Information Distortion Research Articles

CUFNet: A fusion network based on cross-reconstruction uniqueness for visible and infrared images

Image fusion is crucial in computer vision, offering numerous advantages across various applications. However, current fusion methods often face challenges in striking a balance between preserving specific features, which are susceptible to subjective bias, and avoiding the distortion of unique information, thus limiting overall fusion performance. Given these challenges, we have proposed a cross-reconstruction uniqueness fusion network (CUFNet) to fuse visible and infrared images. Initially, we developed the cross-reconstruction uniqueness module to objectively measure feature uniqueness and enhance the accurate retention of distinctive features. Subsequently, we devised the feature compensation module to improve the interaction among medium, deep, and shallow features, generating a fused image with enhanced texture details. Finally, the multilayer feature fusion model was utilized to optimize the retention of specific features across different layers. Experimental validation conducted using two publicly available datasets, TNO and RoadScene, demonstrates that our proposed method, CUFNet, outperforms twelve comparative methods in terms of both quantitative and qualitative perspectives.

STDCformer: Spatial-temporal dual-path cross-attention model for fMRI-based autism spectrum disorder identification

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge. In this regard, this work proposes a model featuring a dual-path cross-attention framework for spatial and temporal patterns, named STDCformer, aiming to enhance the accuracy of ASD identification. STDCformer can preserve both temporal-specific patterns and spatial-specific patterns while explicitly interacting spatiotemporal information in depth. The embedding layer of the STDCformer embeds temporal and spatial patterns in dual paths. For the temporal path, we introduce a perturbation positional encoding to improve the issue of signal misalignment caused by individual differences. For the spatial path, we propose a correlation metric based on Gramian angular field similarity to establish a more specific whole-brain functional network. Subsequently, we interleave the query and key vectors of dual paths to interact spatial and temporal information. We further propose integrating the dual-path attention into a tensor that retains spatiotemporal dimensions and utilizing 2D convolution for feed-forward processing. Our attention layer allows the model to represent spatiotemporal correlations of signals at multiple scales to alleviate issues of information distortion and loss. Our STDCformer demonstrates competitive results compared to state-of-the-art methods on the ABIDE dataset. Additionally, we conducted interpretative analyses of the model to preliminarily discuss the potential physiological mechanisms of ASD. This work once again demonstrates the potential of deep learning technology in identifying ASD and developing neuroimaging biomarkers for ASD.

The Impact of Information Distortions on Decision-Making: A Case Study in Land–Sea Transport Chain Planning

Management based on reliable, complete, and up-to-date information is key to increasing a transport chain’s effectiveness and sustainability. The instability of the business environment and the increase in competition have contributed to the growth of challenges faced by managers of transport and logistics companies who make decisions using low-value information exposed to significant distortions. This article aims to investigate the impact of information distortions on decision-making quality and to determine the probability of making trustworthy decisions in freight land–sea transport chain planning. The research was carried out in several stages, which included the formulation of rules for detecting information distortions, as well as distortion clustering and evaluation of their impact on decision quality. A methodology to assess the probability of making trustworthy decisions was developed. It was shown that information value directly impacts the quality of decisions related to transport chain planning for both traditional and unique/occasional freight. In the case of significant information distortion, the manager’s ability to assess available information considerably increases, especially in crises, when the lack of time to verify information threatens the accuracy of decisions. This study’s results may be useful for transport and logistics companies’ managers, who make decisions using information obtained from various sources.

Популярні маніпуляції із соціологічними даними: критичний аналіз

This article delves into the prevalent forms of manipulation employed in quantitative sociological data. The authors highlight how the burgeoning volume of Big Data and the advancement of data processing technologies in the contemporary world have opened up new avenues for information manipulation. This manipulation often involves the active use of propaganda, disinformation, phishing, and other techniques, which exert a significant impact on society. It leads to the distortion of information through the dissemination of false and misleading data, eroding trust in authoritative sources and media outlets. Moreover, it directly impinges upon social stability, misrepresents the true picture of events, and triggers conflicts, social tensions, and mass unrest. The article expounds on the notion that manipulated data obtained from sociological research can be instrumental in crafting a fabricated reality, influencing people's thoughts and behaviors, and consequently, warping public opinion. The study of manipulative technologies has gained particular urgency in the context of Russia's armed aggression against Ukraine, which has ignited an accompanying information war. The article also devotes considerable attention to a detailed analysis of sociological theories that comprehensively illuminate the issue of the implementation of data manipulation technologies, including «doublethink», «manufacturing consent», the «spiral of silence», «reality packaging», and the «nudge».

