Indirect Dependencies Research Articles

Conditional cross-map-based technique: From pairwise dynamical causality to causal network reconstruction.

Causality detection methods based on mutual cross mapping have been fruitfully developed and applied to data originating from nonlinear dynamical systems, where the causes and effects are non-separable. However, these pairwise methods still have shortcomings in discriminating typical network structures, including common drivers, indirect dependencies, and facing the curse of dimensionality, when they are stepping to causal network reconstruction. A few endeavors have been devoted to conquer these shortcomings. Here, we propose a novel method that could be regarded as one of these endeavors. Our method, named conditional cross-map-based technique, can eliminate third-party information and successfully detect direct dynamical causality, where the detection results can exactly be categorized into four standard normal forms by the designed criterion. To demonstrate the practical usefulness of our model-free, data-driven method, data generated from different representative models covering all kinds of network motifs and measured from real-world systems are investigated. Because correct identification of the direct causal links is essential to successful modeling, predicting, and controlling the underlying complex systems, our method does shed light on uncovering the inner working mechanisms of real-world systems only using the data experimentally obtained in a variety of disciplines.

Toward random walk-based clustering of variable-order networks

AbstractHigher-order networks aim at improving the classical network representation of trajectories data as memory-less order $1$ Markov models. To do so, locations are associated with different representations or “memory nodes” representing indirect dependencies between visited places as direct relations. One promising area of investigation in this context is variable-order network models as it was suggested by Xu et al. that random walk-based mining tools can be directly applied on such networks. In this paper, we focus on clustering algorithms and show that doing so leads to biases due to the number of nodes representing each location. To address them, we introduce a representation aggregation algorithm that produces smaller yet still accurate network models of the input sequences. We empirically compare the clustering found with multiple network representations of real-world mobility datasets. As our model is limited to a maximum order of $2$ , we discuss further generalizations of our method to higher orders.

Fast Format-Aware Fuzzing for Structured Input Applications

Fuzzing is one of the most successful software testing techniques used to discover vulnerabilities in programs. Without seeds that fit the input format, existing runtime dependency recognition strategies are limited by incompleteness and high overhead. In this paper, for structured input applications, we propose a fast format-aware fuzzing approach to recognize dependencies from the specified input to the corresponding comparison instruction. We divided the dependencies into Input-to-State (I2S) and indirect dependencies. Our approach has the following advantages compared to existing works: (1) recognizing I2S dependencies more completely and swiftly using the input based on the de Bruijn sequence and its mapping structure; (2) obtaining indirect dependencies with a light dependency existence analysis on the input fragments. We implemented a fast format-aware fuzzing prototype, FFAFuzz, based on our method and evaluated FFAFuzz in real-world structured input applications. The evaluation results showed that FFAFuzz reduced the average time overhead by 76.49% while identifying more completely compared with Redqueen and by 89.10% compared with WEIZZ. FFAFuzz also achieved higher code coverage by 14.53% on average compared to WEIZZ.

An Algorithm for Mining Indirect Dependencies From Loop-Choice-Driven Loop Structure via Petri Nets

Process mining is an emerging technology used to extract, detect, and improve actual processes by extracting knowledge from event logs generated from information systems. In the production process, we can obtain the optimal process based on practical experience. Indirect dependencies may exist among different structures in the optimal process model discovered from the event log of the executions that perform better. However, the existing process mining algorithms cannot effectively mine the indirect dependencies among different structures. To compensate for this deficiency, an algorithm named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AlphaID</i> is proposed in this article, and it can mine the indirect dependencies in a loop-choice-driven loop structure. First, two algorithms are proposed to efficiently identify loop sequences and choice sequences from event logs. Then, the concept of association rules is proposed to describe indirect dependencies among different structures. Next, we expand the ordinary Petri net and redefine the new transition firing rules to represent the process model obtained by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AlphaID</i> . Finally, the correctness and effectiveness of the algorithm are verified by an artificial case and a real case. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AlphaID</i> is integrated into the ProM which is an open-source process mining tool platform as a plug-in.

