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A cloud computing environment is a distributed system where idle resources are accessible across a wide area network, such as the Internet. Due to the diverse specifications of these resources, computational clouds exhibit high heterogeneity. Task scheduling, the process of dispatching cloud applications onto processing nodes, becomes a critical challenge in such environments. Ensuring high utilization in this heterogeneous environment entails identifying suitable machines or virtual machines capable of efficiently executing jobs, constituting a multi-objective optimization problem. This paper proposes a dynamic Learning Automata-based Task Assignment algorithm, named LATA, to address this challenge. In the algorithm, each application is represented as a Directed Acyclic Graph, with tasks as nodes and data dependencies as edges. Initially, tasks are grouped based on their data dependencies to consolidate independent tasks into one group. Subsequently, a variable-structure learning automaton is assigned to each group of tasks to identify appropriate task-machine combinations. The primary objectives of LATA include minimizing makespan and energy consumption by facilitating efficient task placement to achieve load balance and maximize resource utilization. Additionally, an enhancement is proposed, involving the use of a different grouping policy prior to task assignment to further improve performance. Computer simulation results demonstrate the superior performance of the proposed algorithms in highly heterogeneous environments compared to state-of-the-art algorithms. Notably, total execution time and energy consumption decrease by up to 50% and 37%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&BN, which integrates Bayesian Networks (BN) and Multi-Objective Optimization (MOO). This approach is designed to concurrently overcome these ECC limitations. The ECC-MOO&BN method focuses on extracting diverse and interpretable label orderings for the ECC classifier. We initiated this process by employing mutual information to investigate label relationships and establish the initial structures of the BN. Subsequently, an enhanced NSGA-II algorithm was applied to develop a series of Directed Acyclic Graphs (DAGs) that effectively balance the likelihood and complexity of the BN structure. The rationale behind using the MOO method lies in its ability to optimize both complexity and likelihood simultaneously, which not only diversifies DAG generation but also helps avoid overfitting during the production of label orderings. The DAGs, once sorted topologically, yielded a series of label orderings, which were then seamlessly integrated into the ECC framework for addressing multi-label classification (MLC) problems. Experimental results show that when benchmarked against eleven leading-edge MLC algorithms, our proposed method achieves the highest average ranking across seven evaluation criteria on nine out of thirteen MLC datasets. The results of the Friedman test and Nemenyi test also indicate that the performance of the proposed method has a significant advantage compared to other algorithms. [ABSTRACT FROM AUTHOR]

With the rise of big data processing is creating a new challenge i.e., keeping pace with the flow of information and efficient I/O performance is the key here. However, analyzing I/O performance is a complex task due to the many layers involved, from applications and libraries to the operating system, storage devices, and everything in between. In this paper, we propose a convolutional neural networks (CNN)-based directed acyclic graph regression (DAGR) network to predict the I/O performance of applications. The system first gathers I/O request information directly from the storage layer (block storage). This information is then converted into a visual representation (graph image) and augmented using various techniques to create additional training data. The core of the system is a CNN-based prediction model designed to identify potential I/O performance patterns by analyzing the generated graph images. Evaluations using real-world application benchmarks demonstrate that the proposed method can accurately predict the performance of various applications, including file servers, databases, mail servers, and video servers, with an accuracy of up to 99.73%. [ABSTRACT FROM AUTHOR]

With the rapid development of innovative applications, lots of computation-intensive and delay-sensitive tasks are emerging. Task offloading, which is regarded as a key technology in the emerging mobile edge computing paradigm, aims at offloading the tasks from mobile devices (MDs) to edge servers or the remote cloud to reduce system delay and energy consumption of MDs. However, most existing task offloading studies either didn't consider the dependencies among tasks or simply designed heuristic schemes to solve dependent task offloading problems. Different from these studies, we propose a deep reinforcement learning (DRL) based task offloading scheme to jointly offload tasks with dependencies. Specifically, we model the dependencies among tasks by directed acyclic graphs and formulate the task offloading problem as minimizing the average cost of energy and time (CET) of users. To solve this NP-hard problem, we propose a deep Q-network learning-based framework that creatively utilizes deep neural networks to extract system features. Simulation results show that our proposed scheme outperforms the existing DRL scheme and heuristic scheme in reducing the average CET of all users and can obtain near-optimal solutions. [ABSTRACT FROM AUTHOR]

Dependent tasks are typically modeled using directed acyclic graphs (DAGs), and scheduling algorithms based on DAGs have been extensively researched. Most of the existing algorithms assume that the task or communication duration is deterministic. Nevertheless, any delays in task execution or communication can significantly affect the scheduling results. Aiming at minimizing the DAGs' makespan, a heuristic algorithms called heterogeneous optimistic complete time (HOCT) is proposed. The algorithm assumes that the task characteristic values are modeled randomly. It calculates task priorities based on the acceleration ratio and allocates computing resources using an optimistic execution timetable. Then, a Monte-Carlo simulation-based scheduling algorithm which built on the top of HOCT is proposed. Experimental results show that the proposed algorithm achieves better makespan of the stochastic DAG. It also provides a more robust scheduling solution to unpredictability than critical-path-on-a-processor, heterogeneous earliest finish time-no cross and parental prioritization earliest finish time algorithms. [ABSTRACT FROM AUTHOR]

Here we introduce graphPAF, a comprehensive R package designed for estimation, inference and display of population attributable fractions (PAF) and impact fractions. In addition to allowing inference for standard population attributable fractions and impact fractions, graphPAF facilitates display of attributable fractions over multiple risk factors using fan-plots and nomograms, calculations of attributable fractions for continuous exposures, inference for attributable fractions appropriate for specific risk factor → mediator → outcome pathways (pathway-specific attributable fractions) and Bayesian network-based calculations and inference for joint, sequential and average population attributable fractions in multi-risk factor scenarios. This article can be used as both a guide to the theory of attributable fraction estimation and a tutorial regarding how to use graphPAF in practical examples. [ABSTRACT FROM AUTHOR]

