Different Types Of Information Research Articles

ALGORITHM OF USING THE OSINT TECHNOLOGY IN MODERN SERVICES

The tools and forms of the process of organizing and managing the collection of intelligence data areconsidered. The relationship between operations with different types of information is shown. Techniques of active andpassive information collection were studied. The main types of OSINT data collection tools, their capabilities andapplication features are given. The classification of resources for OSINT operations is given. An information searchalgorithm using modern services (entertainment services) has been developed. The proposed algorithm for using OSINTtechnology in modern services allows you to obtain the maximum amount of data, minimize resources and time, and takesinto account the peculiarities of the legislation of the country of the OSINT object. Based on the statement, the articlealso discusses the relationship between different types of information, the capabilities and application features of variousOSINT data collection tools, and the classification of resources for OSINT operations. Additionally, it presents aninformation search algorithm that utilizes modern services, considering the peculiarities of the legislation of the countryof the OSINT object.

МЕТОД МОДЕЛЮВАННЯ, ЯК ЗАСІБ ФОРМУВАННЯ ІНФОРМАЦІЙНО-АНАЛІТИЧНОЇ КОМПЕТЕНТНОСТІ ПРИ ВИВЧЕННІ ФІЗИКИ

The article considers the necessity of forming information-analytical competence of the future specialist as a component of professional competence, which reflects the readiness and ability of higher education students to apply information-analytical knowledge, skills, abilities in combination with their personal qualities during working with information of different types and forms, and the ability to search, evaluate, preserve, analyze, design and transmit important information in order to obtain qualitatively new knowledge for responsible decision-making in professional activities, preserving and multiplying the most important achievements. Components of information-analytical competence are considered: cognitive component (information-analytical theoretical knowledge; information-analytical technological knowledge); functional component (instrumental and communicative skills; analytical skills; ability to analyze information and formalize it, to compare, summarize, synthesize with available data, develop options for using information; to predict the results of the problem situation, appropriate registration of analysis results and their transfer); personality-evaluating component (orientation and motivation of the individual to perform information and analytical activities). The using of the modeling method is analyzed, which requires the use of theoretical level concepts that do not have direct models in real subjects of study and are formed due to the introduction of idealization and modeling methods in the learning process. The method of modeling is used in all branches of physical knowledge and at all stages of the development of physical theory. Scientific modeling is understood as a way of cognition in which another, more complex system, which is the object of research, is reproduced with the help of the created system. Closely related to the method of modeling are methods of scientific knowledge as an analogy, idealization, and imaginary experiments. In the course of modeling on the basis of establishing the similarity of some already studied essential features of two or more objects, phenomena. The use of modeling method and structural-logical scheme of formation of scientific knowledge and processes and formation of information-analytical competence are compared.

Misinformation due to asymmetric information sharing

On social media platforms, true and false information compete. Importantly, some messages travel much further than others, even if they concern the same topic. This fact is not reflected in models of social learning (or opinion formation) in networks. Our model fills this gap by allowing different types of information to have different decay factors and to be shared with different networks of people, incorporating asymmetries in sharing behaviors. More “shareable” information then dominates in the long run. This yields a substantial probability of misinformation, in contrast to the special case of symmetry covered by the literature. Asymptotic learning requires a perfect balance between two types of asymmetry: the product of decay factor and largest eigenvalue in the respective signal sharing networks must coincide. Approaching this balance reduces the speed of convergence and enables social learning in the shorter term. Our analysis thus suggests that policy makers, who do not know the true state, aim to mitigate asymmetries in signal sharing, e.g. by weakening echo chambers or by fostering the shareability of cumbersome, boring messages.

