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However, as host-switch graph cannot represent such systems, the authors investigate interconnection networks with multi-port hosts by introducing a multi-ported host-switch graph. The past years have seen a flurry of activity in the area of distributed computing and its related fields. The accepted papers for this special issue address the following aspects in computing and networking: Parallel and distributed computing Security and privacy Interconnection networks Artificial intelligence Self-stabilizing algorithms Parallel and distributed computing The first contribution, entitled "Efficient Parallel Implementations to Compute the Diameter of a Graph" by Takafuji et al., presents an efficient implementations of the Blocked Floyd-Warshall algorithm, which uses no barrier synchronization and invokes only one kernel call by using Single Kernel Soft Synchronization (SKSS) techniques. [Extracted from the article]

An editorial is presented on deep learning and big data analytics for processing and analyzing data in 5G and 6G applications. Topics include research activities and areas such as bioinformatics, beyond 5G and 6G communications, healthcare, internet of things (IoT), manufacturing business, and social networks; and providing services with massive connectivity, ultra-low latency, extremely high security, extremely low energy consumption, and ultra-high data-rate.

In this issue, we are pleased to present two papers that showcase innovative techniques in software testing in two different directions (memory-aware combinatorial test generation and survey about the use of machine learning for automated test generation). The results show that ML is used to generate inputs for different types of testing, improve the performance of existing generation methods, and generate test verdicts and oracles. The second paper highlights the potential of ML in automated test generation and provides insights into the challenges that researchers face when integrating ML into testing. [Extracted from the article]

Due to the ever‐increasing number of computer nodes in distributed systems, efficient and effective tools have become crucial for their analysis. Although several efficient methods have been proposed to monitor and profile distributed systems, tracing remains the most effective solution for in‐depth system analysis. Tracing is the act of collecting a trace, which is a sequence of low‐level events generated by the kernel or the userspace. After data collection, the most important part is the event analysis. The paradigm and choice of graphs determine the ability of the user to detect abnormal behaviors and identify their root cause. Although tracing is a highly effective approach to analyzing complex systems, the scalability of the current analysis tools is limited. As a consequence, tracing is often impractical for large distributed systems. This paper identifies the shortcomings of the current approaches, most notably the critical path computation and the trace file transfer between nodes. Then, this paper proposes new solutions to these drawbacks, most notably a distributed algorithm to compute the critical path, that does not aggregate all traces in a single node, and an efficient architecture to perform tracing on distributed systems. These new solutions are made publically available. [ABSTRACT FROM AUTHOR]

Proceedings of the 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2019); December 5, 2019:166-172; IEEE. Parallel and distributed computing has been the basis to many emerging areas, such as smart networks, cloud computing, big data analysis, and blockchain technology. We hope that this special issue provides valuable information and references for pursuit of research in the field of parallel and distributed computing and its relevant areas. [Extracted from the article]

To address the IoT security problem, in this paper we propose and evaluate the DDoS attack mitigation method based on blockchain, and construct a DDoS abnormal information detection and sharing model. The obtained experimental results show that when the number of decision trees increases, the training time of the DDoS attack detection model based on the RF model grows with a minimum trend of 14 s. The testing time is finally maintained at 1 s, and the recognition accuracy of DDoS attacks keeps improving, ultimately reaching over 99.8%. If the amount of DDoS abnormal traffic information exceeds 100 pieces and 2000 pieces, it only takes 0.1 and 5 s to sign the DDoS abnormal traffic information using ECDSA algorithm digitally. The signature verification only takes 0.1 and 9 s, respectively. And compared to conventional network physical system IoT security architecture, a network physical system IoT security architecture that integrates AI empowerment, blockchain, and SDN integration has a higher joint defense success rate. It can be explained that this scheme will be conducive to promoting joint defense against DDoS attacks and ensuring the security of the Internet of Things. [ABSTRACT FROM AUTHOR]

This paper considers distributed inference for two‐sample U‐statistics under the massive data setting. In order to reduce the computational complexity, this paper proposes distributed two‐sample U‐statistics and blockwise linear two‐sample U‐statistics. The blockwise linear two‐sample U‐statistic, which requires less communication cost, is more computationally efficient especially when the data are stored in different locations. The asymptotic properties of both types of distributed two‐sample U‐statistics are established. In addition, this paper proposes bootstrap algorithms to approximate the distributions of distributed two‐sample U‐statistics and blockwise linear two‐sample U‐statistics for both nondegenerate and degenerate cases. The distributed weighted bootstrap for the distributed two‐sample U‐statistic is new in the literature. The proposed bootstrap procedures are computationally efficient and are suitable for distributed computing platforms with theoretical guarantees. Extensive numerical studies illustrate that the proposed distributed approaches are feasible and effective. [ABSTRACT FROM AUTHOR]

