Information Flow Analysis Research Articles

A Submonthly Scale Causal Relation between Snow Cover and Surface Air Temperature over the Autumnal Eurasian Continent

Abstract Snow cover (SC) is an important contributor to atmospheric predictability on subseasonal to seasonal time scales. This paper evaluates the submonthly scale cause-and-effect relationship between SC and surface air temperature (SAT) in Eurasia, which has been typically overlooked in previous statistical analyses of subseasonal to seasonal atmospheric predictability. We focus on the November east–west dipolar SC pattern, a dominant large-scale SC phenomenon. We use an information flow analysis, based on information theory, to infer causal relations not revealed by conventional correlation analysis. This analysis indicates a one-way causality from SAT to SC around the west pole (Europe) in boreal autumn (November), implying that SC has little influence on the time evolution of SAT. In contrast, causality from SC to SAT is significant around the east pole (the Mongolian Plateau). An atmospheric model experiment suggests that the SAT response to SC can persist for a month via snow–albedo feedback, although the response of the upper atmosphere in the model is small. Furthermore, the subseasonal hindcasts show that the contribution of SC may affect the predictability of SAT for up to four weeks around the east pole. We suggest that the geographical and climatological atmospheric conditions are favorable for generating a positive albedo feedback as a “hotspot” of SC–SAT coupling around the east pole in autumn. The agreement of our causality analysis with other analytical and modeling approaches underscores the cause-and-effect relationship between SC and SAT and its contribution to the subseasonal predictability over autumnal Eurasia.

A Transfer entropy-based methodology to analyze information flow under eyes-open and eyes-closed conditions with a clinical perspective

Studying brain dynamics under normal or pathological conditions has proven to be a challenging task, as there is no unified consensus on the best approach. In this article, we present a methodology based on Transfer Entropy to study the information flow between different brain hemispheres in healthy subjects during eyes-open (EO) and eyes-closed (EC) resting states. We used an experimental setup that mimics the technical conditions found in clinical settings and collected data sets from short records of 24 channels electroencephalogram (EEG) at a sampling rate of 65 Hz. Our methodology accounts for interhemispheric and intrahemispheric information flow analysis in both conditions and relies on 4 indexes calculated from the transfer entropy estimations between EEG channels. These indexes provide information on the number, strength, and directionality of active connections. Our results suggest an increase in information transfer in the EC condition for the alpha, beta1, and beta2 frequency bands, but no preferred direction of interhemispheric information movement under either condition. These results are consistent with previously reported studies conducted with denser EEG recordings sampled at a higher rate. In conclusion, our methodology shows a significant difference in the brain’s dynamics of information transfer between EO and EC resting states, which can also be applied to regular clinical sessions.

An Empirical Study on Information Flow Analysis Through Supply Chain Value Stream Mapping in an Automotive Industry

An Empirical Study on Information Flow Analysis Through Supply Chain Value Stream Mapping in an Automotive Industry

Obtaining Information Leakage Bounds via Approximate Model Counting

Information leaks are a significant problem in modern software systems. In recent years, information theoretic concepts, such as Shannon entropy, have been applied to quantifying information leaks in programs. One recent approach is to use symbolic execution together with model counting constraints solvers in order to quantify information leakage. There are at least two reasons for unsoundness in quantifying information leakage using this approach: 1) Symbolic execution may not be able to explore all execution paths, 2) Model counting constraints solvers may not be able to provide an exact count. We present a sound symbolic quantitative information flow analysis that bounds the information leakage both for the cases where the program behavior is not fully explored and the model counting constraint solver is unable to provide a precise model count but provides an upper and a lower bound. We implemented our approach as an extension to KLEE for computing sound bounds for information leakage in C programs.

Protein Is an Intelligent Micelle.