Estimating dependence relationships between variables is a crucial issue in many applied domains and in particular psychology. When several variables are entertained, these can be organized into a network which encodes their set of conditional dependence relations. Typically however, the underlying network structure is completely unknown or can be partially drawn only; accordingly it should be learned from the available data, a process known as structure learning. In addition, data arising from social and psychological studies are often of different types, as they can include categorical, discrete and continuous measurements. In this paper, we develop a novel Bayesian methodology for structure learning of directed networks which applies to mixed data, i.e., possibly containing continuous, discrete, ordinal and binary variables simultaneously. Whenever available, our method can easily incorporate known dependence structures among variables represented by paths or edge directions that can be postulated in advance based on the specific problem under consideration. We evaluate the proposed method through extensive simulation studies, with appreciable performances in comparison with current state-of-the-art alternative methods. Finally, we apply our methodology to well-being data from a social survey promoted by the United Nations, and mental health data collected from a cohort of medical students. R code implementing the proposed methodology is available at https://github.com/FedeCastelletti/bayes_networks_mixed_data. [ABSTRACT FROM AUTHOR]

Task offloading solves the problem that the computing resources of terminal devices in hospitals are limited by offloading massive radiomics-based medical image diagnosis model (RIDM) tasks to edge servers (ESs). However, sequential offloading decision-making is NP-hard. Representing the dependencies of tasks and developing collaborative computing between ESs have become challenges. In addition, model-free deep reinforcement learning (DRL) has poor sample efficiency and brittleness to hyperparameters. To address these challenges, we propose a distributed collaborative dependent task offloading strategy based on DRL (DCDO-DRL). The objective is to maximize the utility of RIDM tasks, which is a weighted sum of the delay and energy consumption generated by execution. The dependencies of the RIDM task are modeled as a directed acyclic graph (DAG). The sequence prediction of the S2S neural network is adopted to represent the offloading decision process within the DAG. Next, a distributed collaborative processing algorithm is designed on the edge layer to further improve run efficiency. Finally, the DCDO-DRL strategy follows the discrete soft actor-critic method to improve the robustness of the S2S neural network. The numerical results prove the convergence and statistical superiority of the DCDO-DRL strategy. Compared with other algorithms, the DCDO-DRL strategy improves the execution utility of the RIDM task by at least 23.07, 12.77, and 8.51% in the three scenarios. [ABSTRACT FROM AUTHOR]

In this study, we aim at investigating dynamic relations among steel price indices of all seven regional markets in China, spanning the period of 2010M1–2021M4. With daily data, we apply techniques of vector error correction modeling and the directed acyclic graph to analyze the issue of contemporaneous causality among the seven indices, the latter of which is facilitated through the LiNGAM algorithm. We find via vector error correction modeling that each of the seven indices is part of cointegration relations and responses to long-run equilibrium disturbances. We arrive at causal paths via the LiNGAM algorithm for enabling innovation accounting analysis. With this analysis, we find sophisticated dynamics in price adjustment processes following shocks, for which east and northeast regions have the most significant impacts on other regions, followed by south and north regions, followed by the southwest region. Central south and northwest regions are not found to have significant impacts on others. These results suggest that policies on regional steel prices in the long run might be designed with most attention paid to five of the seven regions, particularly east and northeast regions. [ABSTRACT FROM AUTHOR]

Previous research has suggested that there are gender differences in the way depressive symptoms present themselves. Nevertheless, existing literature has predominantly concentrated on assessing the severity of these symptoms, and it remains unclear how depressive symptoms are interconnected among women and men in non-Western populations. To address this gap, the present study compared the depressive symptom networks of South Korean women and men. We analyzed data collected from a nationally representative sample of 16,569 South Korean women and men, using regularized partial correlation networks and directed acyclic graphs (DAG) to examine the connections between depressive symptoms. Network analysis revealed that depressed mood, fatigue, and low self-esteem may play a significant role in activating and maintaining the network structure in women and men. Furthermore, the DAG identified probabilistic causal pathways between the nodes. These findings provide important insights into which symptoms play a key role in the depression of South Korean women and men. Our findings may contribute to the development of more tailored interventions for individuals experiencing depression. [ABSTRACT FROM AUTHOR]

The objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables, such as interventions or random trials, are often deemed costly or impractical. With the diversity of means of data acquisition and abundant data, approaches that directly learn causal structure from observational data are attracting more interest in academia. Unfortunately, learning a directed acyclic graph (DAG) from samples of multiple variables has been proven to be a challenging problem. Recent proposals leverage the continuous optimization method to discover the structure of the target DAG from observational data using an acyclic constraint function. Although it has an elegant mathematical form, its optimization process is prone to numerical explosion or the disappearance of high-order gradients of constraint conditions, making the convergence conditions unstable and the result deviating from the true structure. To tackle these issues, we first combine the existing work to give a series of algebraic equivalent conditions for a DAG and its corresponding brief proofs, then leverage the properties of the M-matrix to derive a new acyclic constraint function. Based on the utilization of this function and linear structural equation models, we propose a novel causal discovery algorithm called LeCaSiM, which employs continuous optimization. Our algorithm exploits the spectral radius of the adjacency matrix to dynamically adjust the coefficients of the matrix polynomial, effectively solving the above problems. We conduct extensive experiments on both synthetic and real-world datasets, the results show our algorithm outperforms the existing algorithms on both computation time and accuracy under the same settings. [ABSTRACT FROM AUTHOR]

