Analysis of SARS-CoV-2 cases in the State of Guanajuato during the third wave of infections using advanced information analysis techniques

The State of Guanajuato, located in the center of Mexico, is one of the regions of the country with a high rate of infections of the SARS-CoV-2 virus in relation to its population size, according to official data provided by the federal government. Motivated by this fact, we undertook to further analyze such data in order to identify correlations between a possible complication of the COVID-19 disease, caused by the SARS-CoV-2 virus, and some non-transmissible chronic diseases and other comorbidities. To carry out our study, we rely on the KDD methodology and specialized machine-learning tools, that allow to extract hidden knowledge in the data, which cannot usually be obtained using traditional information analysis techniques. In this way, initially, the cases infected by the SARS-CoV-2 virus were characterized in a general way and, later, classification models were built to identify some rules among the comorbidity variables.

QAnon—Religious Roots, Religious Responses

Conspiracy theories are not new to religion, nor an exclusively modern phenomenon. But they take on more destructive and wide-ranging impact with modern communication technologies. Looking at the root psychosocial mechanisms of conspiracy theories, we argue that they frame ideas, history, and culture through the cognitive mindscape of special, ‘hidden knowledge’. They also serve as a unifying theory of conflict and narration of history. The COVID epidemic has strained the economic and political system. Although it may be a matter of perception for Q-followers, a sense of precarity is enhanced by QAnon, thus unleashing and mustering an awakening for such extremist paranoid discourse of ressentiment. This parallels the cognitive mindscape of ‘the Great Replacement’. Prior to election 2020, QAnon’s base had been growing in Evangelical communities. Its presence continues to be felt.

Mapping the messenger: Exploring the disinformation of QAnon

We analyzed message board content originating with the online persona “Q,” leader of the right-wing conspiracy community known as QAnon. We qualitatively placed all of Q’s messages into one of five qualitatively derived categories: allusion to hidden knowledge, undermining institutions and individuals, inspirational, administration and security, and call to action. Further analysis of how these categories are used by Q over time illustrates how the messaging evolved. Specifically, later Q messaging focused less on hidden knowledge and conspiratorial thinking and more on politics relative to earlier messaging. We also note what Q does not include in messages: very few direct calls to action are made to the QAnon community and no specific, direct calls for violent action. Implications and future directions of research are discussed.

Interpretation function of dynamic of an underwater vehicle in non-stationary environment

Рассматривается построение и анализ функций интерпретации моделей нестационарной динамики подводных объектов (ПО) новых поколений на основе функциональных пространств современной теории катастроф (СТК) [1] – [7]. Формальный аппарат концептуальных решений и принципов построения функций интерпретации реализован в нестационарной динамической среде в рамках принципа конкуренции. Процедуры функций интерпретации основаны на использовании различных моделей взаимодействия в зависимости от уровня действующих возмущений. Неопределенность и неполнота исходной информации в динамике взаимодействия ПО в нестационарной среде, определили подход к построению функций интерпретации при построении математического описания задач нестационарной динамики ПО на основе концепции мягких вычислений (Soft Computing) [7] и выявления «скрытых» знаний (Data Mining) [1]. Разработанные модели и алгоритмы интерпретации нестационарной динамики ПО реализованы в функциональном блоке моделирования многофункционального программного комплекса (МПК) динамической визуализации нестационарной динамики ПО в режиме экстренных вычислений (Urgent Computing – UC [6]. The construction and analysis of the interpretation functions of the models of unsteady dynamics of new generation an underwater vehicle (UV) based on the modern theory of disasters (STK) [1] - [7] are considered. The formal apparatus of conceptual solutions and principles of constructing interpretation functions is implemented in a non-stationary dynamic environment within the framework of the principle of competition. The procedures of the interpretation functions are based on the use of various interaction models depending on the level of acting disturbances. The uncertainty and incompleteness of the initial information on the dynamics of the interaction of underwater vehicles in a non-stationary environment determined the approach to constructing interpretation functions when constructing a mathematical description of the problems of non-stationary dynamics of underwater vehicles based on the concept of soft computing (Soft Computing) [7] and the identification of “hidden” knowledge (Data Mining) [1]. The developed models and algorithms for interpreting unsteady dynamics of submarines are implemented in the functional block for modeling a multifunctional software complex (MPC) for dynamic visualization of unsteady dynamics of underwater vehicles in emergency computing mode Urgent Computing [6].