Investigating the Impact of Information Distortions on the Resilience of Land-sea Transport Chains Under Business-environment Instability Conditions

Possessing reliable information is the basis for making accurate decisions when planning land-sea transport chains. Modern transport chains create increasingly complicated networks, which affect the decisions made when planning these chains. It should be noted, however, that information distortions may arise at individual stages of the transport process within complex transport chains. The article aims to examine the sources of hazards and risks occurring in land-sea transport chains, to determine the information distortion indicators that may be processed by transport and logistics industry enterprises under conditions of business environment instability, as well as to determine the levels of these chains' resilience to possible information distortions. The article presents the selected types of managerial decision errors. An approach to assessing the impact of distorted information on the decision-making process has been proposed. Information distortion indicators processed by transport and logistics industry enterprises in conditions of business-environment instability were also identified. An attempt was made to determine the possible levels of resilience of these chains to the symptoms of business-environment instability. It was found that, depending on the degree of distortion of the information used by the manager, their decision may be erroneous to a varying degree, while the degree of managerial decisions' incorrectness can be assessed by the number of decision-making cycles required to make and implement an effective decision.

Infrared and Visible Image Fusion Algorithm Based on Double-Domain Transform Filter and Contrast Transform Feature Extraction

Current challenges in visible and infrared image fusion include color information distortion, texture detail loss, and target edge blur. To address these issues, a fusion algorithm based on double-domain transform filter and nonlinear contrast transform feature extraction (DDCTFuse) is proposed. First, for the problem of incomplete detail extraction that exists in the traditional transform domain image decomposition, an adaptive high-pass filter is proposed to decompose images into high-frequency and low-frequency portions. Second, in order to address the issue of fuzzy fusion target caused by contrast loss during the fusion process, a novel feature extraction algorithm is devised based on a novel nonlinear transform function. Finally, the fusion results are optimized and color-corrected by our proposed spatial-domain logical filter, in order to solve the color loss and edge blur generated in the fusion process. To validate the benefits of the proposed algorithm, nine classical algorithms are compared on the LLVIP, MSRS, INO, and Roadscene datasets. The results of these experiments indicate that the proposed fusion algorithm exhibits distinct targets, provides comprehensive scene information, and offers significant image contrast.

Identification of the bias in embodied emissions flows and their sources

Input-output (IO) table has been widely used to investigate the relationship between the environment and the economy. As pre-analysis data treatment could give rise to information distortion in the adjusted tables, a large body of national IO-based analysis on environmental issues potentially suffer from imports data treatment. When national IO tables with different imports assumptions are available, it is important and possible to investigate the distortion and corresponding bias in findings. Via embodied carbon emissions, a set of IO-based techniques are utilized to analyze the source and transmission of the bias. The empirical study of China shows that relative indicator (embodied intensity) performs more robustly than absolute indicator (embodied emissions) at all transmission layers. Bias in embodied emissions accumulates to ±183.6 Mt. across the products. Supply chains of S27-Construction, S20-Electronic equipment and S18-Transport equipment are most distorted, with sectors such as chemicals, metals and non-metallic minerals as key nodes in transmitting the bias. The decomposition analysis also shows that the bias in S27 (137.9 Mt), S20 (34.7 Mt) and S18 (24 Mt) is primarily due to distorted intermediate use, while the bias in S14 (35.0 Mt) is mainly caused by distorted final demand. Implications on policies and other IO-based studies are discussed.

Параметрическая оптимизация асимметричных многопроводных линий передачи с учетом комбинационных импульсов

Transmission lines are significant elements and play an important role in determining the performance, reliability and functionality of radio electronics. A number of factors appearing in the use of transmission lines can lead to problems in the field of electromagnetic compatibility. One such factor is the appearance of asymmetry due to inhomogeneities, PCB manufacturing errors, dense tracing, mismatch, etc. The paper considers the effect of asymmetry on the characteristics of transmission lines with inhomogeneous dielectric filling. It is found that asymmetry of cross section and/or boundary conditions leads to the appearance of combinational pulses. Combination pulses are of interest because their arrival times do not correspond to the per-unit-length delays of the modes propagating in the transmission lines, but consist of their combinations. Their level may be comparable to the level of information signals, which may result in distortion or loss of useful information. Meanwhile, asymmetry and the resulting combinational pulses represent a new resource for improving the performance of devices based on transmission lines, which can be used to protect radio electronics from electromagnetic interference of picosecond and nanosecond duration. For the first time the optimization of parameters taking into account combinational pulses is carried out on the example of three diagrams of symmetric multiconductor transmission lines with asymmetry of termination loads: reflection symmetric and with a reference conductor in the form of side polygons. Using the genetic algorithm, the optimal parameters are obtained, which allow to reduce the value of the maximum amplitude by 1.5 times, as well as the parameters allowing to increase in 2 times the minimum value of the time interval between the decomposition pulses, on which depends the possibility of complete decomposition of the interference pulse, compared to the value obtained in the initial diagram with optimized parameters, where there are no combinational pulses at the same device dimensions. The optimization results demonstrate the efficiency of using the resource of combinational pulses to improve protective structures based on transmission lines with inhomogeneous dielectric filling.