The estimator of a causal directed acyclic graph (DAG) with the PC algorithm is known to be consistent based on independent and identically distributed samples. In this paper, we consider the scenario when the multivariate samples are identically distributed but not independent. A common example is a stationary multivariate time series. We show that under a standard set of assumptions on the underlying time series involving ρ -mixing, the PC algorithm is consistent under temporal dependence. Further, we show that for the popular time series models such as vector auto-regressive moving average and linear processes, consistency of the PC algorithm holds. We also prove the consistency for the Time-Aware PC algorithm, a recent adaptation of the PC algorithm for the time series scenario. Our findings are supported by simulations and benchmark real data analyses provided towards the end of the paper. [ABSTRACT FROM AUTHOR]

Background: Discovering causality in environmental systems is challenging because frequently controlled experiments or numerical simulations are difficult. Algorithms to learn directed acyclic graphs from system data are powerful, but they often result in too many possible causal structures that cannot be properly evaluated. Results: An approach to this problem proposed here is to initially restrict the system to a target variable with its two major drivers. Subsequently, testable causal structures are obtained from rules to infer directed acyclic graphs and expert knowledge. The proposed approach, which is essentially based on correlation and regression, was applied to understand drivers of a toxic algal bloom in the Odra River in summer 2022. Through this application, useful insight on the interplay between river flow and salt inputs that likely caused the algal bloom was obtained. Conclusions: The Odra River example demonstrated that carefully applied correlation and regression techniques together with expert knowledge can help to discover reliable casual structures in environmental systems. [ABSTRACT FROM AUTHOR]

Mobile edge computing offloads computing-intensive applications from resource-constrained terminal devices to adjacent edge servers to meet users' latency and energy consumption requirements. Most existing studies do not consider the dependencies between applications, leading to the wastage of computing resources. As the number of request users increases, edge servers with limited resources cannot meet the needs of all users. However, there are a large number of idle computing resources on the user side that are not utilized. Aiming at the problem of computing offloading of dependent tasks in this scenario, we establish an end-edge collaboration-dependent task offloading model and propose an offloading algorithm that balances task completion time and energy consumption. Firstly, we solve the problem of collaboratively matching request users by considering user mobility and computing requirements. Secondly, we determine the scheduling order of tasks according to the dependencies between tasks. Finally, we propose a hybrid artificial bee colony algorithm to solve the problem of task offloading. The results show that our algorithm saves 19.9% in average task completion time compared to an offloading strategy that does not consider device-to-device. [ABSTRACT FROM AUTHOR]

Bayesian network classifier (BNC) allows efficient and effective inference under condition of uncertainty for classification, and it depicts the interdependencies among random variables using directed acyclic graph (DAG). However, learning an optimal BNC is NP-hard, and complicated DAGs may lead to biased estimates of multivariate probability distributions and subsequent degradation in classification performance. In this study, we suggest using the entropy function as the scoring metric, and then apply greedy search strategy to improve the fitness of learned DAG to training data at each iteration. The proposed algorithm, called One + Bayesian Classifier (O + BC), can represent high-dependence relationships in its robust DAG with a limited number of directed edges. We compare the performance of O + BC with other six state-of-the-art single and ensemble BNCs. The experimental results reveal that O + BC demonstrates competitive or superior performance in terms of zero-one loss, bias-variance decomposition, Friedman and Nemenyi tests. [ABSTRACT FROM AUTHOR]

We study polytopes defined by inequalities of the form ∑ i ∈ I z i ≤ 1 for I ⊆ [ d ] and nonnegative z i where the inequalities can be reordered into a matrix inequality involving a column-convex { 0 , 1 } -matrix. These generalize polytopes studied by Stanley, and the consecutive coordinate polytopes of Ayyer, Josuat-Vergès, and Ramassamy. We prove an integral equivalence between these polytopes and flow polytopes of directed acyclic graphs G with a Hamiltonian path, which we call spinal graphs. We show that the volumes of these flow polytopes are given by the number of upper (or lower) G-cyclic orders defined by the graphs G. As a special case we recover results on volumes of consecutive coordinate polytopes. We study the combinatorics of k-Euler numbers, which are generalizations of the classical Euler numbers, and which arise as volumes of flow polytopes of a special family of spinal graphs. We show that their refinements, Ramassamy's k-Entringer numbers, can be realized as values of a Kostant partition function, satisfy a family of generalized boustrophedon recurrences, and are log concave along root directions. Finally, via our main integral equivalence and the known formula for the h ∗ -polynomial of consecutive coordinate polytopes, we give a combinatorial formula for the h ∗ -polynomial of flow polytopes of non-nested spinal graphs. For spinal graphs in general, we present a conjecture on upper and lower bounds for their h ∗ -polynomial. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and mobile edge computing (MEC) technologies advance, more workloads are being transferred to edge servers for processing. The offloading strategy is a crucial aspect of this development, considering all relevant factors and requiring a rapid development approach. This research aims to develop and propose an innovative solution for fine-grained task offloading scheduling in the context of the evolving technologies of the Internet of Things (IoT) and mobile edge computing (MEC). The research addresses the challenges posed by task dependencies within user-generated applications processed across distributed edge servers. This study presents a solution for fine-grained task offloading scheduling by representing user-generated applications as a directed acyclic graph (DAG). We propose a multi-population cooperative elite algorithm (MCE-PSO) based on particle swarm optimization to address the challenge of offloading tasks with dependencies in MEC, reducing application execution time and energy consumption. The proposed method uses a Penalty-based algorithm to minimize energy usage. The results of our experiments show that MCE-PSO outperforms benchmark algorithms, reducing overhead by up to 82.6%, 36.6%, and 16.3%. This demonstrates the efficiency and reliability of our system. [ABSTRACT FROM AUTHOR]

