Complex Information Research Articles

Легитимность насилия в техногенном обществе

The author examines the transformation of violence as a way of survival into “violence of power”, characteristic of modern civilization, the legitimacy of its legitimization, the use of resources of the information network society, technogenic civilization to regulate internal and external state relations, and new management perspectives. The legitimacy of the activities of states that claim the right to identify weak, dying or failed states as the source of modern political violence, instability and threats, and to carry out "humanitarian missions" against them, is called into question. The types of violence that are logically built into the development system of modern technogenic society, such as economic, institutional, and informational, are considered. Attention is drawn to the effectiveness of new technologies for the introduction of military innovations and the development of military potential in support of external legitimate state violence. The inability of modern society to control and regulate increasingly complex information systems and the tendency of the authorities to proactively assess negative consequences and prevent the possibility of their occurrence are emphasized. The mechanisms of a technogenic society contribute both to the control of the legitimacy of power and provide the power with the opportunity to control society. Therefore, a culture of peace can serve as a new trend in the development of humanity.

A Multi-Scale GNN-Based Personalized Recommender System for Online Consumption Decision

In an era of consumer electronics, effective consumption recommendation contributes a lot to improving benefits for online shopping operators. However, consumers and products constitute a complex heterogeneous information network, in which extraction of structural features is essential. To construct more fine-grained feature space for modeling, we introduce the graph neural network (GNN) theory for this purpose. Accordingly, a multi-scale GNN-based personalized recommender system for online consumption decision is proposed in this paper. Firstly, we set up an encoder for consumption decision based on a multi-scale GNN structure, and define the loss function. Two realistic datasets are utilized as the research scenario, in order to complete the feature combination of online consumption. Then, personalized recommendation settings are completed based on a recurrent neural network structure. And a graph learning module is embedded in it to integrate cross-attention mechanism to establish the consumption decision algorithm. Finally, the proposed method is tested by comparing with relevant research methods under several metrics: adaptability, success rate, and stability. The results show that the proposed method has achieved some improvement in accuracy, coverage, and recommendation speed.

Improving the communication of multifactorial cancer risk assessment results for different audiences: a co-design process.

Multifactorial cancer risk prediction tools, such as CanRisk, are increasingly being incorporated into routine healthcare. Understanding risk information and communicating risk is challenging and healthcare professionals rely substantially on the outputs of risk prediction tools to communicate results. This work aimed to produce a new CanRisk report so users can directly access key information and communicate risk estimates effectively. Over a 13-month period, we led an 8-step co-design process with patients, the public, and healthcare professionals. Steps comprised 1) think aloud testing of the original CanRisk report; 2) structured feedback on the original report; 3) literature review; 4) development of a new report prototype; 5) first round of structured feedback; 6) updating the new report prototype; 7) second round of structured feedback; and 8) finalising and publishing the new CanRisk report. We received 56 sets of feedback from 34 stakeholders. Overall, the original CanRisk report was not suitable for patients and the public. Building on the feedback, the new report has an overview of the information presented: section one summarises key information for individuals; sections two and three present information for healthcare professionals in different settings. New features also include explanatory text, definitions, graphs, keys and tables to support the interpretation of the information. This co-design experience shows the value of collaboration for the successful communication of complex health information. As a result, the new CanRisk report has the potential to better support shared decision-making processes about cancer risk management across clinical settings.

Multi-Domain Joint Synthetic Aperture Radar Ship Detection Method Integrating Complex Information with Deep Learning