SECURITY OF DATA STORED IN CLOUD SERVICES

The main purpose of this study is to - examine the security issues of data stored in cloud services. The authors of the article discuss the main problems associated with an insecure and incorrectly configured cloud database, which can lead to leakage or distortion of information of Russian citizens and organizations. In the course of the study the authors come to the conclusion that in order to protect their data it is necessary to use cloud storage providers located on the territory of the Russian Federation (hereinafter referred to as RF), use a hidden storage system, regularly update and monitor the system to identify vulnerabilities and prevent possible attacks. In addition, ways to solve the problem and effectively protect information stored in cloud services, including at the legislative level, are proposed.

Lightweight helmet target detection algorithm combined with Effici-Bi-Level Routing Attention.

Wearing helmets is essential in two-wheeler traffic to reduce the incidence of injuries caused by accidents. We present FB-YOLOv7, an improved detection network based on the YOLOv7-tiny model. The objective of this network is to tackle the problems of both missed detection and false detection that result from the difficulties in identifying small targets and the constraints in equipment performance during helmet detection. By applying an enhanced Bi-Level Routing Attention, the network can improve its capacity to extract global characteristics and reduce information distortion. Furthermore, we deploy the AFPN framework and effectively resolve information conflict using asymptotic adaptive feature fusion technology. Incorporating the EfficiCIoU loss significantly improves the prediction box's accuracy. Experimental trials done on specific datasets reveal that FB-YOLOv7 attains an accuracy of 87.2% and 94.6% on the mean average precision (mAP@.5). Additionally, it maintains a high level of efficiency with frame rates of 129 and 126 frames per second (FPS). FB-YOLOv7 surpasses the other six widely-used detection networks in terms of detection accuracy, network implementation requirements, sensitivity in detecting small targets, and potential for practical applications.

An Improved Method for Detecting Covert Channels in the Transport Layer

Steganography is one of the techniques used both to leak information and to prevent theft and distortion of confidential information. Covert channels in the network are one of the platforms for information steganography in the network. Often, this method is realized using the network's protocols. Network protocols are used as an information transfer medium. Network protocols can contain critical information and carry them into the network without attracting consideration. Covert channels are evaluated with three different criteria including capacity, robustness, and insensitivity. Also, the methods to deal with it include removing, limiting, and diagnosing the channel. By definition, covert channels are used to realize covert communication. Steganography methods are safe if the stego has no detectable signatures. Put differently, the stego statistical properties (audio, image, and video) should be similar to the cover properties. The ability to discover the message in the stego depends on the length of the hidden message. The proposed method in this study is to consider the amount of redundant information in a repository, explore the discoverable signs of the covert channel, and check the packet length in the checksum field in the UDP protocol at the transmission layer.

An Image Dehazing Algorithm for Underground Coal Mines Based on gUNet.

Aiming at the problems of incomplete dehazing, color distortion, and loss of detail and edge information encountered by existing algorithms when processing images of underground coal mines, an image dehazing algorithm for underground coal mines, named CAB CA DSConv Fusion gUNet (CCDF-gUNet), is proposed. First, Dynamic Snake Convolution (DSConv) is introduced to replace traditional convolutions, enhancing the feature extraction capability. Second, residual attention convolution blocks are constructed to simultaneously focus on both local and global information in images. Additionally, the Coordinate Attention (CA) module is utilized to learn the coordinate information of features so that the model can better capture the key information in images. Furthermore, to simultaneously focus on the detail and structural consistency of images, a fusion loss function is introduced. Finally, based on the test verification of the public dataset Haze-4K, the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are 30.72 dB, 0.976, and 55.04, respectively, and on a self-made underground coal mine dataset, they are 31.18 dB, 0.971, and 49.66, respectively. The experimental results show that the algorithm performs well in dehazing, effectively avoids color distortion, and retains image details and edge information, providing some theoretical references for image processing in coal mine surveillance videos.