Resilience analysis of infrastructure systems in incremental design change

Disruptions to infrastructure systems can affect many facets of our daily life. Despite best efforts to design infrastructure systems that are resilient to disruptions, many systems are often modified over time to meet changing future needs. Hence, this paper presents a modelling method that aims to provide early estimates of system resilience in the redesign and extension of existing infrastructure systems. The method considers both direct and indirect functional dependencies between system entities and quantifies system resilience from a functional recovery perspective. A case study was carried out and the findings suggest that the method is feasible to support resilience analysis in the evaluation of design options during the incremental change of infrastructure systems.

On the impact of security vulnerabilities in the npm and RubyGems dependency networks

The increasing interest in open source software has led to the emergence of large language-specific package distributions of reusable software libraries, such as npm and RubyGems. These software packages can be subject to vulnerabilities that may expose dependent packages through explicitly declared dependencies. Using Snyk’s vulnerability database, this article empirically studies vulnerabilities affecting npm and RubyGems packages. We analyse how and when these vulnerabilities are disclosed and fixed, and how their prevalence changes over time. We also analyse how vulnerable packages expose their direct and indirect dependents to vulnerabilities. We distinguish between two types of dependents: packages distributed via the package manager, and external GitHub projects depending on npm packages. We observe that the number of vulnerabilities in npm is increasing and being disclosed faster than vulnerabilities in RubyGems. For both package distributions, the time required to disclose vulnerabilities is increasing over time. Vulnerabilities in npm packages affect a median of 30 package releases, while this is 59 releases in RubyGems packages. A large proportion of external GitHub projects is exposed to vulnerabilities coming from direct or indirect dependencies. 33% and 40% of dependency vulnerabilities to which projects and packages are exposed, respectively, have their fixes in more recent releases within the same major release range of the used dependency. Our findings reveal that more effort is needed to better secure open source package distributions.

Understanding information diffusion with psychological field dynamic

Several approaches focus on how to automatically capture the latent features from original diffusion data and predict the future scale of cascades utilizing a black box framework. However, they ignore the penetrating insight into the underlying mechanism that how each participant is involved in the cascade. In this work, we bridge the gap between prediction and understanding of information diffusion by incorporating deep learning techniques and social psychology. To characterize individual participation driven by both subjective and objective impetus and integrate it into the macro-level cascade, we propose an end-to-end model, named PFDID, which is designed based on the field dynamics theory of psychology, including the intrinsic cognition field and the extrinsic environment field. We represent these two field dynamics respectively with the pairwise semantic relation between the message itself and corresponding comment and the forwarder’s micro-community activity embedding to provide educated explanations for forwarding behaviour. Afterwards, the cross infusion mechanism is designed to calculate the mutual influence of inhomogeneous field dynamics inside users and cross influence of homogeneous field dynamics among individuals, whose output is fed into the diffusion network aggregation layer for the cascade size prediction. Extensive experiments on two typical social networks, Sina Weibo and Twitter, manifest that the proposed PFDID outperforms state-of-the-art approaches. Our model achieves excellent prediction results, with MSLE = 1.856 on Sina Weibo and MSLE = 1.962 on Twitter, providing 6.54% and 10.53% relative performance gains, respectively. Furthermore, the interpretability is also discussed based on detailed visualization. We observe that the psychological impetus behind social behaviour varies mainly following two patterns with the spread of information, including gradual change and joint influence. Additionally, the indirect dependencies have also been verified.

Temporal and spatial differences of the under-ice microbiome are linked to light transparency and chlorophyll-a

Under-ice community dynamics are barely understood. Temporal and spatial studies are needed to fully understand the consequences of a declining ice cover on microbial biodiversity. Here, bacterial communities of different years (2015, 2017–2021) and layers (upper and lower euphotic layer, euphotic layer, hypolimnion) were assessed by Illumina sequencing of the 16S rRNA gene. Alpha- and beta-diversity of summer and under-ice hypolimnetic communities were similar, and a seasonal difference was found only when excluding summer hypolimnetic communities. Similarly, in non-metric multidimensional scaling (NMDS), summer and under-ice communities were different even though hypolimnetic communities were similar. Investigating under-ice conditions, the year 2017 showed highest under-ice light and chlorophyll-a while 2021 showed no under-ice light and lowest chlorophyll-a. Under-ice communities were not linked to layer differences implying that a spatial distinction under ice was less important than in summer, especially in years with little or no under-ice light. Most under-ice bacterial classes and ASVs showed direct and indirect dependencies on light availability and primary production. Similarly in NMDS with only under-ice communities, light transparency and primary production were important. In the future, ice conditions with less snow cover might lead to bacterial communities similar to that of high-light years (2017, 2018, 2020).

