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Abstract In this work, we propose a quantum-mechanically measurable basis for the computation of spread complexity. Current literature focuses on computing different powers of the Hamiltonian to construct a basis for the Krylov state space and the computation of the spread complexity. We show, through a series of proofs, that time-evolved states with different evolution times can be used to construct an equivalent space to the Krylov state space used in the computation of the spread complexity. Afterwards, we introduce the effective dimension, which is upper-bounded by the number of pairwise distinct eigenvalues of the Hamiltonian. The computation of the spread complexity requires knowledge of the Hamiltonian and a classical computation of the different powers of the Hamiltonian. The computation of large powers of the Hamiltonian becomes increasingly difficult for large systems. The first part of our work addresses these issues by defining an equivalent space, where the original basis consists of quantum-mechanically measurable states. We demonstrate that a set of different time-evolved states can be used to construct a basis. We subsequently verify the results through numerical analysis, demonstrating that every time-evolved state can be reconstructed using the defined vector space. Based on this new space, we define an upper-bounded effective dimension and analyze its influence on finite-dimensional systems. We further show that the Krylov space dimension is equal to the number of pairwise distinct eigenvalues of the Hamiltonian, enabling a method to determine the number of eigenenergies the system has experimentally. Lastly, we compute the spread complexities of both basis representations and observe almost identical behavior, thus enabling the computation of spread complexities through measurements.

Data processing is complicated nowadays due to its vast amount and low structuration level. Perspective field to provide such structuration is exceptional studies because, in most cases, they foresee measuring parameters planned before such expeditions. Therefore, it is possible to define the parameters that will be measured and prepare forms to fill them during the expedition. This study substantiates the way of processing such data in the form of ontologies, providing structuring and interoperability of expedition data by providing a similar structuring approach. The case of using such an approach based on real-life expedition study in the field of the environment of the National Center "Junior Academy of Sciences of Ukraine" is described. The approach foresees using Excel to define the parameters that will be measured during an expedition in the form of table’s columns. Each row in such a table will represent the place or name of the research object. An additional column is devoted to inserting geolocation. After providing the research, such files are uploaded into Polyhedron system and ontology is generated. Such ontologies are stored and integrated into ArcGis and can be used to present their own results and provide background research by other researchers.

New mass media paradigms for information distribution have emerged with the digital age. With new digital-enabled mass media, the communication process is centered around the user, while multimedia content is the new identity of news. Thus, the media landscape has shifted from mass media to personalized social media. While this progress brings advantages, it also carries the risk of being detrimental to society through the emergence of misinformation (false or inaccurate information) and disinformation (intentionally spreading misinformation) in the form of fake news. Fake news is a tool used to manipulate public opinion on particular topics, distort public perceptions, and generate social unrest while lacking the rigor of traditional journalism. Driven by this current and real-world problem, in this paper, we train multiple Deep Learning architectures for multi-class classification and compare their performance in detecting the veracity of the news articles. To achieve accurate models in detecting misinformation, we employ a large dataset containing 100 000 news articles labeled with ten classes (one with real news and the rest with different types of fake news). We use two preprocessing techniques, i.e., one simple and another very aggressive, to clean the dataset. We also employ three word embeddings that preserve the word context, i.e., Word2Vec, FastText, and GloVe, pre-trained and trained on our dataset to vectorize the preprocessed dataset. For the misinformation task, we train a Logistic Regression as a baseline and compare its results with the performance of ten Deep Learning architectures. We obtain the best results using a Recurrent Convolutional Neural Network based architecture. The experimental results show that the models are highly dependable on text preprocessing and the word embedding employed.

Artificial intelligence (AI) has been successfully applied in industry for decades, ranging from the emergence of expert systems in the 1960s to the wide popularity of deep learning today. In particular, inexpensive computing and storage infrastructures have moved data-driven AI methods into the spotlight to aid the increasingly complex manufacturing processes. Despite the recent proverbial hype, however, there still exist non-negligible challenges when applying AI to smart manufacturing applications. As far as we know, there exists no work in the literature that summarizes and reviews the related works for these challenges. This paper provides an executive summary on AI techniques for non-experts with a focus on deep learning and then discusses the open issues around data quality, data secrecy, and AI safety that are significant for fully automated industrial AI systems. For each challenge, we present the state-of-the-art techniques that provide promising building blocks for holistic industrial AI solutions and the respective industrial use cases from several domains in order to better provide a concrete view of these techniques. All the examples we reviewed were published in the recent ten years. We hope this paper can provide the readers with a reference for further studying the related problems.

This article is devoted to the presentation of the semantic interoperability of research and scientific results through an ontological taxonomy. To achieve this, the principles of systematization and structuration of the scientific/research results in scientometrics databases have been analysed. We use the existing cognitive IT platform Polyhedron and extend it with an ontology-based information model as main contribution. As a proof-of-concept we have modelled two ontological graphs, “Development of a rational way for utilization of methane tank waste at LLC Vasylkivska poultry farm” and “Development a method for utilization of methane tank effluent”. Also, for a demonstration of the perspective of ontological systems for a systematization of research and scientific results, the “Hypothesis test system” ontological graph has created.

Providing complex digital support for scientific research is an urgent problem that requires the creation of useful tools. Cognitive IT-platform Polyhedron has used to collect both existing informational ontology-based tools, and specially designed to complement a full-stack of instruments for digital support for scientific research. Ontological tools have generated using the Polyhedron converter using data from Google sheets. Tools “Search systems”, “Hypothesis test system”, “Centre for collective use”, “The selection of methods”, “The selection of research equipment”, “Sources recommended by Ministry of Education and Science of Ukraine”, “Scopus sources”, “The promising developments of The National Academy of Sciences of Ukraine” were created and structured in the centralized ontology. A comparison of each tool to existing classic web-based analogue provided and described.

Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by employing general and specific contextual cues obtained through the use of document embeddings (Doc2Vec) and Topic Modeling. In our approach, (1) we use a large dataset with game reviews to create different word embeddings by applying Word2Vec, FastText, and GloVe, (2) we create Doc2Vecs enriched with the local context given by the word embeddings for each review, (3) we construct topic embeddings Topic2Vec using three Topic Modeling algorithms, i.e., LDA, NMF, and LSI, to enhance the global context of the Sentiment Analysis task, (4) for each document and its dominant topic, we build the new DocTopic2Vec by concatenating the Doc2Vec with the Topic2Vec created with the same word embedding. We also design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Network Architectures that show promising results for this task. The proposed DocTopic2Vecs are used to benchmark multiple Machine and Deep Learning models, i.e., a Logistic Regression model, used as a baseline, and 18 Deep Neural Networks Architectures. The experimental results show that the new embedding and the new Deep Neural Network Architectures achieve better results than the baseline, i.e., Logistic Regression and Doc2Vec.