Fused solar-blind UV/VIS Bi-spectral sensing and imaging with vertically stacking ZnGa2O4/MAPbI3 structure

Various spectral bands provide different types of information, and information enhancement could be achieved by selective fusion of different spectral bands. The fused solar-blind Ultraviolet (UV)/Visible (VIS) bi-spectral sensing and imaging can provide the precise location of UV targets in virtue of VIS background, which has been increasingly promoted. However, most reported UV/VIS bi-spectral photodetectors (PDs) only have one single channel towards a broadband spectrum of both UV and VIS light, which cannot distinguish two kinds of signals, hindering the image fusion of bi-spectral signals. This work demonstrates the solar-blind UV/VIS bi-spectral PD based on vertically stacking perovskite of MAPbI3 and ternary oxide of ZnGa2O4 with independent and distinct response toward solar-blind UV and VIS light in a single pixel. The PD exhibits excellent sensing properties with an I on/I off ratio of >107 and 102, detectivity of >1010 and 108 Jones, and response decay time of 90 μs and 16 ms for VIS and UV channels, respectively. The successful fusion of VIS and UV images suggests that our bi-spectral PD can be applied in the accurate identification of corona discharge and fire detection.

A data-driven investigation of human action representations

Understanding actions performed by others requires us to integrate different types of information about people, scenes, objects, and their interactions. What organizing dimensions does the mind use to make sense of this complex action space? To address this question, we collected intuitive similarity judgments across two large-scale sets of naturalistic videos depicting everyday actions. We used cross-validated sparse non-negative matrix factorization to identify the structure underlying action similarity judgments. A low-dimensional representation, consisting of nine to ten dimensions, was sufficient to accurately reconstruct human similarity judgments. The dimensions were robust to stimulus set perturbations and reproducible in a separate odd-one-out experiment. Human labels mapped these dimensions onto semantic axes relating to food, work, and home life; social axes relating to people and emotions; and one visual axis related to scene setting. While highly interpretable, these dimensions did not share a clear one-to-one correspondence with prior hypotheses of action-relevant dimensions. Together, our results reveal a low-dimensional set of robust and interpretable dimensions that organize intuitive action similarity judgments and highlight the importance of data-driven investigations of behavioral representations.

The Influence of Various Information About Insect Content and Its Advantages on the Acceptance of Foods Containing Insects

ABSTRACT Information about the content of food products affects the potential consumers’ willingness to purchase certain foodstuffs and include them in their diet. The presentation and scope of information are essential in the case of marketing specific products such as insect-based foods. The purpose of our study was to investigate the influence of information about a product’s advantages, coupled with verbal and visual information provided on the packaging, on the participants’ assessment of food containing insects. The participants (N = 659) were informed about the environmental or health benefits of ingesting insects. They were subsequently presented with a picture of a food product with different types of information about the insect content and asked to evaluate the product. The results suggest that providing images of insects on the packaging results in a lower assessment of the food containing insects. A brief presentation of the advantages of entomophagy has no impact on insect-based food assessment.

An Examination of Memory Performance in a Fearful Virtual Reality Gaming Context

Background The emotion of fear bears a survival significance enabling animals to be highly vigilant in contexts of uncertainty. Expectedly, not all information available in the environment can be processed. Given that a major goal is to get rid of the source of fear in a fearful situation, the current study therefore hypothesized that information relating to the fear source would be better attended to and recalled than contextual information, which was validated by comparing participants’ recall of different types of information presented in a fearful VR gaming context. Methodology Fifty-four participants were divided into two groups and finished a specific segment of a VR game with different fear levels. Results No significant difference in recall performance on the questions about the appearance of the fear source and the questions about the interaction with the source was observed between the fear and less-fear groups. Yet, contextual information was better recalled than information related to the fear source in both groups. Regardless of whether the contextual information was provided beforehand or not, participants still tended to focus primarily on the environment in the gameplay. Also, the immersiveness of the gameplay was positively related to participants’ fear level. Discussion The tendency of attending to contextual information was speculated to be unconditional, in line with the risk calculation observed in prey when situated in less familiar contexts. It was suggested to further validate the present primarily findings by adopting VR headset with eye-tracking function in future studies. Lastly, an understanding of fear’s effect on memorization might also provide some information for developing future survival horror games.