Vignetting Compensation Method for CMOS Camera Based on LED Spatial Array

To solve the problem of pixel light intensity information distortion caused by camera vignetting in optical devices such as CMOS or CCD cameras, existing studies mainly focus on small spatial light fields and point light sources and adopt an integrating sphere and function model for vignetting correction, but it is not suitable for large LED optical composite display devices. Under this background, this paper innovatively proposes a camera vigneting compensation method based on an LED spatial array, independently develops a two-dimensional translation device driven by a high-precision guide rail, uses spatial array technology to obtain the brightness distribution of the corrected display screen to quantify its camera vigneting distortion characteristics, and adopts systematic mathematical operations and iterative compensation strategies. Industry standard tests show that the brightness uniformity of the display has been improved by 5.06%. The above research results have been applied to mass production and industrialization.

Spectral Characteristics Analysis of Images Matrix Masking Results

The article describes the results of a computational experiment to assess the capabilities of extracting useful information if an image masked by quasi-orthogonal matrices sent over an open channel became available to a third party. Hadamard and Mersenne matrices of symmetric and cyclic structure are considered. The results confirm the data that images masked by small-sized matrix leaves edges of the original image on the masked image. However, with an increase in the size of the masking matrix, all considered in the article matrices reliably hides the original image during visual analysis. Masking by symmetric Mersenne-Walsh matrices and cyclic Mersenne matrices based on modified M-sequences provides better spectral secrecy of masked images in comparison with Hadamard matrices. Mersenne matrices of cyclic structure, with equal sizes of the image and the masking matrix, bring the phase spectrum of the masked image to a form close in spectrum to uniform noise, which makes their use more preferable based on the considerations that the human visual system is extremely sensitive to phase-frequency distortions of the visual information.

Transmission-Constrained Consensus Over Random Graphs.

The exchange of information is a crucial factor in achieving consensus among agents. However, in real-world scenarios, nonideal information sharing is prevalent due to complex environmental conditions. Consider the information distortions (data) and stochastic information flow (media) during state transmission both caused by physical constraints, a novel model of transmission-constrained consensus over random networks is proposed in this work. The transmission constraints are represented by heterogeneous functions that reflect the impact of environmental interference in multiagent systems or social networks. A directed random graph is applied to model the stochastic information flow where every edge is connected probabilistically. Using stochastic stability theory and the martingale convergence theorem, it is demonstrated that the agent states will converge to a consensus value with probability 1, despite information distortions and randomness in information flow. Numerical simulations are presented to validate the effectiveness of the proposed model.

The prism of time: the factor of information transfer

The article is aimed at a general analysis of events and circumstances influencing the change and/ or distortion of historical information over time. The methodological basis of the article implies a set of principles and methods of cognition, the choice of which is determined by interdisciplinary approachesto the disclosure of the topic, the assigned tasks, and the specifics of the sources. In the research process, critical and comparative methods of analysis of sources of ancient information are used as well as analysis and systematization of events and circumstances influencing the information with their subsequent classification. The scientific novelty of the research is defined by the fact that, in contrast to the generally accepted analysis of individual cases of distortion of information, this study attempts to identify groups and types of events and circumstances that influenced or could influence the distortion of information throughout the entire period of its existence, and subsequently systematize and classify them as separate phenomena or factors that shall be taken into account when studying such information. The results of the research are based on the analysis of ancient information as well as on the analysis of many events and circumstances that influenced or could influence the perception of such information or its physical distortion. Given the limited time frame, this study analyzes events and circumstances that can be attributed to only one type, since all of them are associated only with the transfer of information from one medium to another (for example, when recording an oral story on a hard medium, when restoring text from the original, when editing, copying or translating text, or the other way of transferring information to a new medium). These events and circumstances are combined into a factor, which can roughly be called “the factor of information transfer”. At the same time, during the study, it was established that the influence of this factor on information mostly occurred for three reasons, namely: due to negligence, ignorance, or pretense.

