Reviewerly/exHarmony huggingface dataset:
Best Computer System Attack Strategy Finding Method

Solution of a number of tasks in the field of computer systems security analysis requires building the most optimal strategy of intruder's actions: analysis of system security, a detailed risk assessment of the operated or designed system, solving the problem of optimal placement of information protection tools, etc. Building an optimal strategy often involves creation of attack trees or attack graphs. After building such objects one can search for the optimal path from the intruder's starting position to the target system malicious event (e.g., disabling the target component). This can be achieved for example by classical optimization problems solutions on graphs. However, when considering real systems, it is necessary to take into account the presence of multiple intruder starting positions that represent, for example, vulnerable system components accessible from external networks. Moreover, usually intruder needs to disable several system components in order to damage the system as a whole. This paper presents a description of the attack impact graph, for which the problem of finding an optimal attack strategy is solved, as well as an method for finding the optimal strategy of an intruder in a computer system modelled by an attack graph. Presented method accepts possibility of multiple initial attack vectors and a set of components (target events) that must be affected by the attack to cause damage to the system.
Analysis of the LSTM-AE OCSVM Method for Finding Anomalies in Multivariate Time Series

The problem of anomaly detection is one of the most pressing tasks of data mining and is actively studied. Anomaly detection is applied in areas such as medical diagnostics, network traffic analysis, text and image processing and recognition, as security and information protection, industrial monitoring, risk management. The obvious approach to solving the anomaly detection problem is as follows: we need to define a domain corresponding to normal behavior. Then any observation lying in this area will be considered normal, and outside the area - abnormal. In this paper, we will consider an anomaly search method based on deep learning without a teacher, where no marked-up data is used. A machine learning algorithm will be paired with deep learning to classify the data. Threshold will also be used in the comparison of classification results. The method will be examined on a dataset of a simulated gasoil heating loop process, which is a multivariate time series.
Benchmarking Photonic Quantum Machine Learning Simulators

In the past few years, quantum computing has gotten more attention, and the need for efficient simulations is getting increasingly important as well. A significant branch of quantum computing is photonic quantum computing. For simulating photonic quantum circuits, Strawberry Fields is the most popular framework. In this paper, we compared it with another framework currently under development called Piquasso regarding gradient calculation time, which is an essential part of continuous-variable quantum neural networks. We present the apparent scalability of Piquasso over Strawberry Fields by storing fewer data, but leading to possible accuracy differences as a trade-off, which could motivate future work.
Relative density clouds: Visualizing and exploring multivariate patterns of group differences

This paper introduces relative density clouds, a simple but powerful method to visualize the relative density of two groups in multivariate space. Relative density clouds employ k-nearest neighbor density estimates to provide information about group differences throughout the entire distribution of the variables. The method can also be used to decompose overall group differences into the specific contributions of differences in location, scale, and covariation. Existing relative distribution methods offer a flexible toolkit for the analysis of univariate differences; relative density clouds bring some of the same advantages to fruition in the context of multivariate research. They can assist in the exploration of complex patterns of group differences, and help break them down into simpler, more interpretable effects. An easy-to-use R function is provided to make this visualization method widely accessible to researchers.
Automated Moderation Helper System Using Artificial Intelligence Based Text Classification and Recommender System Techniques

In this paper we introduce a novel approach for text message moderation assistance, where the system can Configure itself with negatively or positively labeled sample messages. The goal of the system is to assist moderators in moderating text messages, by providing them with recommendations about which messages should be reviewed manually. The recommendations should be based on user-defined rules, as well as machine-learned rules, derived from previously accepted or rejected messages. The proposed method is able to filter messages in Hungarian or English. It is also able to learn from user feedback, in order to better understand the preferences of users.
Addressing the Issue of Attacks, Testing, and Information Leakage Channels in Quantum Communication Networks