With the flourishing development of deep learning, synthetic aperture radar (SAR) ship detection based on this method has been widely applied across various domains. However, most deep-learning-based detection methods currently only use the amplitude information from SAR images. In fact, phase information and time-frequency features can also play a role in ship detection. Additionally, the background noise and the small size of ships also pose challenges to detection. Finally, satellite-based detection requires the model to be lightweight and capable of real-time processing. To address these difficulties, we propose a multi-domain joint SAR ship detection method that integrates complex information with deep learning. Based on the imaging mechanism of line-by-line scanning, we can first confirm the presence of ships within echo returns in the eigen-subspace domain, which can reduce detection time. Benefiting from the complex information of single-look complex (SLC) SAR images, we transform the echo returns containing ships into the time-frequency domain. In the time-frequency domain, ships exhibit distinctive features that are different from noise, without the limitation of size, which is highly advantageous for detection. Therefore, we constructed a time-frequency SAR image dataset (TFSID) using the images in the time-frequency domain, and utilizing the advantages of this dataset, we combined space-to-depth convolution (SPDConv) and Inception depthwise convolution (InceptionDWConv) to propose Efficient SPD-InceptionDWConv (ESIDConv). Using this module as the core, we proposed a lightweight SAR ship detector (LSDet) based on YOLOv5n. The detector achieves a detection accuracy of 99.5 with only 0.3 M parameters and 1.2 G operations on the dataset. Extensive experiments on different datasets demonstrated the superiority and effectiveness of our proposed method.

Binning Metagenomic Contigs Using Contig Embedding and Decomposed Tetranucleotide Frequency.

Metagenomic binning is a crucial step in metagenomic research. It can aggregate the genome sequences belonging to the same microbial species into independent bins. Most existing methods ignore the semantic information of contigs and lack effective processing of tetranucleotide frequency, resulting in insufficient and complex feature information extracted for binning and poor binning results. To address the above problems, we propose CedtBin, a metagenomic binning method based on contig embedding and decomposed tetranucleotide frequency. First, the improved BERT model is used to learn the contigs to obtain their embedding representation. Secondly, the tetranucleotide frequencies are decomposed using a non-negative matrix factorization (NMF) algorithm. After that, the two features are spliced and input into the clustering algorithm for binning. Considering the sensitivity of the DBSCAN clustering algorithm to input parameters, in order to solve the drawbacks of manual parameter input, we also propose an Annoy-DBSCAN algorithm that can adaptively determine the parameters of the DBSCAN algorithm. This algorithm uses Approximate Nearest Neighbors Oh Yeah (Annoy) and combines it with a grid search strategy to find the optimal parameters of the DBSCAN algorithm. On simulated and real datasets, CedtBin achieves better binning results than mainstream methods and can reconstruct more genomes, indicating that the proposed method is effective.

A toolbox for surfacing health equity harms and biases in large language models.

Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. We present resources and methodologies for surfacing biases with potential to precipitate equity-related harms in long-form, LLM-generated answers to medical questions and conduct a large-scale empirical case study with the Med-PaLM 2 LLM. Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases and EquityMedQA, a collection of seven datasets enriched for adversarial queries. Both our human assessment framework and our dataset design process are grounded in an iterative participatory approach and review of Med-PaLM 2 answers. Through our empirical study, we find that our approach surfaces biases that may be missed by narrower evaluation approaches. Our experience underscores the importance of using diverse assessment methodologies and involving raters of varying backgrounds and expertise. While our approach is not sufficient to holistically assess whether the deployment of an artificial intelligence (AI) system promotes equitable health outcomes, we hope that it can be leveraged and built upon toward a shared goal of LLMs that promote accessible and equitable healthcare.

Narrow road extraction from high-resolution remote sensing images: SWGE-Net and MSIF-Net