Some of the conventional approaches to task scheduling are discussed in this section. The scheduling of tasks in the Internet of Things (IoT) application is a complex job in cloud computing due to the heterogeneity characteristics of the IoT. Therefore, an efficient scheduling approach is vital in improving the performance of the Cloud-IoT applications. The improvement in QoS mainly depends on efficient task scheduling and allocating the task to the appropriate Virtual Machine (VM). This paper uses a clustering-based approach to design an optimal solution for task scheduling and VM allocation for IoT applications in education services. The workflow is simplified with horizontal and vertical clustering, and VM is allocated. The task is classified into coarse and fine-grained, based on the average execution time of the total number of tasks. The average execution time is calculated based on the best and worst performance of VM. The vertical clustering approach clusters the tasks into sequential and parallel, thereby turning the huge volume of tasks into simple ones. The horizontal clustering approach selects the VM with the help of a bipartite matching graph which enhances the Quality of Service (QoS). The proposed task clustering has been evaluated in metrics like makespan, response time, throughput, total execution time, task completion time, and VM utilization. The proposed approach achieved a makespan of 2500 ms and throughput of 83 Mbps for 100 tasks, a response time of 0.12 ms, a total execution time of 640 ms, and a total task completion time of 340,000 ms for a 2 arrival rate based on performance evaluation results. [ABSTRACT FROM AUTHOR]

In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees. This goal is typically addressed using Bayesian techniques, however, current Bayesian methods do not scale to large data sets. Furthermore, for large data sets with relatively low signal one cannot even store every good tree individually, especially when the trees are required to be bifurcating. In this paper, we develop a novel object called the “history subpartition directed acyclic graph” (or “history sDAG” for short) that compactly represents an ensemble of trees with labels (e.g. ancestral sequences) mapped onto the internal nodes. The history sDAG can be built efficiently and can also be efficiently trimmed to only represent maximally parsimonious trees. We show that the history sDAG allows us to find many additional equally parsimonious trees, extending combinatorially beyond the ensemble used to construct it. We argue that this object could be useful as the “skeleton” of a more complete uncertainty quantification. [ABSTRACT FROM AUTHOR]

Real causal processes may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs); each sample follows a joint distribution that factorizes according to a DAG, but the DAG may differ between samples due to multiple independent factors. We then introduce an algorithm called Causal Inference over Mixtures that uses longitudinal data to infer a graph summarizing the causal relations generated from a mixture of DAGs even when cycles, non-stationarity, latent variables or selection bias exist. Experiments demonstrate improved performance in inferring ancestral relations as compared to prior approaches. R code is available at https://github.com/ericstrobl/CIM. [ABSTRACT FROM AUTHOR]

Workflow applications are a popular tool used by scientists to model and run applications on cloud computing. With the rise of multi-core parallel systems and cloud computing high-performance computing is becoming more accessible to scientists, but it also presents new challenges. One pressing issue is the energy consumption of these systems, both for environmental and financial reasons. To minimize energy usage in cloud computing, methods such as energy-efficient scheduling are gaining attention. However, current scheduling solutions have limitations, or failing to address the problem as a multi-objective optimization balancing performance and energy. This problem is considered complex and NP-complete. Many researchers have attempted to resolve it using heuristic and meta-heuristic methods. Although these methods may not always provide optimal results, they are still a subject of active research. This paper proposes a new multi-objective optimization algorithm that combines the Heterogeneous Earliest Finish Time and BAT algorithm to optimize multiple conflicting objectives for energy, cost, makespan, and resource utilization. The results are verified using the Analysis of Variance statistical tool, and the proposed algorithm is shown to be superior to existing contemporary algorithms. [ABSTRACT FROM AUTHOR]

Parental burnout results from chronic stress in parenting, and it can be accompanied by harmful behaviors such as parental neglect and violence (Mikolajczak & Roskam, 2018). Network analysis examines psychological phenomena within a system of its constituents, and thus it is promising for understanding the distinct features of parental burnout and behaviors related to it. Recently, Blanchard et al. (2021) conducted the first network analysis of parental burnout and related harmful behaviors in the family context, but did so using an outdated measure and conceptualization of parental burnout. In the present study, in a sample of French-speaking parents (N = 3144, from five different previous studies), we aimed to investigate how each of the four features in the new conceptualization of parental burnout (i.e., emotional exhaustion, feeling fed up, emotional distance, and contrast with the previous parental self) interact with one another and with parental neglect and violence in a network system. In this preregistered reanalysis, we generated two network models commonly used with cross-sectional data: a Graphical Gaussian Model and a Directed Acyclic Graph. Our results point to emotional exhaustion and feeling fed up as key driving forces of the network structure, while emotional distance appears as a critical feature tying parental burnout with parental neglect and violence. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDSs) are employed at various levels in the network to either detect or prevent an intrusion that could cause irrecoverable data damage in IoT applications. Nowadays, different machine learning (ML) and artificial intelligence techniques are used to develop prediction models for IDS. However, existing ML-based detection models mostly rely on associative features to determine the relationship between attacks and traffic variables. These features failed to discern correlation from causality, and thus, many times, they generated poor performance on the testing data. The method of drawing causal inferences can be helpful to researchers in focusing on the causes of the intrusion rather than the correlation of the attributes, which provide only limited information. However, the method of drawing causal inferences has few to no implementations in the IDS. This paper explores using a causal analysis method, the Bayesian causal network for the IDS, and attempts to determine the significant attributes that can lead to an accurate prediction model for the IDS. The presented causal inference method is validated via an experimental analysis of the message queuing telemetry transport protocol dataset. The result shows that the Bayesian network implementation of causal inference has achieved an average accuracy, precision, recall, and F1-score of 99.8%, 99.4%, 98.89%, and 99.13%, respectively, for different considered attack scenarios. The analysis of the results shows that the performance of the presented method is equivalent to or better than other machine learning solutions. [ABSTRACT FROM AUTHOR]