The article is devoted to identifying algorithms for unauthorized actions of an illegitimate user on optoelectronic components of ground and space communication stations, in cases where an attacking entity pre-scans the protected perimeter in search of critical vulnerabilities. Under scanning, the authors accept the technical process of extracting the most useful knowledge, both about the system and the data circulating in it. The methodology outlined in the study is aimed at identifying an attacker acting on two main quantum states of particles - Glauber states and incoherent ones. The most vulnerable component in both cases is the avalanche photodetector, which corresponds to the attack - “blindness”. Detection of an illegitimate user is possible due to a deep analysis of time slots, mainly in manual mode, automatic detection cannot be implemented today due to its low development. The research materials contain a descriptive part of four scenarios where an attacker has unlimited computing power in the form of software and hardware. At the same time, the order of the attacker's goals is defined as: scanning the system, detecting vulnerabilities, implementing security threats, penetrating the network and stealing data.
Creation of a Unified University Blockchain for the Purpose of Storing the University’s Teaching Mate rials

The use of blockchain technology gives universities the opportunity to create their own blockchain. By rethinking the Bologna program applied in education, in the future, universities will be able to work together even more closely in the field of joint curriculum development. It can be observed that different universities teach the same subjects. As a result, university lecturers have even more burdens outside of teaching, as lecturers must prepare the same teaching materials for each university. The aim of the research is to create a blockchain in practice, as well as to present it in detail, where university lecturers could upload uniformly prepared teaching materials. In the long run, a unified and universally accepted university curriculum will certainly bring many advantages. If the university also wants to upload paid course content to the blockchain, a smart contract must be used. With the help of a smart contract, payments for teaching materials would be carried out in an automated manner, so additional burdens could be taken off the shoulders of universities. Since the blocks in the blockchain are closely built on top of each other, it is difficult to modify them with the educational materials stored in them. Since universities must provide students with up-to-date knowledge, the teaching materials must also be up to date. The modification of teaching materials at specified intervals could be solved using the Soft-Fork blockchain process.
Comparative Quality Analysis of Random Number Generators in MATLAB and NumPy packages

The article presents the results of a comparative analysis of randomness testing using statistical tests in the Information Technology Laboratory of the National Institute of Standards and Technology (STS NIST) and Move-To-Front test in software implementations of floating-point random number generators and random integer numbers of the MATLAB package and the NumPy Python library. It was found thatin MATLAB package passing the randomness of Move-To-Front test of a sequence which was made up of random integer numbers calculated on the basis of the conversion from floating-point random number generators to random integer numbers depends on the sequence length and the size of the alphabet used to convert from floating-point random number generators to random integer numbers. According to the authors, as this effect is not detected in the NumPy Python library, the result obtained in the workindicates the presence of an internal defect in the algorithm for generating floating-point random number generators MATLAB.
Exploiting potentialities for space-based quantum communication network: downlink quantum key distribution modelling and scheduling analysis

Abstract The goal of the space-based quantum network is to form the backbone of the quantum internet for long-distance secure data transfer, networked distributed quantum information processing, and other applications. Consider that the quantum network evolved from a recent form where a satellite performs a sequence of satellite-to-ground quantum key distribution (SatQKD) missions that allow any two ground nodes to have the symmetric encryption keys, we here develop a framework for the SatQKD downlink modelling and scheduling analysis. Incorporated with the orbital calculation and the meteorological data to downlink SatQKD modelling, the dynamic characteristics of the satellite-to-ground optical transmission could be simulated. Our work shows that the satellite downlink scheduling allows for the possibility to consider different strategies for SatQKD missions such as extending connection for distant ground nodes, prioritized delivery and promoting keys utilization, which may guide design and analysis of future missions for future satellite application.
TECHNO-MIND COMPONENTS: ARTIFICIAL CONSCIOUSNESS

Background. Th e problem of artifi cial consciousness is relevant in connection with intensive development of artifi cial intelligence technologies and the complication of technological environment of the mankind, including artifi cial agents and decisionmaking systems. It is widely covered in the interdisciplinary discourse of natural sciences, humanities and engineering. Yet, the unresolved number of fundamental issues of a methodological, systemic, and technological nature constrains its solution. Th e paper considers the problem of creating artifi cial systems that make the world and the technical subject acting in them. Objective. Th e engineering and psychological study had it purpose to investigate the technological possibilities and limitations of creating a system with artificial consciousness based on a permanently operating network learning mechanism. Results. The author proposed a post-non-classical two-stage model of the mechanism of techno-self-organization of artifi cial subjective reality, in which technoconsciousness performs the task of a multi-level multi-qualitative reduction of a part of reality accessible to the cognitive experience of a technical subject. It is substantiated that at the current level of technology development, creating a refl ective consciousness with predetermined properties is impossible. However, engineering methods allow for creating an environment that generates artifi cial subjective reality and refl ective consciousness. Conclusion. The article considers fundamental scientific, methodological, psychological, and technological issues of forming self-organizing processes underlying artifi cial consciousness. Th e author proposed his concept and metaphorical model of the brain as a generative self-organizing environment. Th e study considers the possibility of its implementation in designing and analyzing systems with artifi cial consciousness.
Vector Field-Based Guidance for Planar Smooth Path Following of a Small Unmanned Helicopter

