained again until 1000 iterations and the performance increased to 98.3448% in accuracy and 98.3793% in F1 score. The result exceeded several previous works on the same dataset.

At present, the limitation of intangible cultural experience lies in the lack of long-term immersive cultural experience for users. In order to solve this problem, this study divides the process from the perspective of Freudian psychology and combines the theoretical research on intangible cultural heritage and flow experience to get the preliminary research direction. Then, based on the existing interactive experience cases of intangible cultural heritage, a set of method model of immersive interactive experience of intangible cultural heritage based on flow theory is summarized through user interviews in this research. Finally, through data verification, the model is proved to be correct. In addition, this study offers some important insights into differences between primary users and experienced users, and proposed specific guiding suggestions for immersive interactive experience design of intangible cultural heritage based on flow theory in the future.

Conversational agents (CAs), often referred to as chatbots, are being widely deployed within existing commercial frameworks and online service websites. As society moves further into incorporating data rich systems, like the internet of things (IoT), into daily life, it is expected that conversational agents will take on an increasingly important role to help users manage these complex systems. In this, the concept of personality is becoming increasingly important, as we seek for more human-friendly ways to interact with these CAs. In this work a conceptual framework is proposed that considers how existing standard psychological and persona models could be mapped to different kinds of CA functionality outside of strictly dialogue. As CAs become more diverse in their abilities, and more integrated with different kinds of systems, it is important to consider how function can be impacted by the design of agent personality, whether intentionally designed or not. Based on this framework, derived archetype classes of CAs are presented as starting points that can hopefully aid designers, developers, and the curious, into thinking about how to work toward better CA personality development.

The drawbacks of traditional methods of cybernetic vulnerability detection relate to the required time to identify new threats, to register them in the Common Vulnerabilities and Exposures (CVE) records, and to score them with the Common Vulnerabilities Scoring System (CVSS). These problems can be mitigated by early vulnerability detection systems relying on social media and open-source data. This paper presents a model that aims to identify emerging cybernetic vulnerabilities in cybersecurity news articles, as part of a system for automatic detection of early cybernetic threats using Open Source Intelligence (OSINT). Three machine learning models were trained on a novel dataset of 1000 labeled news articles to create a strong baseline for classifying cybersecurity articles as relevant (i.e., introducing new security threats), or irrelevant: Support Vector Machines, a Multinomial Naïve Bayes classifier, and a finetuned BERT model. The BERT model obtained the best performance with a mean accuracy of 88.45% on the test dataset. Our experiments support the conclusion that Natural Language Processing (NLP) models are an appropriate choice for early vulnerability detection systems in order to extract relevant information from cybersecurity news articles.

Access to data at large scales expedites the progress of research in medical fields. Nevertheless, accessibility to patients' data faces significant challenges on regulatory, organizational and technical levels. In light of this, we present a novel approach based on the crowdsourcing paradigm to solve this data scarcity problem. Utilizing the infrastructure that blockchain provides, our decentralized platform enables researchers to solicit contributions to their well-defined research study from a large crowd of volunteers. Furthermore, to overcome the challenge of breach of privacy and mutual trust, we employed the cryptographic primitive of Zero-knowledge Argument of Knowledge (zk-SNARK). This not only allows participants to make contributions without exposing their privacy-sensitive health data, but also provides a means for a distributed network of users to verify the validity of the contributions in an efficient manner. Finally, since without an incentive mechanism in place, the crowdsourcing platform would be rendered ineffective, we incorporated smart contracts to ensure a fair reciprocal exchange of data for reward between patients and researchers.

Trust is an important characteristic of successful interactions between humans and agents in many scenarios. Self-driving scenarios are of particular relevance when discussing the issue of trust due to the high-risk nature of erroneous decisions being made. The present study aims to investigate decision-making and aspects of trust in a realistic driving scenario in which an autonomous agent provides guidance to humans. To this end, a simulated driving environment based on a college campus was developed and presented. An online and an in-person experiment were conducted to examine the impacts of mistakes made by the self-driving AI agent on participants’ decisions and trust. During the experiments, participants were asked to complete a series of driving tasks and make a sequence of decisions in a time-limited situation. Behavior analysis indicated a similar relative trend in the decisions across these two experiments. Survey results revealed that a mistake made by the self-driving AI agent at the beginning had a significant impact on participants’ trust. In addition, similar overall experience and feelings across the two experimental conditions were reported. The findings in this study add to our understanding of trust in human-robot interaction scenarios and provide valuable insights for future research work in the field of human-robot trust.

The paper discusses the use of virtual (VR) and augmented (AR) reality for visual analytics in information security. Paper answers two questions: “In which areas of information security visualization VR/AR can be useful?” and “What is the difference of the VR/AR from similar methods of visualization at the level of perception of information?”. The first answer is based on the investigation of information security areas and visualization models that can be used in VR/AR security visualization. The second answer is based on experiments that evaluate perception of visual components in VR.

The area of military operations is presently a peculiar, heterogenic environment providing the decision-makers with varied data and information on the potential or the real enemy. However the vast number and diversity of the available information does not facilitate the decision process. The achievement of information advantage in line with the rule: the first to notice, the first to understand and the first to act depends among other things on the proper information processing. In the theory of Electronic Warfare, the processing of information about the electronic objects of the enemy emitting electromagnetic energy is realized by Signals Intelligence. The fastest processing of information in the information system of Signals Intelligence is presently provided by cybernetic environment. The construction of an information processing system in the cybernetic environment of Signals Intelligence is thus a very complex task. The article presents theoretical basis of information processing in cybernetic environment of Signals Intelligence based on research carried out by the authors. The article can be described as the added value since it presents and clarifies a complex concept of cybernetic environment of Signal Intelligence. Moreover, it provides a new definition of information process as a system of operations on intelligence information and data. It also presents the stages of information process as well as the structure of information processing process. In the further part it shows the factors and elements of the cybernetic environment of Signals Intelligence isolated in the process of research. The document provides a perspective for the processing of information in the cybernetic environment of Signals Intelligence, it fills the gap in research on information processing in the cybernetic environment of Signals Intelligence as well as assures strong theoretical basis and provides an incentive for further research on the information processing in the cybernetic environment of Signals Intelligence.

The paper contains the results of perspective digital signatures algorithms based on hash functions analysis. Several aspects of their implementation are presented. The comparative analysis was carried out by the method of hierarchies. Some problems of implementation in the existing infrastructure are described. XMSS algorithm implementation with Ukrainian hash function national standard is presented.

Phishing emails have affected users seriously due to the enormous increasing in numbers and exquisite camouflage. Users spend much more effort on distinguishing the email properties, therefore current phishing email detection system demands more creativity and consideration in filtering for users. The proposed research tries to adopt creative computing in detecting phishing emails for users through a combination of computing techniques and social engineering concepts. In order to achieve the proposed target, the fraud type is summarised in social engineering criteria through literature review; a semantic web database is established to extract and store information; a fuzzy logic control algorithm is constructed to allocate email categories. The proposed approach will help users to distinguish the categories of emails, furthermore, to give advice based on different categories allocation. For the purpose of illustrating the approach, a case study will be presented to simulate a phishing email receiving scenario.

Cybernetic closed loop regulators are used to model socio-technical systems in adversarial contexts. Cybernetic principles regarding these idealized control loops are applied to show how the incompleteness of system models enables system exploitation. We consider abstractions as a case study of model incompleteness, and we characterize the ways that attackers and defenders interact in such a formalism. We end by arguing that the science of security is most like a military science, whose foundations are analytical and generative rather than normative.