As the core of blockchain technology, consensus mechanism determines the security, stability and availability of the system. Most of consensus mechanism including Po-X, X-BFT and so on mainly focuses on the design and improvement of the consensus layer, ignoring the impact of the communication mechanism on consensus efficiency and the privacy protection of the consensus node. It leads to the emergence of node corruption and conspiracy attacks. In this paper, we present MACT, a new multi-channel anonymous consensus mechanism for consortium blockchain. MACT uses anonymous communication mechanism, based on Onion Routing Technology Tor, to protect node privacy and prevent the corruption of consensus nodes. To reduce the computational power consumption, MACT abandons the traditional attempt conversion and mining mechanism, and adopts double ring threshold signature. Besides, to achieve the cross chain management of alliance members as well as the privacy protection of the branch chain ledgers, we improve the distributed ledger of MACT based on directed acyclic graph technology(DAG). Additionally, we design a set of identity authentication mechanism NKC for MACT based on the improved Keberos protocol, which is completely independent of the third party public key infrastructure, reduces the probability of Byzantine nodes to a certain extent. The experiment results demonstrate that MACT can achieve anonymous consensus and has good consensus efficiency. [ABSTRACT FROM AUTHOR]

Border Gateway Protocol (BGP), the default inter-domain routing protocol on the Internet, lacks inherent mechanisms to validate the prefix ownership and integrity of inter-domain routes exchanged among multiple domains, resulting in BGP hijack attacks. Conventional security approaches such as RPKI and BGPSec are centralized and complex by nature, and require changes to existing routing infrastructure. In recent times, blockchain based solutions are proposed for validating the routing information exchanged across different domains in a decentralized manner. However, because of lower transaction throughput, longer confirmation time and huge storage overhead, the existing solutions are not suitable for validating the routing information exchanged among domains, where a large number of prefix allocations and BGP route advertisements are recorded as transactions on the blockchain. This work proposes an Inter-domain Prefix and Route Validation (IPRV) framework for validating prefix ownership and inter-domain routes exchanged among the domains on the Internet. IPRV leverages (a) Fast and Scalable Directed Acyclic Graph-based Distributed Ledger (FSD2L) to record transactions corresponding to the prefix allocations and BGP route advertisements made by different domains on the Internet, and (b) Route Validation Nodes (RVNs) which maintain FSD2L to provide prefix and route validation services to the BGP routers within a domain. IPRV framework is implemented and verified using docker containers, and the simulations performed on large inter-domain networks showed that the proposed IPRV framework using RVNs and FSD2L achieves high transaction throughput while minimizing the storage consumption of the FSD2L. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising technology for the Internet of Vehicles, especially in terms of application offloading and resource allocation. Most existing offloading schemes are sub-optimal, since these offloading strategies consider an application as a whole. In comparison, in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning. In our framework, each application is modelled as a directed acyclic graph, where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks. Both vehicles and MEC server within the communication range can be used as candidate offloading nodes. Then, the offloading involves assigning these computing nodes to subtasks. In addition, the proposed offloading scheme deal with the delay constraint of each subtask. The experimental evaluation show that, compared to existing non-partitioning offloading schemes, this proposed one effectively improves the performance of the application in terms of execution time and throughput. [ABSTRACT FROM AUTHOR]

This study explores dynamic relationships among Chinese housing prices for the years 2010–2019. With monthly data from 99 major cities in China, we use the vector error correction model and directed acyclic graph to characterize contemporaneous causality among housing prices from different tiers of cities. The PC algorithm identifies the causal pattern and the LiNGAM algorithm further identifies the causal path, from which we perform innovation accounting analysis. Complex housing price dynamics are found in the price adjustment process following price shocks, which is not only dominated by the top tiers of cities. This suggests that policies on housing prices in the long run might need to be planned from a national perspective. [ABSTRACT FROM AUTHOR]

In order to solve the complex task scheduling problem of fog computing processing big data in the industrial Internet of Things, a task scheduling strategy based on ant colony algorithm called TSSAC (task scheduling strategy with ant colony)is proposed. Tasks with dependencies are modeled as a directed acyclic graph. The performance indices including makespan, load balancing and energy consumption of fog server are optimized simultaneously, and the ant colony algorithm is used to solve the multi-objective optimization problem. The pheromone heuristic factor and pheromone evaporation coefficient of the ant colony algorithm are updated in a linear increasing way, so that the ants are less affected by pheromones in the early stage and obtain a larger search range. During the later stage, it is greatly affected by pheromone and quickly converges to the optimal solution. Furthermore, during the task execution container migration mechanism is introduced to solve the overload problem caused by high server utilization and energy loss caused by low server utilization simultaneously. The simulation results show that the proposed task scheduling strategy TSSAC reduces energy consumption by 23.5% compared with the traditional algorithm, meanwhile, achieves a compromise between task makespan and load balancing index compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

Given a set S of integers, for a directed acyclic pseudograph we say that it has an S-good orientation if all its sources and sinks have degrees in S; in these terms, the existing notion of good orientation is a { 1 } -good orientation. We give a criterion for a pseudograph to admit an S-good orientation in terms of the structure of its leaf blocks. This criterion allows to compute whether a pseudograph admits an S-good orientation with optimal (linear) time and space complexity. As an application, we give a simple criterion for a graph to be realized as the Reeb graph of a Morse function in terms of leaf blocks of the graph. Similar conditions can be obtained for other classes of functions, such as (simple) Morse–Bott functions or round functions. [ABSTRACT FROM AUTHOR]

Bipolar disorder (BD) is linked to a high risk of relapse in the year postpartum. The aim of this study was to search for an association of a mood episode during pregnancy with a lack of maternal improvement after a post-partum episode requiring joint hospitalization. In an observational, naturalist, and multicentric study, 261 women suffering from a BD and jointly hospitalized with their child in a Mother-Baby Unit (MBU) were assessed for risk factors associated with a lack of maternal improvement at discharge. A directed acyclic graph (DAG)-based approach was used to identify confounders to be included in a multiple regression model. In bivariate analyses, a lack of improvement (16.9%) was associated with pregnancy specificities (decompensation, psychotropic treatment, antipsychotics, and benzodiazepines intake), as well as maternal smoking during pregnancy and baby's neonatal hospitalization. In a multivariate analysis based on DAG, a lack of improvement was linked to psychiatric decompensation during pregnancy (OR = 3.31, 95%CI [1.55–7.35], p = 0.002), independently from maternal age, mother's maltreatment during childhood, low level of education, single status, low familial social support, and diagnosis of personality disorder. This study shows the critical importance of mental health during pregnancy in women with BD. Clinical screening and evaluation of the benefit/risk balance of psychotropics during pregnancy are essential. [ABSTRACT FROM AUTHOR]