This study investigates a guidance method that will allow the small unmanned helicopter to follow the predefined horizontal smooth path. A stable nonlinear guidance law, which needs the information of the inertial position and groundspeed of the helicopter and the implicit function of the desired path, is designed to generate the reference course rate command based on the concept of the vector field. The asymptotic approximation to the desired path with bounded following errors has also been demonstrated by using the Lyapunov stability arguments. Some conditions for guaranteeing the stability have been extended. Simulations on following four types of planar paths, i.e., the square, circular, elliptic, and cubic curve paths, have verified the effectiveness of the proposed method. The predefined square path following performance of the proposed vector field-based method is compared with another two guidance laws, which are based on the PD-like and fuzzy logic control. The comparison shows that the proposed method can guide the helicopter to follow the predefined path most smoothly. The maximum overshoot by using the proposed method is less than 0.016 meters, while those by using the other two methods are all larger than 0.80 meters. Moreover, the vector field-based method will cost the least time for the vehicle to converge to the predefined path.
Classifying handwriting samples according to their type using discriminant analysis

Classifying handwriting samples according to their type (i.e. natural, disguised, traced, simulated or unintentionally unnatural) is an important task in handwriting analysis. It may facilitate the collection of writing standards and also help experts to assess the differences between questioned material and comparative samples or to choose the best writing features and the most relevant examination protocol for the case. Current research aimed to create a method for classifying the type of a handwriting sample using discriminant analysis. Five basic types (i.e. natural, disguised, traced, simulated and unintentionally unnatural) and some subtypes were included in this study. Participants (N = 139) wrote their full signatures, fictional signatures or a short sentence. Motor and dimensional features were assessed. The methods proved to be more than twice as accurate in classifying samples according to their type than a random choice probability (e.g. 44% as opposed to 17% for the 6-types classifier). This proof-of-a-concept study demonstrates that handwriting samples may be classified according to their type with satisfying accuracy based on their writing features and statistical tools of discriminant analysis. However, further studies are necessary to improve the accuracy of the method.
Prototype generation method using a growing self-organizing map applied to the banking sector

Abstract In fields like security risk analysis, Fast Moving Consumer Goods, Internet of Things, or the banking sector, it is necessary to deal with large datasets containing a great list of variables. In these situations, the analysis becomes intricate and computationally expensive, so data reduction techniques play an important role. Prototype generation methods provide a reduced dataset with the same properties as the original. GSOMs (growing self-organizing maps) reduce the data size without the need for prefixing the number of neurons needed to represent the input space. To the best of the authors’ knowledge, this is the first time that the GSOM is applied for reduction and generation of prototypes, posing an advantage over their predecessors, the SOMs (self-organizing maps), which do not have the automatic growth feature. This work addresses the use of a GSOM to reduce the number of prototypes to use in a 1-NN (1 nearest neighbor) classifier. The proposed methodology is applied to an income dataset for testing and a large bank dataset that contain classifications into two different groups. The 1-NN classifier is used to obtain predictions using the nodes of the GSOM as prototypes. This article demonstrates that GSOMs save a significant amount of time in obtaining nearly the same validation results as SOMs by comparing the classifications obtained in the bank dataset. The results show data reductions of more than 99%, and accuracies greater than 80% for the income dataset and 74% for the bank dataset.
Computer Network Technology for College Students and its Application in English Interactive Teaching