Automated Repair of Process Models with Non-local Constraints Using State-Based Region Theory

State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.

MANAGEMENT OF THE MARKETING STRATEGY OF THE COMPANY BASED ON PARTNERSHIP RELATIONS

The study of scientific and methodological approaches to the formation of partnership relations revealed that the distribution of marketing functions can lead to mismatch between the entities of the network organization, and creation of super-network structures is, in fact, the initial step of integration, which is contrary to the principles of marketing partnership relations. A model of interaction management has been substantiated, it involves highlighting of the integrator-company, which has the highest degree of influence on the channel participants. It allows to establish deep flexible partnerships between partners with saving the autonomy of their business activities. It is substantiated that the integrator should be interpreted as the channel member company, which has the highest degree of influence on other participants, calculated through the determinants of the degrees of direct and indirect dependencies between them. The main functions of the integrator- company are to conduct descriptive marketing research to identify factors of external environment influencing the formation of company interaction strategies and to analyze effectiveness of their partnerships. It allows to ensure additional benefits for the members of the partnership.

The Structure of the Relationship of Physical Activity and Selected Factors Shaping the Condition and Self-assessment of Health of Patients with the Lumbar Spine Pain Syndrome

Introduction. The pain syndrome of the lumbar spine is one of the most common civilization diseases and requires surgery in the case of a significant percentage of patients. It is also associated with a number of other health problems, with gender being an important factor that differentiates patients’ health.Aim. For this reason, our own research was focused on determining the structure of the relationship between physical activity and selected factors shaping the condition and self-assessment of health of patients in the lumbar spine pain syndrome depending on gender.Material and Methods. The study sample consists of 205 patients hospitalized at the Department of Pediatric Neurosurgery and Neurosurgery of the Independent Public Clinical Hospital No. 4 in Lublin and at the Department of Pediatric Neurosurgery, Neurotraumatology and Neurosurgery, University Hospital No. 1 in Bydgoszcz. The Research used the Physical Activity Questionnaire IPAQ in Polish version, Visual Analogue Scale VAS, Questionnaire for Depression Measurement KPD, a proprietary questionnaire, as well as the results of laboratory tests of glucose concentration in the blood serum. The analysis of multidimensional relations between the variables was performed using the SEM structural equation modeling using the IBM SPSS 25 program with the AMOS extension.Results. In the subgroups of women and men, models that were structurally similar, although different in the values of selected path coefficients, were developed, which explains the variances of: blood glucose concentration, BMI, intensity of depression symptoms and self-assessment of health condition. The developed structural model shows that physical activity with mediating participation of blood glucose concentration, BMI and depression is associated with self-assessment of health. The indirect dependencies of physical activity and self-assessment of health condition also occur with the mediating participation of the BMI index and blood glucose concentration, as well as the BMI index and depression.Conclusions. The obtained data indicate that in the absence of health contraindications, an important element of interdisciplinary programs supporting the condition and self-assessment of health of patients with lumbar spine pain syndrome may be regular physical activity, adjusted to the needs and capabilities of the body. The differences between men and women suggest that preventive interventions should be gender-specific. (JNNN 2021;10(4):153–161)

The dependency of autophagy and ubiquitin proteasome system during skeletal muscle atrophy.