Метод интеллектуального анализа текстовой информации для психиатрической диагностики

The automated depression detection system is a progressive technique in terms of improving clinical diagnosis and early medical intervention in cases where depression can have the most serious consequences, including self-harm or suicide. An innovative method of automated detection of depression based on textual data of patients is proposed. The developed method includes modern technologies such as the architecture of the recurrent neural network LSTM and various methods of text vectorization. Experiments conducted on publicly available datasets have confirmed the high efficiency and accuracy of the proposed method compared to the approaches used today. A unique feature of the method is the use of textual characteristics, which ensures the safety of the data provided by patients and eliminates their distortion. This approach not only increases the reliability of the results, but also avoids potential distortion of information in the analysis process. The developed method of automatic assessment of depression has high accuracy and does not require the presence of a doctor, which significantly increases the effectiveness of the process of identifying and assessing the level of depression. This approach can become a promising direction in the development of automated mental health support systems, reducing reaction time and providing more prompt assistance. In the future, the research will include training the model on data in Russian and further tuning of methods, as well as expanding the use of GloVe vectorization to improve contextual understanding of textual data. These steps are aimed at creating a more adapted and effective system for detecting depression in various linguistic contexts.

Tindakan Komunikatif Jurgen Habermas dan Implikasinya terhadap Podcast di Indonesia

The discourse on public communication is a crucial discussion. In today's digital era, platforms that provide digital content need to be reexamined. One digital content that is a reference for the public is Podcast. In its development, the emergence of Podcasts continues to have great significance for people's lives. The variety of information presented often has the potential to create information distortion in digital media. From this phenomenon, Jurgen Habermas' communicative action is a practical methodology that can provide valid, emancipatory and rational information. Community agreement in communicating is the most important thing in Habermas' communicative actions. However, by going through several logical conditions. The aim of this research is to answer the issue of potential information distortion and analyze the implications of Jurgen Habermas's concept of communicative action for Podcasts in Indonesia. This research uses a literature review method, so the sources used are scientific literature. The research results show that Jurgen Habermas' communicative actions can be a methodological basis for presenting valid and rational information. Information presented to the public can be accounted for, because it has passed the requirements determined by Jurgen Habermas. So, in implementing it, Podcasts must comply with validity claims, carry out argumentative communication, and make communicative action the fundamental basis for creating Podcast content.

Energy Efficient and Differentially Private Federated Learning via a Piggyback Approach

This paper aims to develop a differential private federated learning (FL) scheme with the least artificial noises added while minimizing the energy consumption of participating mobile devices. By observing that some communication efficient FL approaches and even the nature of wireless communications contribute to the differential privacy (DP) preservation of training data on mobile devices, in this paper, we propose to jointly leverage gradient compression techniques (i.e., gradient quantization and sparsification) and additive white Gaussian noises (AWGN) in wireless channels to develop a piggyback DP approach for FL over mobile devices. Even with the piggyback DP approach, information distortion caused by gradient compression and noise perturbation may slow down FL convergence, which in turn consumes more energy of mobile devices for local computing and model update communications. Thus, we theoretically analyze FL convergence and formulate an energy efficient FL optimization under piggyback DP, transmission power, and FL convergence constraints. Furthermore, we propose an efficient iterative algorithm where closed-form solutions for artificial DP noise and power control are derived. Extensive simulation and experimental results demonstrate the effectiveness of the proposed scheme in terms of energy efficiency and privacy preservation.

Study of color degradation in package prints: Analyzing kinetics with principal component analysis

AbstractColor degradation is a pressing issue in various fields like food and medicine packaging, as well as printed materials, leading to a loss or distortion of vital information. Therefore, understanding how the color of package prints evolves over time is essential for product quality assessment and effective product life cycle management. This study focuses on investigating color changes in prints on blister foils, widely used in the medicine and packaging industries. To assess fading characteristics, samples printed via the gravure process have been exposed to a Xenon Test Chamber (B‐SUN). Spectrophotometric measurements of the prints have been conducted using a spectroradiometer, and the data have been analyzed using principal component analysis (PCA). The fading nature of cyan and black prints aligns with Type IV, while magenta and yellow prints correspond to Type II, as described by Giles. Notably, cyan and black inks demonstrated resistance toward fading at the end of the exposure, with a minor exception. The investigation involved curve fitting, revealing a consistent first‐order kinetic model governing fading rates throughout the exposure time. High R2 values (0.96–0.99) and low root means square error values (0.02–0.08) are observed across all cases. While color fading involves diverse kinetics, the fading rate consistently adheres to a singular first‐order kinetic path. The study highlights that yellow ink is most sensitive to light, followed by magenta ink. These findings offer valuable insights for optimizing packaging materials and ensuring product quality in industries reliant on printed materials. By mitigating color degradation, manufacturers can enhance the longevity and appeal of their products, reinforcing consumer trust and satisfaction. Furthermore, the effective utilization of PCA as a data analysis tool offers a valuable approach for future investigations in color science and related fields.