Applications with several tasks consuming huge energy during they are executed on modern high performance computing systems has not gone unnoticed. Fortunately, researchers have provided many solutions to reduce energy consumption by employing dynamic power management (DPM) and dynamic voltage frequency scaling (DVFS). We offer another perspective for studying this issue through increasing the actual amount of completed workload per unit energy consumption (i.e. efficiency of unit energy consumption). In order to Maximize the amount of Work under a Given Energy, an energy efficiency algorithm for tasks with time and precedence constraints, called MWGE, is proposed in this paper. For tasks with precedence constraints, we calculate the upward rank values to establish their scheduling order and tune processor speed according to the execution workload to achieve maximum efficiency. And for tasks with time constraints, we utilize the earliest deadline first (EDF) to schedule task and select the optimal speed to achieve our objective as well as guarantee that each task meets its deadline. Compared with greedy algorithm, our algorithm decreases the rate of speed switching and increases energy efficiency obviously. To demonstrate the effectiveness and accuracy of MWGE, the execution of two real-time applications with 100 tasks under a given budget energy are simulated. Our simulation results show that, on average, MWGE algorithm can achieve a 56.32% less switching rate and 13.42% higher energy efficiency compared to the existing greedy algorithm. [ABSTRACT FROM AUTHOR]

Background: Urine sodium and potassium are used as surrogate markers for dietary consumption in adults with hypertension, but their role in youth with hypertension and their association with components of the renin-angiotensin-aldosterone system (RAAS) are incompletely characterized. Some individuals with hypertension may have an abnormal RAAS response to dietary sodium and potassium intake, though this is incompletely described. Our objective was to investigate if plasma renin activity and serum aldosterone are associated with urine sodium and potassium in youth referred for hypertensive disorders. Methods: This pilot study was a cross-sectional analysis of baseline data from 44 youth evaluated for hypertensive disorders in a Hypertension Clinic. We recorded urine sodium and potassium concentrations normalized to urine creatinine, plasma renin activity, and serum aldosterone and calculated the sodium/potassium (UNaK) and aldosterone/renin ratios. We used multivariable generalized linear models to estimate the associations of renin and aldosterone with urine sodium and potassium. Results: Our cohort was diverse (37% non-Hispanic Black, 14% Hispanic), 66% were male, and median age was 15.3 years; 77% had obesity and 9% had a secondary etiology. Aldosterone was associated inversely with urine sodium/creatinine (β: −0.34, 95% CI −0.62 to −0.06) and UNaK (β: −0.09, 95% CI −0.16 to −0.03), and adjusted for estimated glomerular filtration rate and serum potassium. Conclusions: Higher serum aldosterone levels, but not plasma renin activity, were associated with lower urine sodium/creatinine and UNaK at baseline in youth referred for hypertensive disorders. Further characterization of the RAAS could help define hypertension phenotypes and guide management. [ABSTRACT FROM AUTHOR]

Bayesian networks in the form of Directed Acyclic Graphs (DAGs) represent an effective tool for modeling and inferring dependence relations among variables, a process known as structural learning. In addition, when equipped with the notion of intervention, a causal DAG model can be adopted to quantify the causal effect on a response due to a hypothetical intervention on some variable. Observational data cannot distinguish between DAGs encoding the same set of conditional independencies (Markov equivalent DAGs), which however can be different from a causal perspective. In addition, because causal effects depend on the underlying network structure, uncertainty around the DAG generating model crucially affects the causal estimation results. We propose a Bayesian methodology which combines structural learning of Gaussian DAG models and inference of causal effects as arising from simultaneous interventions on any given set of variables in the system. Our approach fully accounts for the uncertainty around both the network structure and causal relationships through a joint posterior distribution over DAGs, DAG parameters and then causal effects. [ABSTRACT FROM AUTHOR]

Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and (1 + λ) . In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the ( 1 + λ ) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP's number of registers and CGP's levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse. [ABSTRACT FROM AUTHOR]

This study examines a price relationship among domestic South Korean (hereafter, Korea) premium beef, "Hanwoo," non-premium beef, "Yukwoo," and imported beef from the USA and Australia. In our study, we utilize monthly data from Korea Institute for Animal Products Quality Evaluation and the Global Trade Atlas from the period between January 2009 and December 2016. We model a directed acyclic graph and employ the Granger causality test and variance decomposition methods. The results show that based on the price relationship, the imported beef products compete with Yukwoo, and not with Hanwoo. Our findings imply that Korean consumers consider Hanwoo to be different from imported beef. [ABSTRACT FROM AUTHOR]

A representation of complex high-tech digital projects is proposed in the form of a directed graph that combines input, output and intermediate data with software modules into a single bundle. This representation makes it possible to consider a digital project as an informational and algorithmic unit of technical and economic interaction that do not require additional acquisition and development of software or hardware. The project in this form is created and operated on a single multi-party cloud platform for high-performance computing, which is accessible to both scientific and engineering personnel of all participants within their access rights. This approach evolves the open innovation concept for digital projects and is implemented as a cloud-based Internet platform. The latter includes a central application server and a repository that stores both algorithms and data, provides means for user registration and maintenance, communication, as well as accounting for the resource usage in form of algorithms and data in solving application problems. As a confirmation of the fundamental adequacy of the proposed approach, a working prototype of such system is presented together with a list of projects in various domain areas implemented using the prototype. [ABSTRACT FROM AUTHOR]