ABSTRACT Accurate and complete road network extraction plays a critical role in urban planning, street navigation, and emergency response. At present, narrow roads are a main feature in most public road datasets. However, the continuity and boundary completeness of the extraction results for these narrow roads are relatively poor, due to their varied shapes, uneven spatial distribution, and the presence of various interfering elements. To address these issues, this study introduces a novel network, the Self-weighted Global Context Road Extraction Network (SWGE-Net), which integrates a dilate block and an improved coordinate attention mechanism to effectively capture the complex details and spatial information of narrow roads. Furthermore, most public road training datasets often lack labels for very narrow roads, this omission leads to poor extraction results for these roads in test datasets. In order to further improve the extraction capability for unlabeled, extremely narrow roads, this study introduces another network called the Multi-scale Information Fusion Road Extraction Network (MSIF-Net), which uses the same encoders as SWGE-Net and has a special module for merging information at different scales. This module, with a dilate block and pyramid pooling-based decoder, makes the network better at recognizing and combining features of different sizes. Experimental results indicate that SWGE-Net outperforms the baseline network with road IoU scores of 71.57% and 60.67% on the DeepGlobe and CHN6-CUG road datasets, respectively an improvement of 18.51% and 5.40%. Meanwhile, MSIF-Net not only exceeds the baseline in road IoU scores for both datasets, but also achieves the best performance in extracting unlabeled, extremely narrow roads in qualitative experiments.

Robust Reversible Watermarking Scheme in Video Compression Domain Based on Multi-Layer Embedding

Most of the existing research on video watermarking schemes focus on improving the robustness of watermarking. However, in application scenarios such as judicial forensics and telemedicine, the distortion caused by watermark embedding on the original video is unacceptable. To solve this problem, this paper proposes a robust reversible watermarking (RRW)scheme based on multi-layer embedding in the video compression domain. Firstly, the watermarking data are divided into several sub-secrets by using Shamir’s (t, n)-threshold secret sharing. After that, the chroma sub-block with more complex texture information is filtered out in the I-frame of each group of pictures (GOP), and the sub-secret is embedded in that frame by modifying the discrete cosine transform (DCT) coefficients within the sub-block. Finally, the auxiliary information required to recover the coefficients is embedded into the motion vector of the P-frame of each GOP by a reversible steganography algorithm. In the absence of an attack, the receiver can recover the DCT coefficients by extracting the auxiliary information in the vectors, ultimately recovering the video correctly. The watermarking scheme demonstrates strong robustness even when it suffers from malicious attacks such as recompression attacks and requantization attacks. The experimental results demonstrate that the watermarking scheme proposed in this paper exhibits reversibility and high visual quality. Moreover, the scheme surpasses other comparable methods in the robustness test session.

Cluster Analysis Methods to Support Population Health Improvement Among US Counties.

Population health rankings can be a catalyst for the improvement of health by drawing attention to areas in need of relative improvement and summarizing complex information in a manner understood by almost everyone. However, ranks also have unintended consequences, such as being interpreted as "hard truths," where variations may not be significant. There is a need to improve communication about uncertainty in ranks, with accurate interpretation. The most common solutions discussed in the literature have included modeling approaches to minimize statistical noise or borrow strength from covariates. However, the use of complex models can limit communication and implementation, especially for broad audiences. Explore data-informed grouping (cluster analysis) as an easier-to-understand, empirical technique to account for rank imprecision that can be effectively communicated both numerically and visually. Cluster analysis, specifically k-means clustering with Wasserstein (earth mover's) distance, was explored as an approach to identify natural and meaningful groupings and gaps in the data distribution for the County Health Rankings' (CHR) health outcomes ranks. County-level health outcomes from the 2022 CHR. 3082 counties that were ranked in the 2022 CHR. Data-informed health groups. Cluster analysis identified 30 health groupings among counties nationwide, with cluster size ranging from 9 to 184 counties. On average, states had 16 identified clusters, ranging from 3 in Delaware and Hawaii to 27 in Virginia. Number of clusters per state was associated with number of counties per state and population of the state. The method helped address many of the issues that arise from providing rank estimates alone. Public health practitioners can use this information to understand uncertainty in ranks, visualize distances between county ranks, have context around which counties are not meaningfully different from one another, and compare county performance to peer counties.