Interpreting biological phenomena at the molecular and cellular levels reveals the ways in which information that is specific to living organisms is processed: from the genetic record contained in a strand of DNA, to the translation process, and then to the construction of proteins that carry the flow and processing of information as well as reveal evolutionary mechanisms. The processing of a surprisingly small amount of information, i.e., in the range of 1 GB, contains the record of human DNA that is used in the construction of the highly complex system that is the human body. This shows that what is important is not the quantity of information but rather its skillful use-in other words, this facilitates proper processing. This paper describes the quantitative relations that characterize information during the successive steps of the "biological dogma", illustrating a transition from the recording of information in a DNA strand to the production of proteins exhibiting a defined specificity. It is this that is encoded in the form of information and that determines the unique activity, i.e., the measure of a protein's "intelligence". In a situation of information deficit at the transformation stage of a primary protein structure to a tertiary or quaternary structure, a particular role is served by the environment as a supplier of complementary information, thus leading to the achievement of a structure that guarantees the fulfillment of a specified function. Its quantitative evaluation is possible via using a "fuzzy oil drop" (FOD), particularly with respect to its modified version. This can be achieved when taking into account the participation of an environment other than water in the construction of a specific 3D structure (FOD-M). The next step of information processing on the higher organizational level is the construction of the proteome, where the interrelationship between different functional tasks and organism requirements can be generally characterized by homeostasis. An open system that maintains the stability of all components can be achieved exclusively in a condition of automatic control that is realized by negative feedback loops. This suggests a hypothesis of proteome construction that is based on the system of negative feedback loops. The purpose of this paper is the analysis of information flow in organisms with a particular emphasis on the role of proteins in this process. This paper also presents a model introducing the component of changed conditions and its influence on the protein folding process-since the specificity of proteins is coded in their structure.

Analysis of Information Flow Advertising Text in Social Media from the Perspective of Semiotics: A Case Study of Tiktok App

In view of the development trend of Internet technology, information flow advertising has gradually attracted more scholars' attention based on better communication effect and user experience. Tiktok platform has high user stickiness and strong media potential. Therefore, this paper mainly applies the semiotics theory to the analysis of Tiktok information flow advertising text to explore the transformation and conflict of advertising narrative, deconstruction of advertising visual performance and other aspects.

Application of Multilevel Models in Classification and Regression Problems

There is a constant need to create methods for improving the quality indicators of information processing. In most practical cases, the ranges of target variables and predictors are formed under the influence of external and internal factors. Phenomena such as concept drift cause the model to lose its completeness and accuracy over time. The purpose of the work is to improve the processing data samples quality based on multi-level models for classification and regression problems. A two-level data processing architecture is proposed. At the lower level, the analysis of incoming information flows and sequences takes place, and the classification or regression tasks are solved. At the upper level, the samples are divided into segments, the current data properties in the subsamples are determined, and the most suitable lower-level models are assigned according to the achieved qualitative indicators. A formal description of the two-level architecture is given. In order to improve the quality indicators for classification and regression solving problems, a data sample preliminary processing is carried out, the model’s qualitative indicators are calculated, and classifiers with the best results are determined. The proposed solution makes it possible to implement constantly learning data processing systems. It is aimed at reducing the time spent on retraining models in case of data properties transformation. Experimental studies were carried out on several datasets. Numerical experiments have shown that the proposed solution makes it possible to improve the quality processing indicators. The model can be considered as an improvement of ensemble methods for processing information flows. Training a single classifier, rather than a group of complex classification models, makes it possible to reduce computational costs.

Binsec/Rel: Symbolic Binary Analyzer for Security with Applications to Constant-Time and Secret-Erasure

This article tackles the problem of designing efficient binary-level verification for a subset of information flow properties encompassing constant-time and secret-erasure . These properties are crucial for cryptographic implementations but are generally not preserved by compilers. Our proposal builds on relational symbolic execution enhanced with new optimizations dedicated to information flow and binary-level analysis, yielding a dramatic improvement over prior work based on symbolic execution. We implement a prototype, Binsec/Rel , for bug-finding and bounded-verification of constant-time and secret-erasure and perform extensive experiments on a set of 338 cryptographic implementations, demonstrating the benefits of our approach. Using Binsec/Rel , we also automate two prior manual studies on preservation of constant-time and secret-erasure by compilers for a total of 4,148 and 1,156 binaries, respectively. Interestingly, our analysis highlights incorrect usages of volatile data pointer for secret-erasure and shows that scrubbing mechanisms based on volatile function pointers can introduce additional register spilling that might break secret-erasure. We also discovered that gcc -O0 and backend passes of clang introduce violations of constant-time in implementations that were previously deemed secure by a state-of-the-art constant-time verification tool operating at LLVM level, showing the importance of reasoning at binary level.