Long-Term Climate Sensitivity of Resin-Tapped and Non-Resin-Tapped Scots Pine Trees Based on Tree Ring Width and Blue Intensity

The resin tapping of pine trees in Poland ended in the early 1990s. However, we can still find individual trees, and sometimes larger groups of trees, that were tapped. This study focused on the effect of the mechanical wounding of trees during resin tapping on the growth and climatic sensitivity of pine trees. The study concerned a 160-year-old pine stand in northwestern Poland in which resin tapping was last performed in the 1970s. All the trees had remained standing because of their high quality, which had destined them for seed collection. The stand included both resin-tapped (RT) and non-RT (NRT) trees. Our study was based on a dendrochronological analysis of two signals—annual tree ring widths (TRWs) and their delta blue intensity (DBI). We observed a significant increase in annual TRW after resin tapping had ceased, alongside a decrease in the DBI. The temporal stability in growth response was examined using daily climatic correlations from 1921 to 2021. It was found that the climatic sensitivity of RT and NRT pines was similar. There were differences in only some of the years, most while resin tapping was occurring, and then approximately 20 years after the resin tapping had ceased. However, these were small differences that mainly related to the strength of the correlation. It was also discovered that we can obtain different types of information from the study of TRWs and DBI.

Comprehension of explicit and implicit information in prereaders: the role of maternal education, receptive vocabulary, executive functions, and theory of mind

ABSTRACT Listening narrative comprehension is a complex process that requires the processing of explicit (i.e., information presented in the text) and implicit information (i.e., information inferable from the text) and involves several linguistic and cognitive skills. However, the specific role of these skills in children’s comprehension remains unclear. This study investigated the contribution of maternal education, receptive vocabulary, executive functions (working memory, inhibitory control and cognitive flexibility), and Theory of Mind to the comprehension of explicit and implicit information during a listening comprehension task among 100 Italian native speakers attending first grade (M age = 6.5 years, SD = 3.7). Hierarchical linear regression analyses indicated that maternal education and children’s verbal skills were positively associated with comprehension of explicit information, whereas cognitive flexibility and Theory of Mind provided an independent contribution to the comprehension of implicit information over and above maternal education and verbal skills. Prereaders not only process different types of information during a listening comprehension task and engage in integrative processes to go beyond the information presented in the text but also rely on different linguistic and cognitive skills in the comprehension of both explicit and implicit information.

Social Media Use and COVID-19 Vaccination Intent: An Exploratory Study on the Mediating Role of Information Exposure

Abstract We stumble upon new and repeating information daily. As information comes from many sources, social media continues to play a predominant role in disseminating information, ultimately impacting individuals’ perceptions and behaviors. A prime example of this impact was observed during the COVID-19 pandemic, in which social media use was influencing willingness to receive the COVID-19 vaccine. While studies on this relationship between social media use and vaccination intent have been widely investigated, less is known about the mechanisms that link these two variables, specifically the types of information seen on social media platforms and the effects of these different types of information. In this exploratory study, we demonstrate the mediator role of information exposure (to include both types of information and frequency) between social media use and vaccination intent. Our results show that different types of information mediate this relationship differently and demonstrate how these relationships were further moderated by the income level of the participant. We conclude with the implications of these findings and how our findings can inform the direction of future research within the field of human–computer interaction.

Hoe herkennen we de emoties van Ovidius’ Aglauros, Pyramus en anderen?

Emotions make a story more than a list of events. While reading, readers assign emotions to characters, and this constitutes an important, if not crucial, aspect of understanding narrative texts. Information in the text enables this process of assigning emotions, but readers’ pre-existing knowledge and their previous (emotional) experiences also play a role. In this article, I aim to provide more insight into the interaction between reader and text, and present different types of information that play a role in understanding emotions in narratives. These types are based on research on text comprehension and reading processes and concepts from cognitive and affective narratology. Examples include knowledge about typical plot structures, types of characters and emotional scripts. I provide illustrations taken from the set texts of the 2023 Latin Dutch exam (Ovid’s Metamorphoses). Some passages in this corpus provide detailed descriptions of a character’s emotions, while others appeal more to the reader’s background knowledge. The collected texts contain many stories with a romantic plot line. We find gods with sudden feelings of desire, unrequited love, budding and forbidden relationships, long and happy marriages. Deception, misunderstanding and bad news (Fama) are obstacles to these relationships, creating strong emotions.

