Recently the World faced force push to distant learning caused by COVID-19 disease. Statistical numbers show a notable increasing number of users of corporate educational solutions utilizing cloud architecture. However, non-cloud-based learning tools do not meet this growth. In this work the authors consider the causes of that contradictory behaviour and present an explanation based on differences between two types of these educational systems. Also, the authors formulate an interpretation giving a list of extracted technologies or product features that allow corporate solutions to quickly gain popularity among educational society. In addition, clear examples of their connection to learning methods that can improve teaching, learning, and the last, but not the least a user’s experience are provided. And finally, the authors highlight a sig- nificant role of integration and interoperability standards supporting easy com- ponents replacement and scaling.
Social networks are an integral part of modern life. They allow us to communicate online and exchange all kinds of information. In this paper, we consider the social network Instagram and its hashtags as a key tool for finding relevant information and new friends. The aim of our work is an empirical analysis of hashtags for posts in Instagram with certain locations. We obtain database of users of the Instagram network and collect a dataset of posts for three Far Eastern cities. Then, we build a friendship graph, for which we solve the link prediction problem. We show that both, structural and attributive graph information, such as hashtags, is important to achieve best quality.
P108 - Predictive mathematical modelling of recurrence periods for the secondary distant metastases in patients with ER/PR/HER2/Ki-67 subtypes of breast cancer
Quite recently, considerable attention has been paid to distance learn- ing due to mass schools and universities closing because of coronavirus disease. This fact brought to light not only strengths but also weaknesses of existing edu- cational software and use cases. This paper is motivated by the questions which are rinsed by thoroughly adopted educational software and the deep gap between educational methodologies and up-to-date distributed information infrastructure capabilities. The lack of an engineering approach in the deployment of distance learning tools leads to both technological and methodological problems. We pre- sent exploratory analysis of data gathered from authority web sources. The sig- nificant ideological differences between educational software generations are discussed with special attention to non-cloud-based and cloud-based collabora- tive technologies and corresponding platforms. The authors conclude that the power of integration based on industry-wide interoperability standards is useful to solve current problems in distance education related to software.
We give a proof-theoretic and algorithmic complexity analysis for systems introduced by Morrill to serve as the core of the CatLog categorial grammar parser. We consider two recent versions of Morrill’s calculi, and focus on their fragments including multiplicative (Lambek) connectives, additive conjunction and disjunction, brackets and bracket modalities, and the ! subexponential modality. For both systems, we resolve issues connected with the cut rule and provide necessary modifications, after which we prove admissibility of cut (cut elimination theorem). We also prove algorithmic undecidability for both calculi, and show that categorial grammars based on them can generate arbitrary recursively enumerable languages.
Pattern Mining (PM) has an important place in Data Mining and Knowledge Discovery and has many applications in a wide variety of domains. To date, a lot of different approaches to PM have been proposed. However, new methods continue to appear. Some of the reasons for that are the following: (i) there is no gold standard for evaluating the quality of PM approaches, (ii) the results of existing approaches are unsatisfactory. But what is wrong with them? In this paper, we adopt the best practices of building supervised models to PM. We propose a PM method Keeplt Simple that combines the technique of supervised learning and the state-of-the-art of modern PM. We show in experiments that the proposed approach allows for obtaining small sets of interesting and non-redundant patterns.
Aiming to understand the data complexity of answering conjunctive queries mediated by an axiom stating that a class is covered by the union of two other classes, we show that deciding their first-order rewritability is PSPACE-hard and obtain a number of sufficient conditions for membership in AC0, L, NL, and P. Our main result is a complete syntactic AC0/NL/P/CONP tetrachotomy of path queries under the assumption that the covering classes are disjoint.
This book constitutes the proceedings of the 8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019.
The 24 full papers and 10 short papers were carefully reviewed and selected from 134 submissions (of which 21 papers were rejected without being reviewed). The papers are organized in topical sections on general topics of data analysis; natural language processing; social network analysis; analysis of images and video; optimization problems on graphs and network structures; analysis of dynamic behaviour through event data.
Previously, a mathematical model of primary tumor (PT) growth and secondary distant metastasis (sdMTS) growth in breast cancer (BC) (CoMPaS), considering the TNM classification, was presented. Nowadays, the updated model CoMPaS and the corresponding software tool can help to optimize the process of detecting the different diagnostic periods for sdMTSs in BC patients with different tumor subtypes ER/PR/HER2/Ki-67 and the growth rate of the PT and sdMTSs.
This volume contains the refereed proceedings of the 8th International Conference on Analysis of Images, Social Networks, and Texts (AIST 2019). The previous conferences during 2012–2018 attracted a significant number of data scientists – students, researchers, academics, and engineers working on interdisciplinary data analysis of images, texts, and social networks.
Infinitary action logic is an extension of the multiplicative-additive Lambek calculus with Kleene iteration, axiomatized by an 𝜔-rule. Buszkowski and Palka (2007) show that this logic is \(\Pi^0_1\)-complete. As shown recently by Kuznetsov and Speranski, the extension of infinitary action logic with the exponential modality is much harder: \(\Pi^1_1\)-complete. The raise of complexity is of course due to the contraction rule. We investigate fragments of infinitary action logic with exponential, which still include contraction, but have lower (e.g., arithmetically bounded) complexity. In this paper, we show an upper \(\Pi^0_1\) bound for the fragment of infinitary action logic, in which the exponential can be applied only to formulae of implication depth 0 or 1.