Analysis of Respiratory Sinus Arrhythmia and Directed Information Flow between Brain and Body Indicate Different Management Strategies of fMRI-Related Anxiety

Respiratory sinus arrhythmia (RSA) denotes decrease of cardiac beat-to-beat intervals (RRI) during inspiration and RRI increase during expiration, but an inverse pattern (termed negative RSA) was also found in healthy humans with elevated anxiety. It was detected using wave-by-wave analysis of cardiorespiratory rhythms and was considered to reflect a strategy of anxiety management involving the activation of a neural pacemaker. Results were consistent with slow breathing, but contained uncertainty at normal breathing rates (0.2-0.4 Hz). We combined wave-by-wave analysis and directed information flow analysis to obtain information on anxiety management at higher breathing rates. We analyzed cardiorespiratory rhythms and blood oxygen level-dependent (BOLD) signals from the brainstem and cortex in 10 healthy fMRI participants with elevated anxiety. Three subjects with slow respiratory, RRI, and neural BOLD oscillations showed 57 ± 26% negative RSA and significant anxiety reduction by 54 ± 9%. Six participants with breathing rate of ~0.3 Hz showed 41 ± 16% negative RSA and weaker anxiety reduction. They presented significant information flow from RRI to respiration and from the middle frontal cortex to the brainstem, which may result from respiration-entrained brain oscillations, indicating another anxiety management strategy. The two analytical approaches applied here indicate at least two different anxiety management strategies in healthy subjects.

Identifying Security and Privacy Violation Rules in Trigger-Action IoT Platforms With NLP Models

Trigger-action platforms are systems that enable users to easily define, in terms of conditional rules, custom behaviors concerning Internet of Things (IoT) devices and Web services. Unfortunately, although these tools stimulate the creativity of users in building automation, they may also introduce serious risks for the users. Indeed, trigger–action rules can lead to the possibility of users harming themselves, for example, by unintentionally disclosing nonpublic information, or unwillingly exposing their smart environment to cyber-threats. In this article, we propose to use natural language processing (NLP) techniques to detect automation rules, defined within trigger–action IoT platforms, that potentially violate the security or privacy of the users. The proposed NLP-based models capture the semantic and contextual information of the trigger-action rules by applying classification techniques to different combinations of rule’s features. We evaluate the proposed solution with the mainstream trigger-action platform, namely, If-This-Then-That, by training the NLP models with a data set of 76 741 rules labeled by using an ensemble of three semi-supervised learning techniques. The experimental results demonstrate that the model based on bidirectional encoder representations from transformers (BERTs) obtains the highest performances when trained on all features, achieving average Precision and Recall values between 88% and 93%. We also compare the achieved performances with those of a baseline system implementing information flow analysis.

Declarative Secure Placement of FaaS Orchestrations in the Cloud-Edge Continuum

The decision-making related to the placement of applications made from orchestrated serverless functions onto Cloud-Edge infrastructures is a challenging problem as it must consider functional and non-functional requirements. In this article, we propose a novel declarative methodology to determine the placement of FaaS orchestration onto Cloud-Edge resources while satisfying all the requirements of the FaaS orchestrations and relying on information-flow analyses and padding techniques to prevent information leaks through side channels. A motivating example from Augmented Reality is used to showcase the open-source declarative prototype implementing our proposal. Besides, the prototype is assessed via simulation to evaluate execution times, placement success rates and energy consumption.

μDep: Mutation-Based Dependency Generation for Precise Taint Analysis on Android Native Code

The existence of native code in Android apps plays an important role in triggering inconspicuous propagation of secrets and circumventing malware detection. However, the state-of-the-art information-flow analysis tools for Android apps all have limited capabilities of analyzing native code. Due to the complexity of binary-level static analysis, most static analyzers choose to build conservative models for a selected portion of native code. Though the recent inter-language analysis improves the capability of tracking information flow in native code, it is still far from attaining similar effectiveness of the state-of-the-art information-flow analyzers that focus on non-native Java methods. To overcome the above constraints, we propose a new analysis framework, <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> Dep, to detect sensitive information flows of the Android apps containing native code. In this framework, we combine a control-flow based static binary analysis with a mutation-based dynamic analysis to model the tainting behaviors of native code in the apps. Based on the result of the analyses, <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> Dep conducts a stub generation for the related native functions to facilitate the state-of-the-art analyzer DroidSafe with fine-grained tainting behavior summaries of native code. The experimental results show that our framework is competitive on the accuracy, and effective in analyzing the information flows in real-world apps and malware compared with the state-of-the-art inter-language static analysis.

