intelligence analysis
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Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.

We present five studies aimed at developing a new vocabulary test for university students. Such a test isuseful as an indication of crystallized intelligence and because vocabulary size correlates well withreading comprehension. In the first study, a list of 100 words based on Nation’s Vocabulary Size Test waspresented to 195 participants and compared to other tests of crystallized intelligence. Analysis suggestedthe presence of two distinct factors, which we interpreted as evidence for the possible existence of twotypes of difficult words: Unfamiliar words for general knowledge and unfamiliar words for specializedknowledge. In the subsequent studies we tried to develop vocabulary tests for each type of words, at thesame time trying out various reading comprehension tests to use as validation criterion. However, in thefinal study a high correlation (r =.82) was found between our two vocabulary tests, indicating that theymeasure the same latent factor, contrary to our initial assumption. Both tests have high reliability (r >.85) and correlate well (r > .4) with general knowledge, author recognition, and reading comprehension.As part of our research efforts, a collection of new and existing tests was used and (often) improved toverify the validity of the vocabulary tests. An exploratory factor analysis on all tests established 3 factors(text comprehension, crystallized intelligence, and reading rate), with the vocabulary tests loading on thefactor of crystallized intelligence, which in turn correlated with reading comprehension. Structuralequation modeling corroborated the interpretation. We end by providing an overview of the differenttests that were developed or improved throughout the studies. They are freely available for researchpurposes at https://osf.io/ef3s4/.

With the development of advanced persistent threat (APT) and the increasingly severe situation of network security, the strategic defense idea with the concept of “active defense, traceability, and countermeasures” arises at the historic moment, thus cyberspace threat intelligence (CTI) has become increasingly valuable in enhancing the ability to resist cyber threats. Based on the actual demand of defending against the APT threat, we apply natural language processing to process the cyberspace threat intelligence (CTI) and design a new automation system CTI View, which is oriented to text extraction and analysis for the massive unstructured cyberspace threat intelligence (CTI) released by various security vendors. The main work of CTI View is as follows: (1) to deal with heterogeneous CTI, a text extraction framework for threat intelligence is designed based on automated test framework, text recognition technology, and text denoising technology. It effectively solves the problem of poor adaptability when crawlers are used to crawl heterogeneous CTI; (2) using regular expressions combined with blacklist and whitelist mechanism to extract the IOC and TTP information described in CTI effectively; (3) according to the actual requirements, a model based on bidirectional encoder representations from transformers (BERT) is designed to complete the entity extraction algorithm for heterogeneous threat intelligence. In this paper, the GRU layer is added to the existing BERT-BiLSTM-CRF model, and we evaluate the proposed model on the marked dataset and get better performance than the current mainstream entity extraction mode.

This paper shows empirically the impact of organizational and behavioral determinants on the CEO's investment horizon choice, using artificial intelligence explanatory methods. We apply our approach to 100 Saudi firms. We test the effect of three organizational determinants: ownership concentration, board independence, and CEO remuneration system; and three behavioral determinants: myopia, the locus of control and commitment, on the CEO's investment horizon choice. The study’s key finding is that executives' commitment bias is the most important variable in terms of modal value that affects firms' long-term investment choice. We also find a positive and significant relationship between myopia and long-term investment choice, whereas the lowliest determinant of the horizon choice is the locus of control. More generally, these results show that CEOs who are likely to be the most myopic may display long-term behavior with the existence of high cognitive involvement.

Statistical intelligence formulates the analysis model and reveals the system that can be easily visible and understandable to mankind. On one hand, it will benefit the society to predict the nature or man-created virus environment, and on the other hand, it will solve the problems of intelligent agents' formation with their functionality. It's a well-known fact that the agents are visible and noticeable, and they perform their own assigned task, but their recognition process is delayed. The chapter will focus on the statistical intelligence analysis that includes the properties of the error tolerance, forecasting, and high reliability. The information is always the part of the memory, but the processing methodology that may lead to knowledge is lacking. This may include the logical induction, Bayesian statistics, functional decision theory, value learning, forecasting, etc. Statistics will assist in path selection to formulate the highly adaptive intelligent system with the said functionalities with reduction in the overall cost factors.

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision-making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints; and brittleness, (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this article, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues. We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments, and our research has broader application than the use case discussed.

Abstract Well log analysis, through deploying advanced artificial intelligence (AI) algorithms, is key for wellbore geological studies. By analyzing different well characteristics with modern AI tools it becomes possible to estimate interwell saturation with improved accuracy, outlining primary fluid channels and saturation propagations in the reservoirs interwell region. The development of modern deep learning and artificial intelligence methods allows analysts to predict interwell saturation as a function of observed data in the near wellbore logged geological layers. This work addresses the use of deep neural network architectures as well as tensor regression models for predicting interwell saturation from other well characteristics, such as resistivity and porosity, as well as local near-well saturation. Several algorithms are compared in terms of both accuracy and computational efficiency. Sensitivity analysis for model parameters is carried out, which is based on the wells’ geometry, radius, and multiple sampling techniques. Additionally, the impact of local saturation prior knowledge on the model accuracy is analyzed. A reservoir box model encompassing volumetric interwell porosity, resistivity and saturation data was utilized for the validating and testing of the AI algorithms. A prototype is developed with Python 3.6 programming language.