Information protection and cyber security in the public and financial sectors

The public sector faces increasing challenges in the field of cyber security and information protection, stimulated by the rapid development of digital technologies. A significant increase in the need for reliable protection of state data and information systems requires state institutions to implement complex information protection measures. The purpose of the study is to analyze modern approaches and technologies in the field of cyber security and the use of encryption, cryptography, and the use of cloud technologies that can be used in the public sector for effective information protection. The research methodology involves consideration of strategies that allow to increase the level of security and ensure the protection of critical infrastructure. The results of the study emphasize the need to adapt to the rapidly changing digital environment and involve modern technological solutions to create a reliable information network system in the public sector. The findings of the study draw attention to the importance of global cooperation and increased investment in the field of countering digital attacks, demonstrating examples of effective international cooperation and support in this direction. The need for the integration of international standards and practices in order to create a global digital space has been revealed. A promising area of research is the analysis of the impact of the latest technologies on data storage strategies, which contributes to the identification of ways for further innovations in the industry and the creation of flexible information network systems capable of effectively responding to changing conditions and challenges of modern cyberspace.

InfoFlowNet: A multi-head attention-based self-supervised learning model with surrogate approach for uncovering brain effective connectivity

Deciphering brain network topology can enhance the depth of neuroscientific knowledge and facilitate the development of neural engineering methods. Effective connectivity, which gauges the directional influences among brain regions, is pivotal for these studies. This research introduces InfoFlowNet, a novel self-supervised learning model designed to infer causal relationships in electroencephalogram (EEG) data, aiming at capturing the complex information flow among brain regions. Specifically, InfoFlowNet employs convolution operations and multi-head self-attention mechanisms with a masking feature, which ignores self-connections to focus on inter-regional dynamics. Additionally, a new causal magnitude estimation is introduced by assessing the impact of shuffled surrogate data on signal prediction to quantify causal influence. Experiments using synthetic data and real EEG datasets demonstrate that InfoFlowNet effectively uncovers time-varying causal relationships. Compared with the Granger causality model (GCM) and the temporal causal discovery framework (TCDF), InfoFlowNet shows superior sensitivity in detecting significant causal edges. For instance, in a psychomotor vigilance task, InfoFlowNet identified 8 out of 16 significant causal edges, significantly outperforming GCM and TCDF. Furthermore, InfoFlowNet successfully passes the whiteness test on the model's residuals, and advanced analyses illustrate the advantages of utilizing multi-head attention and masking mechanisms to capture effective connectivity. In sum, InfoFlowNet represents a significant advancement in the analysis of effective connectivity, offering a deeper understanding of brain network dynamics. The model's ability to discern complex causal interactions supports its potential application in broader neuroscientific research and applications.

Controls of tectonics and paleoclimate on depositional–diagenetic evolution and pore types of Upper Cretaceous successions (Sarvak Formation) in the Abadan plain, Iran

This study examines the influence of tectonics and paleoclimate on the diagenetic evolution of Upper Cretaceous carbonate reservoirs (Sarvak Formation) in the Abadan Plain, Zagros Basin, Iran. Through petrographic analysis and scanning electron microscopy, the sedimentary facies, diagenetic processes, and pore system within the Sarvak Formation were analyzed. Facies analysis identified eight microfacies, which were deposited in shallow to deep settings of a carbonate ramp. Among these, the grain-dominated facies (packstone and grainstone) of shoal complexes and reef-talus settings were identified as the most important reservoir facies. The formation's complex diagenetic history includes marine, meteoric, and burial diagenetic processes, with meteoric diagenesis playing a significant role beneath paleoexposure surfaces. The Sarvak Formation was categorized into eight flow units, seven pore size classes, eleven electrofacies, eight velocity deviation zones, and thirteen Lorenz zones. The study's findings indicate that the most favorable reservoir units are located beneath the Cenomanian–Turonian exposure surface, characterized by significant dissolution of rudist-dominated facies due to meteoric processes and karstification. Tectonic uplift, along with weathering and erosion within a warm and humid (tropical) paleoclimate, significantly impacts the diagenetic evolution, pore types, and petrophysical properties of the Sarvak Formation. This research underscores the complex interplay between tectonics, paleoclimate, and petrophysical properties in the Abadan Plain, enhancing our understanding of carbonate systems in tectonically active and paleoclimatically dynamic regions.