Education shapes the structure of semantic memory and impacts creative thinking

Education is central to the acquisition of knowledge, such as when children learn new concepts. It is unknown, however, whether educational differences impact not only what concepts children learn, but how those concepts come to be represented in semantic memory—a system that supports higher cognitive functions, such as creative thinking. Here we leverage computational network science tools to study hidden knowledge structures of 67 Swiss schoolchildren from two distinct educational backgrounds—Montessori and traditional, matched on socioeconomic factors and nonverbal intelligence—to examine how educational experience shape semantic memory and creative thinking. We find that children experiencing Montessori education show a more flexible semantic network structure (high connectivity/short paths between concepts, less modularity) alongside higher scores on creative thinking tests. The findings indicate that education impacts how children represent concepts in semantic memory and suggest that different educational experiences can affect higher cognitive functions, including creative thinking.

Becoming Palestine

In Becoming Palestine, Gil Z. Hochberg examines how contemporary Palestinian artists, filmmakers, dancers, and activists use the archive in order to radically imagine Palestine's future. She shows how artists such as Jumana Manna, Kamal Aljafari, Larissa Sansour, Farah Saleh, Basel Abbas, and Ruanne Abou-Rahme reimagine the archive, approaching it not through the desire to unearth hidden knowledge, but to sever the identification of the archive with the past. In their use of archaeology, musical traditions, and archival film and cinematic footage, these artists imagine a Palestinian future unbounded from colonial space and time. By urging readers to think about archives as a break from history rather than as history's repository, Hochberg presents a fundamental reconceptualization of the archive's liberatory potential.

Mining hidden knowledge: Embedding models of cause-effect relationships curated from the biomedical literature

We explore the use of literature-curated signed causal gene expression and gene-function relationships to construct un-supervised embeddings of genes, biological functions, and diseases. Our goal is to prioritize and predict activating and inhibiting functional associations of genes, and to discover hidden relationships between functions. As an application, we are particularly interested in the automatic construction of networks that capture relevant biology in a given disease context. We evaluated several unsupervised gene embedding models leveraging literature-curated signed causal gene expression findings. Using linear regression, it is shown that, based on these gene embeddings, gene-function relationships can be predicted with about 95% precision for the highest scoring genes. Func- tion embedding vectors, derived from parameters of the linear regression model, allow to infer relationships between different functions or diseases. We show for several diseases that gene and function embeddings can be used to recover key drivers of pathogenesis, as well as underlying cellular and physiological processes. These results are presented as disease-centric net- works of genes and functions. To illustrate the applicability of the computed gene and function embeddings to other machine learning tasks we expanded the embedding approach to drug molecules, and used a simple neural network to predict drug- disease associations.

LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.

Research on Modeling and Scheduling Methods of an Intelligent Manufacturing System Based on Deep Learning

Under the background of intelligent manufacturing, the modeling and scheduling of an intelligent manufacturing system driven by big data have attracted increasing attention from all walks of life. Deep learning can find more hidden knowledge in the process of feature extraction of the hierarchical structure and has good data adaptability in domain adaptation. From the perspective of the manufacturing system, intelligent scheduling is irreplaceable in intelligent production when the manufacturing quantity of workpieces is small or products are constantly changing. This paper expounds the outstanding advantages of deep learning in intelligent manufacturing system modeling, which provides an effective way and powerful tool for intelligent manufacturing system design, performance analysis, and running status monitoring and provides a clear direction for selecting, designing, or implementing the deep learning architecture in the field of intelligent manufacturing system modeling and scheduling. The scheduling of the intelligent manufacturing system should integrate intelligent scheduling of part processing and intelligent planning of product assembly, which is suitable for intelligent scheduling of any kind and quantity of products and resources.

Complementing Privacy and Utility Trade-Off with Self-Organising Maps

In recent years, data-enabled technologies have intensified the rate and scale at which organisations collect and analyse data. Data mining techniques are applied to realise the full potential of large-scale data analysis. These techniques are highly efficient in sifting through big data to extract hidden knowledge and assist evidence-based decisions, offering significant benefits to their adopters. However, this capability is constrained by important legal, ethical and reputational concerns. These concerns arise because they can be exploited to allow inferences to be made on sensitive data, thus posing severe threats to individuals’ privacy. Studies have shown Privacy-Preserving Data Mining (PPDM) can adequately address this privacy risk and permit knowledge extraction in mining processes. Several published works in this area have utilised clustering techniques to enforce anonymisation models on private data, which work by grouping the data into clusters using a quality measure and generalising the data in each group separately to achieve an anonymisation threshold. However, existing approaches do not work well with high-dimensional data, since it is difficult to develop good groupings without incurring excessive information loss. Our work aims to complement this balancing act by optimising utility in PPDM processes. To illustrate this, we propose a hybrid approach, that combines self-organising maps with conventional privacy-based clustering algorithms. We demonstrate through experimental evaluation, that results from our approach produce more utility for data mining tasks and outperforms conventional privacy-based clustering algorithms. This approach can significantly enable large-scale analysis of data in a privacy-preserving and trustworthy manner.