How does financial development influence carbon emission intensity in the OECD countries: Some insights from the information and communication technology perspective

Based on an extended STIRPAT framework, this paper investigates the effects of financial development on carbon emission intensity in OECD countries from linear and non-linear perspectives, where financial development is proxied by three dimensions: financial deepening, financial deepening, and financial size, and financial efficiency. Fortunately, three types of financial development significantly alleviate carbon emission intensity. An extended moderation effect model is built to estimate the effect of financial development via information and communication technology on carbon emission intensity. The results reveal that internet-based information and communication technology and service-based information and communication technology are positively correlated with carbon emission intensity. To effectively handle the endogeneity issue triggered by causal relationships between variables and allow potential non-linear nexus, an advanced dynamic panel threshold model incorporating the generalised method of moments is employed to investigate how financial development affects carbon emission intensity under different types of information and communication technology. Empirical evidence demonstrates the significance of the non-linear nexus between financial development and carbon emission intensity. Lastly, heterogeneity analysis demonstrates the existence of heterogeneity associated with institutional quality, degree of economic development, and resource endowment concerning the effect of financial development on carbon emission intensity among the OECD countries.

Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

AbstractRecently, transformer‐based networks have been introduced for the classification of hyperspectral image (HSI). Although transformer‐based methods can well capture spectral sequence information, their ability to fuse different types of information contained in HSI is still insufficient. To exploit rich spectral, spatial and semantic information in HSI, a novel semantic and spatial‐spectral feature fusion transformer (S3FFT) network is proposed in this study. In the proposed S3FFT method, spatial attention and efficient channel attention (ECA) modules are employed for the extraction of shallow spatial‐spectral features. Then, a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature extraction. Finally, the semantic, spatial and spectral features are combined by the transformer for classification. Compared with traditional deep learning methods and recently transformer‐based methods, the proposed S3FFT shows relatively better results on three HSI datasets.

Introducing the Jihadi Plots in Europe Dataset (JPED)

The article introduces a new dataset on terrorist attack activity by jihadis in Western Europe. The dataset was built over nearly two decades at the Norwegian Defence Research Establishment. It records not only attacks that were launched, but also plots that were foiled by counterterrorism. The rationale behind the dataset is that analyses based on launched attacks may misrepresent the scale and nature of terrorism. The article shows that if we were to measure the jihadi threat to Europe by attacks only we would miss out on 58.2% of the total attack activity. We also show that while attack data signify a trend towards small attacks by individuals, these data hide the fact that terror networks persist in plotting big attacks that get foiled. We discuss how analyses based on attacks only may distort comparative analyses of different types of terrorists across countries, as well as causal analyses of terrorism drivers. By including foiled plots we gain a fuller picture of terrorism. The Jihadi Plots in Europe Dataset allows the gauging of different aspects of the threat from Al-Qaida and Islamic State in Europe. The dataset documents when and where terror plots occur, whether or not they result in attacks, and different types of information about perpetrators and how they operate. The Dataset offers a reliable overview of jihadi terrorism in Europe for research and policy.

Characteristic evaluation via multi-sensor information fusion strategy for spherical underwater robots

Currently, most of the existing fusion methods ignore the rich multi-source information of different types of sensor nodes in the underwater unknown environment, which makes it challenging for Autonomous Underwater Vehicles (AUVs) to accurately perceive the external environment and make actionable decisions. Considering the key issues such as attitude estimation, positioning and obstacle avoidance involved in performing AUV tasks, this paper proposed a Multi-Source Information Fusion (MSIF) model for Spherical Underwater Robots (SURs) we developed based on various low-cost sensors. Multi-source information from an Inertial Measurement Unit (IMU), Pressure Sensor Array (PSA), Obstacle Avoidance Sensor Array (OASA), Depth Sensor (DS), Looking-Down Camera (LDC) and Acoustic Communication System (ACS) were fused to enable SUR to obtain high-precision estimated data for attitude estimation, positioning and obstacle avoidance, etc. More precisely, according to the correlation between the sensors, the optimized model was constructed to compensate for angle errors, velocity errors, orientation errors, etc. Subsequently, a machine learning method using Back Propagation Neural Network (BPNN) was proposed to improve the accuracy and effectiveness of the MSIF model through feature selection, data training, and feature estimation, etc. Finally, a series of experiments were performed under different scenarios, such as motion and obstacle avoidance experiments. The theoretical derivation and comprehensive evaluations demonstrated the effectiveness and feasibility of the proposed model, which provided a new reference value for solving issues such as attitude estimation, positioning and obstacle avoidance of AUVs.