Восприятие имиджа нового лидера политической партии ЛДПР российскими пользователями социальных медиа

The article presents the results of an assessment of the perception by Russian users of the image of the LDPR leader Leonid Slutsky in the social media space in 2022 after the death of the founder of the party Vladimir Zhirinovsky. Based on the data obtained in the course of cognitive mapping, automated social media analysis of information flows and event analysis, a number of weaknesses in the positioning of Leonid Slutsky as a party leader were identified, including the absence of real informational reasons, low user resonance, a small share of information occasions directly addressed to the public. The authors reveal that, first of all, Leonid Slutsky is not perceived by users as the leader of the Liberal Democratic Party. The stability of the image of Vladimir Zhirinovsky plays an important role by in the minds of the population. The article offers recommendations for leveling each of the noted risks.

Fairness of Information Flow in Social Networks

Social networks form a major parts of people’s lives, and individuals often make important life decisions based on information that spreads through these networks. For this reason, it is important to know whether individuals from different protected groups have equal access to information flowing through a network. In this article, we define the Information Unfairness (IUF) metric, which quantifies inequality in access to information across protected groups. We then introduce MinIUF , an algorithm for reducing inequalities in information flow by adding edges to the network. Finally, we provide an in-depth analysis of information flow with respect to an attribute of interest, such as gender, across different types of networks to evaluate whether the structure of these networks allows groups to equally access information flowing in the network. Moreover, we investigate the causes of unfairness in such networks and how it can be improved.

Dauer fate in a Caenorhabditis elegans Boolean network model.

Cellular fates are determined by genes interacting across large, complex biological networks. A critical question is how to identify causal relationships spanning distinct signaling pathways and underlying organismal phenotypes. Here, we address this question by constructing a Boolean model of a well-studied developmental network and analyzing information flows through the system. Depending on environmental signals Caenorhabditis elegans develop normally to sexual maturity or enter a reproductively delayed, developmentally quiescent 'dauer' state, progressing to maturity when the environment changes. The developmental network that starts with environmental signal and ends in the dauer/no dauer fate involves genes across 4 signaling pathways including cyclic GMP, Insulin/IGF-1, TGF-β and steroid hormone synthesis. We identified three stable motifs leading to normal development, each composed of genes interacting across the Insulin/IGF-1, TGF-β and steroid hormone synthesis pathways. Three genes known to influence dauer fate, daf-2, daf-7 and hsf-1, acted as driver nodes in the system. Using causal logic analysis, we identified a five gene cyclic subgraph integrating the information flow from environmental signal to dauer fate. Perturbation analysis showed that a multifactorial insulin profile determined the stable motifs the system entered and interacted with daf-12 as the switchpoint driving the dauer/no dauer fate. Our results show that complex organismal systems can be distilled into abstract representations that permit full characterization of the causal relationships driving developmental fates. Analyzing organismal systems from this perspective of logic and function has important implications for studies examining the evolution and conservation of signaling pathways.

Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis.

Autism spectrum disorder (ASD) is a pervasive developmental disorder with severe cognitive impairment in social communication and interaction. Previous studies have reported that abnormal functional connectivity patterns within the default mode network (DMN) were associated with social dysfunction in ASD. However, how the altered causal connectivity pattern within the DMN affects the social functioning in ASD remains largely unclear. Here, we introduced the Liang information flow method, widely applied to climate science and quantum mechanics, to uncover the brain causal network patterns in ASD. Compared with the healthy controls (HC), we observed that the interactions among the dorsal medial prefrontal cortex (dMPFC), ventral medial prefrontal cortex (vMPFC), hippocampal formation, and temporo-parietal junction showed more inter-regional causal connectivity differences in ASD. For the topological property analysis, we also found the clustering coefficient of DMN and the In-Out degree of anterior medial prefrontal cortex were significantly decreased in ASD. Furthermore, we found that the causal connectivity from dMPFC to vMPFC was correlated with the clinical symptoms of ASD. These altered causal connectivity patterns indicated that the DMN inter-regions information processing was perturbed in ASD. In particular, we found that the dMPFC acts as a causal source in the DMN in HC, whereas it plays a causal target in ASD. Overall, our findings indicated that the Liang information flow method could serve as an important way to explore the DMN causal connectivity patterns, and it also can provide novel insights into the nueromechanisms underlying DMN dysfunction in ASD.