This book constitutes the proceedings of the 18th Russian Conference on Artificial Intelligence, RCAI 2020, held in Moscow, Russia, in October 2020.
The 27 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 140 submissions. The conference deals with a wide range of topics, including data mining and knowledge discovery, text mining, reasoning, decisionmaking, natural language processing, vision, intelligent robotics, multi-agent systems,machine learning, AI in applied systems, and ontology engineering.
The paper addresses the questions of data science education of current importance. It aims to introduce and justify the framework that allows flexibly evaluate the processes of a data expedition and a digital media created during it. For these purposes, the authors explore features of digital media artefacts which are specific to data expeditions and are essential to accurate evaluation. The rubrics as a power but hardly formalizable evaluation method in application to digital media artefacts are also discussed. Moreover, the paper documents the experience of rubrics creation according to the suggested framework. The rubrics were successfully adopted to two data-driven journalism courses. The authors also formulate recommendations on data expedition evaluation which should take into consideration structural features of a data expedition, distinctive features of digital media, etc.
In this paper we learn how to manage a dialogue relying on discourse of its utterances. We consider two complementary approaches of dialogue management based on the discourse text analysis to extend the abilities of the interactive information retrieval-based chat bot.
In this paper, the utility and advantages of the discourse analysis for text pairs categorization and ranking are investigated. We consider two tasks in which discourse structure seems useful and important: automatic verification of political statements, and ranking in question answering systems. We propose a neural network based approach to learn the match between pairs of discourse tree structures. To this end, the neural TreeLSTM model is modified to effectively encode discourse trees and DSNDM model based on it is suggested to analyze pairs of texts. In addition, the integration of the attention mechanism in the model is proposed. Moreover, different ranking approaches are investigated for the second task. In the paper, the comparison with state-of-the-art methods is given. Experiments illustrate that combination of neural networks and discourse structure in DSNDM is effective since it reaches top results in the assigned tasks. The evaluation also demonstrates that discourse analysis improves quality for the processing of longer texts.
The seven preceding editions of the FCA4AI Workshop showed that many researchers working in Artificial Intelligence are deeply interested by a well-founded method for classification and data mining such as Formal Concept Analysis (see https://conceptanalysis. wordpress.com/fca/). FCA4AI was co-located with ECAI 2012 (Montpellier), IJCAI 2013 (Beijing), ECAI 2014 (Prague), IJCAI 2015 (Buenos Aires), ECAI 2016 (The Hague), IJCAI/ECAI 2018 (Stockholm), and IJCAI 2019 (Macao). The workshop has now a quite long history and all the proceedings are available as CEUR proceedings (see http://ceur-ws. org/, volumes 939, 1058, 1257, 1430, 1703, 2149, and 2529). This year, the workshop has again attracted many researchers from many countries working on actual and important topics related to FCA, showing the diversity and the richness of the relations between FCA and AI. Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications and association rules) which can be used for many Artificial Intelligence needs, e.g. knowledge discovery, learning, knowledge representation, reasoning, ontology engineering, as well as information retrieval and text processing. Recent years have been witnessing increased scientific activity around FCA, in particular a strand of work emerged that is aimed at extending the possibilities of FCA w.r.t. knowledge processing, such as work on pattern structures, relational context analysis, and triadic analysis. These extensions are aimed at allowing FCA to deal with more complex data, both from the data analysis and knowledge discovery points of view. Actually these investigations provide new possibilities for AI practitioners within the framework of FCA. Accordingly, we are interested in the following issues: • How can FCA support AI activities such as knowledge processing, i.e. knowledge discovery, knowledge representation and reasoning, learning, i.e. clustering, pattern and data mining, natural language processing, and information retrieval (non exhaustive list). • How can FCA be extended in order to help Artificial Intelligence researchers to solve new and complex problems in their domains. The workshop is dedicated to discussion of such issues. First of all we would like to thank all the authors for their contributions and all the PC members for their reviews and precious collaboration. This year, 24 papers were submitted and 14 were accepted for presentation at the workshop, out of which 6 short papers. The papers submitted to the workshop were carefully peer-reviewed by three members of the program committee. Finally, the order of the papers in the proceedings (see page 5) follows the program order (see http://fca4ai. hse.ru/2020/).
Information retrieval (IR) chatbot is a special class of virtual assistants, which is widely used nowadays in customer support services. However, the work of modern IR retrieval systems is limited by simple queries to the database, which does not utilize all the potential of interaction with the user. In this paper we implement an FCA-based approach to deliver the relevant information the user has requested. A developing approach integrates a concept-based model build upon the database and intelligent traversal through it. The proposed algorithm has been implemented as an additional function within the existing IR chatbot. In this paper we also enlighten the perspectives for further development of the proposed system. Formal Concept Analysis (FCA) technique and Pattern Structures as its extension are proposed to process unstructured data (objects with a text description), which has become a common way of presenting various items recently.
An approximate discovery of closed itemsets is usually based on either setting a frequency threshold or computing a sequence of projections. Both approaches, being incremental, do not provide any estimate of the size of the next output and do not ensure that “more interesting patterns” will be generated first. We propose to generate closed itemsets incrementally, w.r.t. the size of the smallest (cardinality-minimal or minimum) generators and show that this approach (i) exhibits anytime property, and (ii) first generates itemsets of better quality and then those of lower quality.