A proteomics approach to identify COPD-related changes in lung fibroblasts.

Lung fibroblasts are implicated in abnormal tissue repair in chronic obstructive pulmonary disease (COPD). The exact mechanisms are unknown and comprehensive analysis comparing COPD- and control fibroblasts is lacking. The aim of this study is to gain insight into the role of lung fibroblasts in COPD pathology using unbiased proteomic and transcriptomic analysis. Protein and RNA were isolated from cultured parenchymal lung fibroblasts of 17 patients with stage IV COPD and 16 non-COPD controls. Proteins were analyzed using LC-MS/MS and RNA through RNA sequencing. Differential protein and gene expression in COPD was assessed via linear regression, followed by pathway enrichment, correlation analysis, and immunohistological staining in lung tissue. Proteomic and transcriptomic data were compared to investigate the overlap and correlation between both levels of data. We identified 40 differentially expressed (DE) proteins and zero DE genes between COPD and control fibroblasts. The most significant DE proteins were HNRNPA2B1 and FHL1. Thirteen of the 40 proteins were previously associated with COPD, including FHL1 and GSTP1. Six of the 40 proteins were related to telomere maintenance pathways, and were positively correlated with the senescence marker LMNB1. No significant correlation between gene and protein expression was observed for the 40 proteins. We hereby describe 40 DE proteins in COPD fibroblasts including previously described COPD proteins (FHL1, GSTP1) and new COPD research targets like HNRNPA2B1. Lack of overlap and correlation between gene and protein data supports the use of unbiased proteomics analysis and indicates that different types of information are generated with both methods.

Exploring the Quantum Nature of Human Cognition: Integrating Quantum Cognitive Science and Advanced Cognitive Assessment through the Verbovisual Cognitive Function Screening Tool (VCFST)

The Verbovisual Cognitive Function Screening Tool (VCFST) is a cognitive screening tool that aims to evaluate cognitive abilities by drawing upon established theories such as dual-coding theory, common coding theory, propositional theory, and ideomotor theory. However, recent developments in quantum cognitive science offer promising applications to enhance the assessment model. This article explores the utilization of quantum cognition concepts within the VCFST and discusses its implications for cognitive assessment. Quantum cognitive science is an emerging interdisciplinary field that applies principles from quantum physics to the study of human cognition. It suggests that cognitive processes may not adhere strictly to classical logic and linear thinking but can exhibit characteristics of quantum phenomena such as superposition, entanglement, and contextuality. By incorporating quantum concepts into cognitive assessment, a more nuanced understanding of complex cognitive processes can be achieved.The VCFST, with its focus on verbovisual factors and comprehensive assessment of cognitive domains, can benefit from the integration of quantum cognitive science principles. Superposition, borrowed from quantum physics, implies that individuals can simultaneously perceive and attend to multiple stimuli. By considering the superposition of verbovisual stimuli within the VCFST, a more accurate representation of cognitive abilities can be obtained, capturing the simultaneous processing of different types of information. Entanglement, another key principle of quantum cognition, can be applied to evaluate the interconnectedness of cognitive domains within the VCFST. Analogous to entangled particles, changes in one cognitive domain may influence others. By assessing the impact of changes in one domain on performance in other domains, the VCFST can provide a holistic understanding of cognitive functioning. Contextuality, a fundamental concept in quantum cognition, emphasizes the role of context in shaping cognitive processes. By incorporating contextuality into the VCFST, contextual cues can be introduced, and stimulus presentation can be varied to reflect real-world cognitive demands. This approach enables a more ecologically valid measure of cognitive abilities, considering the influence of context on cognitive processing. Embracing quantum cognitive science principles in the VCFST offers several advantages, including capturing the complexity of cognitive processes, accounting for non-linear dynamics, and exploring potential quantum-like effects within human cognition. Future research can further refine the VCFST by conducting quantitative studies to assess the efficacy of incorporating quantum concepts into cognitive assessment and developing computational models inspired by quantum cognitive science.The utilization of quantum cognitive science concepts within the VCFST shows promise for advancing cognitive assessment practices. By incorporating principles such as superposition, entanglement, and contextuality, the VCFST can provide a comprehensive and nuanced evaluation of cognitive abilities. Further research in quantum cognitive science and the refinement of assessment methodologies can contribute to the development of more advanced and accurate cognitive assessment tools, facilitating personalized diagnostic and therapeutic strategies for cognitive disorders and neurodegenerative conditions. The integration of quantum cognitive science into cognitive assessment represents an exciting frontier that can revolutionize our understanding of human cognition.