Applying the Wilson Methodology to Improve Process Management. Case. Engineering Services Company

Knowledge management and information flow analysis are vital elements that can improve an organization's operations. Therefore, in this paper, the Wilson methodology will be applied. By using the recommended management, it would imply a strategic alignment, guaranteeing that all processes with their respective information flow in the same direction within the company, meeting business objectives, and allowing adapting the use of different strategic analysis tools, in such a way that it provides a benefit adjusted to each process, with a greater sensitivity in all employees aimed at the continuous improvement of the company

The Impact of Artificial Intelligence on News Production and Communication in the Smart Media Era

Modernization of the media and the advent of artificial intelligence have led to significant changes in the process of news production. AI plays an important role in improving the efficiency and accuracy of reporting. With the help of machine learning algorithms, AI can independently study and analyze information flows, automatically analyze and select the most relevant information. This allows journalists to save time searching and verifying data, while also providing accurate and timely information. With the development of AI, algorithms for automatic content creation were created. This is useful in cases where events such as fires or earthquakes need to be reported quickly and there is no time to wait for manual documentation. However, AI cannot completely replace humans in creating stories because it lacks love and aesthetic taste. AI algorithms can automatically check facts and structured information to create smarter, more readable content. This will improve the quality of the article and increase readers' confidence in the information. The use of artificial intelligence in journalism is an important part of modern journalism. The more efficient and accurate the reporting, the better the information for readers. It is also necessary to adhere to ethical and legal standards when using AI to avoid manipulation and falsification of information. Thus, artificial intelligence has a significant impact on the production and delivery of information, increasing efficiency and accuracy. However, ethical and legal considerations must be taken into account when using AI in communications.

Development of algorithms for optimizing business processes of interaction with consumers in a gas distribution organization

The results of a detailed analysis of the main information flows in a gas distribution company and the features of workflow automation when working with clients are presented. Algorithms for optimizing business processes based on the implementation of an IT-project have been developed. The proposed algorithms for interaction with clients and ways of organizing the company’s information space will improve the quality of customer service and the efficiency of organization management. Keywords data optimization, information flow, information technology, business process, algorithm, gas distribution organization, implementation efficiency, IT-project

ПРОГНОЗУВАННЯ ЧАСОВИХ РЯДІВ СОЦІАЛЬНИХ МЕРЕЖ НА ОСНОВІ НЕЙРОМЕРЕЖЕВОГО ПІДХОДУ

The article presents a predictive model for analyzing the state and behavior of social network objects in dynamics and for building predictions of user behavior. This opens up opportunities for choosing the most promising promotion of objects in social networks, the best placement of advertising, the possibility of creating independent projects. The article proposes an approach to the formalization of the processes of growth/decrease in the popularity of content under the influence of external factors within the framework of a mathematical predictive model based on a recurrent neural network. The purpose of the model is mathematical processing and analysis of stochastic information flows, graphical presentation of forecasts of music trends, changes in the popularity of bloggers, video content, and more. The developed model can be used to predict the growth of reposts in social networks. The list of external and internal variables that affect the behavior of the output data at each moment of time is described. The behavior of time series is studied on the basis of input data obtained from social networks (nomenclature of indicators, number of reposts of researched objects, simulation period). A feature of the model is the ability to work with Big Data arrays, which in most cases have incomplete or "noisy" information. The dependence between input and output is proposed to be specified using a system of constraints and external coefficients that change over time. Control parameters are predictors of the model. Features of building a predictive model are shown, a list of the main limitations of the mathematical model is described. The result of the implementation of the tasks set in the research is a dynamic predictive model that provides an opportunity to create predictions based on such indicators as reposts on social networks, music trends, and the popularity of bloggers. It is proposed to use a recurrent artificial network with a dynamic neuron to predict reposts on selected research objects. The neuron processes input signals and calculates a predictive indicator of the number of reposts. The learning process of the neural network during the simulation period of one month is demonstrated. To reveal the prospects of further development of the model in order to improve the accuracy of forecasts of socio-economic processes.