Among the four proteolytic systems in the cell, autophagy and the ubiquitin-proteasome system (UPS) are the main proteolytic events that allow for the removal of cell debris and proteins to maintain cellular homeostasis. Previous studies have revealed that these systems perform their functions independently of each other. However, recent studies indicate the existence of regulatory interactions between these proteolytic systems via ubiquitinated tags and a reciprocal regulation mechanism with several crosstalk points. UPS plays an important role in the elimination of short-lived/soluble misfolded proteins, whereas autophagy eliminates defective organelles and persistent insoluble protein aggregates. Both of these systems seem to act independently; however, disruption of one pathway affects the activity of the other pathway and contributes to different pathological conditions. This review summarizes the recent findings on direct and indirect dependencies of autophagy and UPS and their execution at the molecular level along with the important drug targets in skeletal muscle atrophy.

Identification of dependencies between product parameters and process stakeholders

In today’s mechanical engineering companies, the rising interdisciplinarity of processes and development teams as well as distributed company locations leads to increased production times and more complex product development processes (PDP), although shorter development times are requested. Especially changes, e.g. component optimization or changed customer requirements, as well as parallel sub-processes, require increased planning to make dependencies visible and estimate expenses.When requesting a change to e.g. a component property, it is difficult to identify on the one hand which processes and technical parameters are affected and on the other hand which stakeholders have to be included in the decision making. It is not yet state-of-the art that stakeholders affected by a change in the PDP are identified and notified automatically by the IT-system. Furthermore, it is challenging to identify indirectly connected stakeholders, which are not linked to changing properties or processes, concerning the changes made.In this paper, a methodological approach is presented, that links the technical parameters with the processes and the different stakeholders, using the example of a PDP for light weight composite components.The procedure comprises the implementation of the PDP for light weight composite, including the process-dependent roles, in a product data management (PDM) system. The process structure is then linked to a Model-Based Systems Engineering (MBSE) tool. By building a system model, the product parameters are linked to the processes and roles. Using the established system model, the direct and indirect dependencies between the technical parameters and the stakeholders are visualized.

Full Domestic Supply Chains of Blue Virtual Water Flows Estimated for Major U.S. Cities

AbstractThrough the trade of products and services, cities indirectly depend on distant water sources to function, prosper, and grow. To fully account for indirect (virtual) water dependencies, virtual water flows need to be known along complex supply chains. To this purpose, we build a new environmental multiregional input–output model for U.S. regions. The model is used to quantify the domestic, blue virtual water flows and analyze the water footprints of 69 major U.S. cities. Our results show a large inequality in the urban water consumed for economic production: just 7 out of the 69 cities included in this study account for 35% of the U.S. national water footprint of production. This is due to the production of water‐intensive agricultural products in the metropolitan areas of western cities. The inequality reduces for the urban water footprint of consumption because, through the supply chains of industrialized food sectors, western virtual water is partially transferred to eastern cities as final demand. The water embodied in industrial products and services tends to be higher in western cities than in eastern cities; that is, the water embodied in food services could be several times higher in Los Angeles than in New York City. Trade hub cities attract large inflows of products which are mostly transformed for consumption elsewhere. Thus, the omission of product interdependencies within trade hub cities can increase by several times their water footprints of consumption. Overall, the proposed model is able to enhance subnational estimates of U.S. virtual water flows.

Quantifying Risk Propagation Within a Network of Business Processes and IT Services

Nowadays, the organic nature of business processes and the increasingly complex and dynamic business environment make organizations face severe operational risks. However, current risk analysis methods of Information Technology (IT) resources ignore inter-process correlation and thus inter-process risk propagation. This gap needs a solution since the rigid alignment of organizations cause the risks which propagate throughout the whole organization to be the most serious operational risks. This paper presents a holistic approach for quantifying risk propagation in business processes based on the risk analysis of their underlying IT and human resources. This approach adapts financial techniques to quantify the level of risk that average and severe events on IT resources generate on individual business processes, and to quantify the risk propagation impact among dependent processes. This approach was applied to an enterprise modeling case study to quantify risk propagation for different risk epicenter scenarios. The results show that the proposed approach is capable of finding and quantifying both direct and indirect dependencies among operational assets within an organization. A high level of accuracy was observed when comparing the actual value of the process risk and the projected value considering risk propagation.