Cross-view enhancement network for underwater images

Single underwater image enhancement remains a challenging ill-posed problem, even with advanced deep learning methods, due to the significant information degeneration and various irrelevant contents. Current deep learning-based underwater image enhancement methods only consider using a single clear image as a positive feature for guiding the training of the enhancement network. However, the limited amount of helpful information constrains the network performance, and irrelevant contents consume many bits. Therefore, it is crucial to efficiently utilize cross-view neighboring features and provide corresponding relevant information for underwater enhancement. To address the challenges of degraded underwater images, we propose a novel cross-domain enhancement network (CVE-Net) that uses high-efficiency feature alignment to utilize neighboring features better. We employ a self-built database to optimize the helpful information and develop a feature alignment module (FAM) to adapt the temporal features. The dual-branch attention block is designed to handle different types of information and give more weight to essential features. Experiments demonstrate that CVE-Net outperforms state-of-the-art (SOTA) underwater vision enhancement methods in terms of both qualitatively and quantitatively results, significantly boosts the performance on underwater image quality, achieving a PSNR of 28.28 dB, which is 25% higher than Ucolor on the multi-view dataset. CVE-Net improves image quality while maintaining a good complexity-performance trade-off.

What types of information can and do latent print examiners review? A survey of practicing examiners

Understanding typical work practices is important to understanding the decision-making process underlying latent print comparison and improving the reliability of the discipline. Despite efforts to standardize work practices, a growing literature has demonstrated that contextual effects can influence every aspect of the analytic process. However, very little is known about what types of information are available to latent print examiners, and what types of information latent print examiners routinely review. We surveyed practicing latent print examiners (N = 284) regarding what types of information are accessible during routine casework, and what types of information they routinely review during casework. We also explored whether access and inclination to review different types of information vary according to unit size and examiner role. Results indicated that information describing the physical evidence is accessible by almost all examiners (94.4%), and most examiners have access to offense type (90.5%), method of evidence collection (77.8%), and the names of both suspect (76.1%) and victim (73.9%). However, evidence description (86.3%) and method of evidence collection (68.3%) were the only information types consistently reviewed by most examiners. Findings also indicate that examiners in smaller laboratories have access to more information types and often review more information types than examiners from larger laboratories, but both populations choose to not review information at similar rates. Further, examiners in supervisory positions are more likely to choose to not review information than examiners in non-supervisory positions. Although there is some consensus regarding what types of information examiners routinely review, findings suggest that there is little absolute consensus regarding what information examiners can even access, and highlight two sources of variability in examiner work practices: employment setting and examiner role. This is concerning in light of efforts to maximize the reliability of analytic procedures (and ultimately, conclusions) and represents an important area of future study as the field progresses.

A Novel Automated Approach to Mutation-Cancer Relation Extraction by Incorporating Heterogeneous Knowledge.

Automatic extraction of relations between gene mutations and cancer entities occurring in the cancer literature using text mining can rapidly provide vital information to support precision cancer medicine. However, mutation-cancer relation extraction is more challenging than general relation extraction from free text, since it is often not possible without cancer-specific background knowledge and thus the model replies on a deeper understanding of complex surrounding tokens. We propose a deep learning model that jointly extracts mutations and their associated cancers. Background knowledge comes from two different knowledge bases which store different types of information about mutations. Given the different ways in which knowledge is stored in these two resources, we propose two separate methods for embedding knowledge, namely sentence-based knowledge integration and attribute-aware knowledge integration. The evaluation demonstrated that our model outperforms a number of baseline models and gains 96.00%, 92.57% and 94.57% F1 scores on three public datasets, EMU BCa, EMU PCa, and BRONCO, thus illustrating the effectiveness of our knowledge integration approach. The auxiliary experiments show that our models can utilize more informative text from the KBs and link the mutations to their corresponding cancer disease although the input text provides insufficient context.