A Multi-Mechanism Fusion Method for Robust State of Charge Estimation via Bidirectional Long Short-Term Memory Model with Mixture Kernel Mean p-Power Error Loss Optimized by Golden Jackal Optimization Algorithm

Abstract Accurate state of charge (SOC) estimation is crucial for effective battery management in various applications. The bidirectional long short-term memory (BiLSTM) as an outstanding nonlinear regression model can be used for SOC estimation. This work develops a novel multi-mechanism fusion method based on BiLSTM to further enhance its estimation performance for SOC, in which the convolutional neural network (CNN), attention mechanism, and mixture kernel mean p-power error (MKMPE) loss are introduced into the BiLSTM framework for addressing different issues. First, the introduction of CNN components aims to extract essential features from battery data, enhancing the model's comprehension of complex information. Then, the attention mechanism is used to further refine the model's perceptual ability and a robust MKMPE loss is introduced into the BiLSTM framework to replace its original mean squared error loss, and a novel robust model is developed to suppress non-Gaussian noise interference. Finally, some key hyperparameters of the proposed model are fine-tuned using the golden jackal optimization algorithm, resulting in improved estimation performance. Comparative numerical experiments are meticulously conducted in various cases to evaluate the performance of the proposed method, and the experiment results demonstrate that it can perform outstanding effectiveness in handling non-Gaussian noise scenarios.

Simona: a comprehensive R package for semantic similarity analysis on bio-ontologies

BackgroundBio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains.ResultsWe present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies.ConclusionsSimona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems.

Digital Design Quality Management: The Theory of Faults

Built environment and infrastructure designs are produced in complex information systems. Managing this complexity poses a significant challenge; data scarcity and bias are substantial obstacles. This paper addresses the gap in data availability by mining building information modeling (BIM) meta-databases. Two case study databases are studied and assessed through various analytical tools, including network modeling, statistics, entropy, fast Fourier transform (fft), and information constraint (IC). Through analytical and empirical investigation, insights into the morphology, characteristics, failures, causes, and trajectories of design information systems are derived, showcasing that complex systems can be retroactively deciphered and predicted to a certain extent. This study acknowledges design errors as social and psychological phenomena and proposes behavioristic and systemic remedies for addressing design faults. Applying design quality-control management tools, risk assessment, and responsibility allocation becomes feasible, enabling proactive and real-time corrective actions. This research contributes a theoretical framework and practical methodologies as well as the application of data analysis tools to design quality control management, addressing both the information gap and the complexity challenge.

ENGAGING INDONESIAN YOUTH WITH THE MEBUUG-BUUGAN TRADITION THROUGH THE NOVEL "SEHARI SETELAH SEPI"

This research explores the potential of novel illustrations as an educational medium for the Mebuug-buugantradition, a unique ritual from Kedonganan Village, Bali, which has been increasingly marginalized from the younger generation's knowledge. Through a qualitative approach, this study analyzes how the "slice of life" genre and anime/webtoon visual style can be utilized in the fictional novel "Sehari Setelah Sepi" to bridge the generational gap and promote local cultural heritage. Set in 2017-2018, when the Mebuug-buugan tradition was revived, the novel aims to present a relevant and engaging story for Indonesian teenagers. Analysis of literature, competitor research, and Unique Selling Proposition (USP) formed the foundation for designing practical illustrations. Research findings indicate that combining the "slice of life" genre with anime/webtoon visual style can effectively convey complex information about the history and meaning of the Mebuug-buugan tradition in a simple yet profound manner. The novel serves as entertainment and an effective educational tool for introducing Balinese cultural values to the younger generation.