In order to improve the effect of sports strategy formulation, this paper combines artificial intelligence assisted agile Internet of Things to conduct research on sports simulation scheme, and builds an intelligent sports simulation system.The sports simulation model designed in this paper can change the mechanism parameters according to the actual situation of the moving object, establish the fixed coordinate system and local coordinate system of the parallel platform model, and establish the mathematical model of the parallel platform.Moreover, according to the principle of vector closure, the inverse kinematics position solution is analyzed, and the inverse kinematic velocity solution is derived by using the equation of the inverse position solution.The experimental research results show that the sports simulation system based on artificial intelligence-assisted agile Internet of Things proposed in this paper has a good performance in sports simulation.
Systematic Literature Review on Cost-Efficient Deep Learning

Cloud computing and deep learning, the recent big trends in the software industry, have enabled small companies to scale their business up rapidly. However, this growth is not without a cost – deep learning models are related to the heaviest workloads in cloud data centers. When the business grows, the monetary cost of deep learning in the cloud grows fast as well. Deep learning practitioners should be prepared and equipped to limit the growing cost. We performed a systematic literature review on the methods to control the monetary cost of deep learning. Our library search resulted in 16066 papers from three article databases, IEEE Xplore, ACM Digital Library, and Scopus. We narrowed them down to 112 papers that we categorized and summarized.We found that: 1) Optimizing inference has raised more interest than optimizing training. Popular deep learning libraries already support some of the inference optimization methods such as quantization, pruning, and teacher-student. 2) The research has been centered around image inputs, and there seems to be a research gap for other types of inputs. 3) The research has been hardwareoriented, and the most typical approach to control the cost of deep learning is based on algorithm-hardware co-design. 4) Offloading some of the processing to client devices is gaining interest and has the potential to reduce the monetary cost of deep learning.
Pandemics and the built environment: A human–building interaction typology

Surveys of urban history from ancient times to the present reveal a continuum of collective responses to pandemics ranging from quarantine facilities and monitoring the spread of disease to building new wastewater networks. The contemporary COVID-19 pandemic includes new digital tools and techniques that supplement (and sometimes replace) the existing analogue responses, while raising new ethical issues with respect to privacy. A typology of pandemic responses in cities is created, based on human&ndash;building interaction (HBI) principles. This typology can be used to compare and contrast analogue and digital responses relating to distancing, monitoring and sanitising. It provides a summary of a wide range of individual and collective implications of pandemics and demonstrates the indelible connections between pandemics and the built environment. In addition, the typology provides a tool to interpret some of the opportunities and drawbacks of digitalising cities. Practice relevance The COVID-19 pandemic has demonstrated the enduring co-evolution of cities and disease through history. This study aims to inform future pandemic preparedness by providing a framework for designers, managers and users of public spaces to evaluate the multiple implications of emerging technologies that are integrated within the urban fabric. While the rapid rise of digitalisation to advance urban health agendas continues to raise new questions relating to individual and civic freedoms, HBI qualitatively provides a lens through which to examine the overlapping spatial, ethical, and temporal consequences for humans and the built environment. Urban planning researchers and designers can use HBI principles to humanise the sustainable smart city.
The Development of Intelligent Agents: A Case-Based Reasoning Approach to Achieve Human-Like Peculiarities via Playback of Human Traces

Recent advances in the digital gaming industry have provided impressive demonstrations of highly skillful artificial intelligence agents capable of performing complex intelligent behaviors. Additionally, there is a significant increase in demand for intelligent agents that can imitate video game characters and human players to increase the perceived value of engagement, entertainment, and satisfaction. The believability of an artificial agent’s behavior is usually measured only by its ability in a specific task. Recent research has shown that ability alone is not enough to identify human-like behavior. In this work, we propose a case-based reasoning (CBR) approach to develop human-like agents using human game play traces to reduce model-based programming effort. The proposed framework builds on the demonstrated case storage, retrieval and solution methods by emphasizing the impact of seven different similarity measures. The goal of this framework is to allow agents to learn from a small number of demonstrations of a given task and immediately generalize to new scenarios of the same task without task-specific development. The performance of the proposed method is evaluated using instrumental measures of accuracy and similarity with multiple loss functions, e.g. by comparing traces left by agents and players. The study also developed an automated process to generate a corpus for a simulation case study of the Pac-Man game to validate our proposed model. We provide empirical evidence that CBR systems recognize human player behavior more accurately than trained models, with an average accuracy of 75%, and are easy to deploy. The believability of play styles between human players and AI agents was measured using two automated methods to validate the results. We show that the high p-values produced by these two methods confirm the believability of our trained agents.