A proposed java forward slicing approach

Many organization, programmers, and researchers need to debug, test and make maintenance for a segment of their source code to improve their system. Program slicing is one of the best techniques to do so. There are many slicing techniques available to solve such problems such as static slicing, dynamic slicing, and amorphous slicing. In our paper, we decided to develop a tool that supports many slicing techniques. Our proposed tool provides new flexible ways to process simple segments of Java code, and it generates needed slicing according to the user needs, our tool will provide the user with direct and indirect dependencies for each variable in the code segments. This tool can work under various operating systems and does not need particular environments. Thus, our tool is helpful in many aspects such as debugging, testing, education, and many other elements.

Evaluating the trustworthiness of BPEL processes based on data dependency and XBFG

Composite services implement value-added functionality by composing service components of various granularities. Trust is an important criterion to judge whether a composite service can behave as expected. There is a great need for a flexible trust evaluation method for composite services. In this paper, a data dependency-based trust evaluation approach for composite services is proposed. Firstly, we derive define-use pairs of variables to identify data dependency between service components in BPEL processes modelled by an extensible BPEL flow graph (XBFG); then, dependency links including both direct and indirect data dependencies are used to evaluate the trust of these service components; furthermore, on the basis of BPEL structure and XBFG, reduction rules are proposed to evaluate the global trust of BPEL processes. Experimental results demonstrate that the proposed approach is effective for the trust evaluation of BPEL composite services and stable with the growing number of service components in BPEL.

Evaluating the trustworthiness of BPEL processes based on data dependency and XBFG

Composite services implement value-added functionality by composing service components of various granularities. Trust is an important criterion to judge whether a composite service can behave as expected. There is a great need for a flexible trust evaluation method for composite services. In this paper, a data dependency-based trust evaluation approach for composite services is proposed. Firstly, we derive define-use pairs of variables to identify data dependency between service components in BPEL processes modelled by an extensible BPEL flow graph (XBFG); then, dependency links including both direct and indirect data dependencies are used to evaluate the trust of these service components; furthermore, on the basis of BPEL structure and XBFG, reduction rules are proposed to evaluate the global trust of BPEL processes. Experimental results demonstrate that the proposed approach is effective for the trust evaluation of BPEL composite services and stable with the growing number of service components in BPEL.

Well-Governed Sustainability and Financial Performance: A New Integrative Approach

This study investigates the interlinkages between the dimensions of corporate social responsibility (CSR-economic, environmental, social), financial performance (ROA, ROE), and corporate governance (CG), by applying the structural equation modeling technique (SEM). It is based on a sample of 614 large companies from the European Economic Area, covering specific indicators published by the Thomson Reuters database, for the years 2013–2017. The equation models are structured starting from isolated dependencies between variables, up to the global ones (direct, indirect, and total dependencies). The mixed results obtained imply that the nature and heterogenous content of CSR lead to different statistical dependencies for each of the two financial performance indicators. ROA is positively influenced by the economic dimension of CSR, but, the level of this rate does not necessarily contribute to an increase in the involvement of the company in this type of CSR actions. At the same time, ROA is influenced and affects in a negative way the environmental and social dimensions of CSR. In the case of ROE, it is influenced and impacts the economic and social dimensions in a positive way. The environmental dimension of CSR influences ROE positively, but it is negatively affected by this profitability rate. Corporate governance exerts a positive impact on all of the model’s variables, both as a direct and indirect factor of influence.

Causal network reconstruction from time series: From theoretical assumptions to practical estimation.

Causal network reconstruction from time series is an emerging topic in many fields of science. Beyond inferring directionality between two time series, the goal of causal network reconstruction or causal discovery is to distinguish direct from indirect dependencies and common drivers among multiple time series. Here, the problem of inferring causal networks including time lags from multivariate time series is recapitulated from the underlying causal assumptions to practical estimation problems. Each aspect is illustrated with simple examples including unobserved variables, sampling issues, determinism, stationarity, nonlinearity, measurement error, and significance testing. The effects of dynamical noise, autocorrelation, and high dimensionality are highlighted in comparison studies of common causal reconstruction methods. Finally, method performance evaluation approaches and criteria are suggested. The article is intended to briefly review and accessibly illustrate the foundations and practical problems of time series-based causal discovery and stimulate further methodological developments.