Decoding Research with a Glance: The Power of Graphical Abstracts and Infographics

Background and Aims: Historically integral to science, visual elements like Darwin’s evolutionary trees have evolved with technological advancements, leading to more sophisticated and accessible visual representations. Graphical abstracts (GA) and infographics (IG) have become pivotal in enhancing the dissemination and understanding of scientific research by transforming complex data into visually engaging summaries. This review explores the evolution, impact, and design principles of these tools in scientific communication, highlighting their significance in the digital era. Methods: We did a comprehensive review of the English literature, in April 2024, from the databases of PubMed, Scopus, Embase, and Google Scholar to curate the appropriate articles related to the GA and IG related to research-publications. We have also made some representative examples of each of these two tools, using published articles in the Apollo Medicine journal. Results: The GA and IG, are the tools that not only aid in bridging the gap between scientific communities and the public but also enhance comprehension, engagement, and retention of information across diverse audiences. By utilising strategic layouts, colours, and interactive elements, they make complex information more digestible and appealing. Conclusion: This review addresses the challenges of maintaining accuracy and avoiding oversimplification in visual science communication. The future directions suggest a move towards more interactive and immersive visual experiences, emphasising the need for empirical research to optimise design and impact. GA and IG are transforming scientific communication by making research findings more accessible and engaging, fostering a better-informed public.

Construction of Cultural Heritage Knowledge Graph Based on Graph Attention Neural Network

To address the challenges posed by the vast and complex knowledge information in cultural heritage design, such as low knowledge retrieval efficiency and limited visualization, this study proposes a method for knowledge extraction and knowledge graph construction based on graph attention neural networks (GAT). Using Tang Dynasty gold and silver artifacts as samples, we establish a joint knowledge extraction model based on GAT. The model employs the BERT pretraining model to encode collected textual knowledge data, conducts sentence dependency analysis, and utilizes GAT to allocate weights among entities, thereby enhancing the identification of target entities and their relationships. Comparative experiments on public datasets demonstrate that this model significantly outperforms baseline models in extraction effectiveness. Finally, the proposed method is applied to the construction of a knowledge graph for Tang Dynasty gold and silver artifacts. Taking the Gilded Musician Pattern Silver Cup as an example, this method provides designers with a visualized and interconnected knowledge collection structure.

Effective presentation of ontological overlap of multiple conceptual models

Conceptual models are used to help professionals understand complex information systems and solve problems during systems analysis and design. Because single model often do not represent all relevant information, typically multiple models are used in combination. To design effective combinations of models, we propose a systematic approach that uses color highlighting to foreground overlapping concepts between multiple models to help readers identify corresponding information between models. We conducted two empirical studies – an online experiment and an eye-tracking experiment – to evaluate the cognitive efficacy of this approach. Our findings suggest that color highlighting can somewhat improve participants’ domain understanding but not the efficiency of problem-solving. Findings from the eye-tracking study suggest that the use of color can have both beneficial and harmful effects, depending on the extent of overlap.

Two-sided matching theory-based second-hand house transaction evaluation and recommendation by the modified PLC-DEMATEL method

To enhance the two-sided matching efficiency in a multi-source heterogeneous environment, this paper takes the randomness and unstructured features of the online comments into consideration, and proposes a new matching mechanism by introducing the complex information representation tool. Firstly, the concept of the probabilistic linguistic normal cloud (PLNC) model is introduced to preserve information cohesion and characteristic distribution. Next, the basic operation laws and corresponding operators are given. Then, an innovative maximum boundary concept skipping the indirect approach is presented to update the similarity degree and distance measures, also the correlation coefficient. Furthermore, for the multi-indicator systems with interactions, the peer experts are invited to evaluate the relationship between indicators, a modified algorithm based on the Decision-Making and Trial Evaluation Laboratory (DEMATEL) method is applied to obtain the subjective weights of indicators. After that, a whole matching process and a correlation coefficient cluster method-based recommendation algorithm are presented. A case study is provided to illustrate the method, wherein a new indicator system is constructed by analyzing the correlation of multiple indicators based on online linguistic evaluations. The random forest model is combined to obtain the objective weights and balance its reliability. Finally, sensitivity analysis and comparative analysis are employed to validate the effectiveness and applicability.