Epidemiological studies have established that noise from transportation sources exceeding the safe limits elevates the risk for cardiovascular diseases. The results however have remained heterogeneous. The present study was conducted to investigate the association between road traffic noise exposure and prevalence of coronary artery disease besides sub-group analysis was performed for identifying the most susceptible population. Traffic noise exposure was measured using the Lden metric in both continuous and categorical forms. A cross-sectional study was performed and information about sociodemographic, lifestyle, and health-related factors was collected. Noise level < 60 dB(A) representing the quiet areas was used as the reference group. Univariate and multivariate logistic regressions were performed to estimate the odds for self-reported coronary artery disease concerning road traffic noise after adjusting for confounding variables. The residents living in noisy areas were found to have a 2.25 times higher risk per 5 dB(A) increment in the noise levels (95% CI = 1.38 to 3.67). Males were at a higher risk of CAD (OR = 2.61; 95% CI = 1.84 to 3.72) as compared to females (OR = 2.07; 95% CI = 1.37–3.13). The subgroup analysis revealed that being sensitive to noise, belonging to a higher age group, reporting higher stress levels, and poor sleep quality were associated with higher risk. The study also provides evidence that exposure to noise levels greater than 60 dB(A) is associated with the prevalence of coronary artery disease in adults. [ABSTRACT FROM AUTHOR]

Several disease affecting the human respiratory system, such as asthma, pneumonia, etc. are associated with distinctive sounds. Here, we propose a methodology towards their automatic identification by means of signal processing and pattern recognition algorithms. We designed a suitable feature set based on wavelet packet analysis characterizing data coming from diverse classes of respiratory sounds following the logic of the challenge organised within the International Conference on Biomedical Health Informatics in 2017. The patterns revealed by the feature extraction stage are modelled by hidden Markov models. Automatic identification is carried out via a directed acyclic graph (DAG) scheme limiting the problem space while based on decisions made by the available HMMs. Thorough experiments following a well-established protocol demonstrate the efficacy of the proposed solution. Indeed, such a DAG-based structure outperforms the current state of the art including deep networks based on convolutional kernels. Importantly, the presented solution offers a high level of explainability as one is able to investigate the effective DAG path and understand both correct and incorrect predictions. [ABSTRACT FROM AUTHOR]

Cloud computing is a computing model that fully utilizes the resources on the Internet to maximize the utilization of resources. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. Task scheduling is one of the crucial and challenging non-deterministic polynomial-hard problems in cloud computing. In task scheduling, obtaining shorter makespan is an important objective and is related to the pros and cons of the algorithm. Machine learning algorithms represent a new method for solving this type of problem. In this paper, we propose a novel task scheduling algorithm called QL-HEFT that combines Q-learning with the heterogeneous earliest finish time (HEFT) algorithm to reduce the makespan. The algorithm uses the upward rank (ranku) value of HEFT as the immediate reward in the Q-learning framework. The agent can obtain better learning results to update the Q-table through the self-learning process. The QL-HEFT algorithm is divided into two major phases: a task sorting phase based on Q-learning for obtaining an optimal order and a processor allocation phase using the earliest finish time strategy. Experiments show that QL-HEFT achieves a shorter makespan compared to three other classical scheduling algorithms as well as good performances in terms of the average response time. [ABSTRACT FROM AUTHOR]

Foetal alcohol spectrum disorders (FASDs) are a leading cause of developmental disability. Prenatal alcohol use is the sole necessary cause of FASD, but it is not always sufficient. Multiple factors influence a child's susceptibility to FASD following prenatal alcohol exposure. Much of the FASD risk factor literature has been limited to discussions of association, rather than causation. While knowledge of predictor variables is important for identifying who is most at risk of FASD and for targeting interventions, causal knowledge is important for identifying effective mechanisms for prevention and intervention programmes. We conducted a systematic search and narrative synthesis of the evidence and used this to create a causal diagram (directed acyclic graph; DAG) to describe the causal pathways to FASD. Our results show that the aetiology of FASD is multifaceted and complex. FASD risk is determined by a range of lifestyle, sociodemographic, maternal, social, gestational, and genetic factors. The causal diagram that we present in this review provides a comprehensive summary of causal risk factors for FASD and can be used as a tool to inform data collection and statistical modelling strategies to minimise bias in future studies of FASD. [ABSTRACT FROM AUTHOR]

Task scheduling, which plays a crucial role in cloud computing and is the critical factor influencing the performance of cloud computing, is an NP-hard problem that can be solved with a heuristic algorithm. In this paper, we propose a novel heuristic algorithm, called biogeography-based optimization (BBO), and a new hybrid migrating BBO (HMBBO) algorithm, which integrates the migration strategy with particle swarm optimization (PSO). Both methods are proposed to solve the problem of scheduling-directed acyclic graph tasks in a cloud computing environment. The basic idea of our approach is to exploit the advantages of the PSO and BBO algorithms while avoiding their drawbacks. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. Both simulation and real-life experiments are conducted to verify the effectiveness of HMBBO. The experiment shows that compared with several classic heuristic algorithms, HMBBO has advantages in terms of global search ability, fast convergence rate and a high-quality solution, and it provides a new method for task scheduling in cloud computing. [ABSTRACT FROM AUTHOR]

In this commentary I respond to Lewis (2019) and Lanier (2019), building on their critiques and ideas, offering some additional thoughts about the dissemination of the Campbell, Rubin, and Pearl causal frameworks and their potential emergent value to the future of family violence research. I clarify that the central issue to credibility is the plausibility of assumptions, that some widely utilized methods often require researchers to make implausible assumptions, and that there is value to knowing and using all three frameworks. [ABSTRACT FROM AUTHOR]

One important determinant of well-being is the environmental quality. Many countries apply environmental regulations, reforms and policies for its improvement. However, the question is how the people value the environment, including the air quality. This study examines the association between air pollution and life satisfaction using the Swiss Household Panel survey over the years 2000–2013. We follow a Bayesian network (BN) strategy to estimate the causal effect of the income and air pollution on life satisfaction. We look at five main air pollutants: the ground-level ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and particulate matter of 10 micrometres (PM10). Then, we calculate the individuals' marginal willingness to pay (MWTP) of reducing air pollution that aims to improve their life satisfaction. Beside the BN model, we take advantage of the panel structure of our data and we follow two approaches as robustness check. This includes the adapted probit fixed effects and the generalised methods of moments system. Our findings show that O3 and PM10 present the highest MWTP values ranging between $8000 and $12,000, followed by the remained air pollutants with MWTP extending between $2000 and $6500. Applying the BNs, we find that the causal effect of income on life satisfaction is substantially increased. We also show the causal effects of air pollutants remain almost the same, leading to lower values of willingness to pay. [ABSTRACT FROM AUTHOR]

The economic crisis and the pressure towards efficient and effective use of public money claim for a higher accountability of research expenditure, as well as for a greater proximity of research to the needs of community. While agricultural productivity represents a worldwide goal for agricultural research as a response to growing food, feed and energy demands, other objectives besides productivity are becoming central. The challenge is to take into account broader impacts that go beyond academic and economic ones, and to improve knowledge on the causal impact-generating mechanisms. In this paper, we adopt a causal perspective and estimate the impact of agricultural research expenditure on multiple dimensions. We develop a structural equation model relating research expenditure, research activity, productivity and multiple impact indicators within a dynamic impact pathway, accounting for existing domain knowledge on causal relationships and their lag structures. The model is applied on EU 15 countries over the period 1980–2014, making use of official statistics from several European databases. [ABSTRACT FROM AUTHOR]

In this article, a new criterion, domain coverage, for white box testing is introduced. In search of the biggest subdomains for input variables exercising a given path, the variable domains are broken down into overlapping subregions in such a way that the resultant subregions either exercise or deviate from the path. To this aim, an incremental method is applied to detect the subregions, covering the longest subpaths from the start of the given path. The detected subdomain is further subdivided, and this process is repeated as far as the path is fully covered. If no subdomain is detected, the method is backtracked. The backtrack begins with the subdomain covering the longest subpath at the beginning of the given path, but this time the subdomain is divided into relatively smaller subregions, compared with the last time. A directed acyclic graph representation of each input variable domain is used to ignore re-examination of those subdomains which deviate from the path. Conducted experiments reveal that our proposed domain-oriented algorithm outperforms the existing path-oriented test data generation algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing has become a highly required platform in fields of information technology due to providing inexpensive services with high availability and scalability. The dynamic and diverse nature of the cloud computing systems makes scheduling of workflow tasks a pivotal issue. This paper proposes an algorithm to schedule applications' tasks to virtual machines (VMs) of cloud computing systems. The algorithm has three phases: level sorting, task-prioritizing and virtual machine selection. The three-phase process successfully assigns the virtual machine for each task without making any difficulties for evaluating the algorithm performance; extensive simulation experiments are performed. The introduced ICTS algorithm analyzes each incoming task which is sorted and ranked while assigning the virtual machine to the particular task which improves the overall scheduling process because it processes the job according to the importance. Then the efficiency of the system is evaluated using experimental results that indicate the improved cost task scheduling (ICTS) algorithm provides an improvement in schedule length as well as significant monetary cost saving. [ABSTRACT FROM AUTHOR]

Efficient task scheduling is important for achieving high performance in heterogeneous distributed computing systems. The main focus of this research is to build a task scheduling algorithm for a heterogeneous environment. We proposed an algorithm named parental prioritization earliest finish time. It has two phases, tasks prioritization phase and processor assigning phase. In the tasks prioritization phase, tasks will schedule in parental priority queue (PPQ) on the basis of downward rank and parental priority. Task prioritization is based on the directed acyclic graph. It can schedule the task of successor row before the current row if it has less communication cost. In the processor assigning phase, the processor will allocate to the scheduled tasks obtained from PPQ keeping the computation cost to a minimum. This proposed algorithm is compared with HEFT and CPOP algorithms through graphs generated from random task graph generator and a set of tasks. The experimental results show that our proposed scheduling algorithm performs significantly better than other algorithms in terms of both cost and makespan of schedules. [ABSTRACT FROM AUTHOR]

This paper investigates properties of fragmentary structures and establishes a relation between them and marked acyclic digraphs with one source and also a correspondence between classes of isomorphic fragmentary structures and unmarked acyclic digraphs of certain type, which are called feasible graphs. The concepts of a dimension of a feasible graph and its corresponding isomorphic fragmentary structures are defined. An expression is obtained for the lower-bound estimate of a dimension. A theorem on properties of feasible graphs is proved. The numbers of fragmentary structures and classes of isomorphic fragmentary structures of small dimensions are counted. [ABSTRACT FROM AUTHOR]

Abstract: The methods of protection against attacks on the ad hoc networks in the self-organizing cyber-physical systems are analyzed in the article, and their issues are identified. The authors propose an adaptive management method for the ad hoc network topology, where the blockchain-like directed acyclic graph is applied to solve the identified issues. [ABSTRACT FROM AUTHOR]

The construction of thin maximal bifix codes of degree 1 or 2 is clear and simple. In this paper, a class of thin maximal bifix codes of degree 3 which contains all finite maximal bifix codes of degree 3 is investigated. The construction of such codes is determined. It is well known that for any positive integers d and k, there are finitely many finite maximal bifix codes of degree d over a k-letter alphabet. But the enumeration problem is unsolved in general. In this paper, we show that for any positive integer k, there is a bijection from the set of all finite maximal bifix codes of degree 3 over a k-letter alphabet onto the set of all directed acyclic graphs with k vertices, and then, the enumeration problem of finite maximal bifix codes of degree 3 is solved. [ABSTRACT FROM AUTHOR]