In the process of accessing distributed power sources in microgrid, there will be serious coupling between active power and reactive power, which will lead to significant changes in the inverter outlet voltage and impedance and increase the circulating current phenomenon. The generation of circulating current will accelerate the aging of devices, reduce the inverter efficiency, and threaten the stable operation of the system. In this paper, the mechanism of circulating current generation is analysed, the basic characteristics of circulating current are studied, and an inverter parallel circulating current suppression strategy with adaptive virtual composite impedance and droop control dynamic adjustment algorithm is proposed. The virtual impedance setting can weaken the influence of line difference on system stability, make the system equivalent output impedance resistive, weaken the inductive device characteristics and reduce the circulating current amplitude; the adaptive droop algorithm implantation dynamically correlates the control power with the droop coefficient, enhance the power decoupling accuracy and weaken the circulating current influenced by power changes. The simulation platform and experimental setup established by the large‐scale simulation software PSCAD/EMTDC are used to study the basic scenarios of the inverter under two conditions of equal and unequal capacity, and the rationality of the proposed control strategy is tested. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Concurrency and Computation: Practice and Experience, which contains eight selected papers from the 14th International Conference on Parallel Processing and Applied Mathematics (PPAM 2022). The conference focused on topics related to high-performance computing (HPC) and included discussions on emerging technologies, algorithms, and software tools. The selected papers cover a range of subjects, including the convergence of iterative methods for solving nonlinear equations, testing interval arithmetic libraries, automatic performance tuning for symmetric eigenvalue problems, GPU computing, simulation of molecular magnetism, load balancing in distributed memory systems, predicting execution time of computational workloads, and resource selection for cloud-based applications. The papers provide valuable insights and advancements in the field of HPC. [Extracted from the article]

Summary: Applying ensemble feature selection (EFS) models in various problems has not been actively discussed, and there has been a lack of effort to make it applicable in the situations such as distributed environments. Due to restrictions of centralized algorithms such as their poor scalability in the high dimension data and also distributed nature of some data, using the traditional centralized computing for dealing with such problems may be inevitable. This paper aims to develop a homogenous distributed ensemble feature selection (HMDE‐FS) framework through a distributed resampling approach rather than a centralized one. The homogenous ensembles mainly operate along with a resampling process, so applying various methods to resampling can affect the performance of the model. Among various strategies, those with and without replacement are two of the main technique families, hence we investigated the efficiency of two well‐known with/without replacement techniques: bootstrapping (BS) and cross‐validation (CV) inspired method that we named crisscross (CC). The proposed HMDE‐FS approaches are tested on eight datasets, and the heavy experimental results illustrate that these methods considerably reduce runtime, while classification accuracy maintains its competitiveness. [ABSTRACT FROM AUTHOR]

The complexity and dynamism of distributed intelligent systems have motivated the utilization of autonomic computing in such systems. However, the interoperability of agents in order to attain self‐managing properties is itself a great challenge that requires more attention. Although a number of semantic approaches exist for central autonomic systems, there are not many attempts to develop mechanisms for facilitating semantic interoperability to attain a distributed feedback loop. In this paper, a semantic approach is presented to overcome this challenge. The main component of this approach is AutonoML language that provides the shared semantics for autonomic computing in distributed environments. Besides this language, the method of its application for enhancing the interoperability of the distributed MAPE‐K loop is presented. In order to evaluate this mechanism, three well‐known metrics from the ontological assessment area are utilized, and the results are compared with an existing autonomic ontology that shows greater relational and attributes richness of the proposed semantic structures. Moreover, in order to evaluate the applicability of the mechanism, a case from NASA‐ANTS project is studied, and the results show that the proposed mechanism is capable of facilitating the achievement of more stable self‐adaptive solutions in less time and by exchanging fewer messages. [ABSTRACT FROM AUTHOR]

A primary objective in detecting disease outbreaks is to precisely identify substantial increases in process means to control the spread of outbreaks. In this paper, we introduce a novel directionally sensitive adaptive multivariate exponentially weighted moving average chart for efficiently detecting either increases or decreases in the mean vector of a multivariate normally distributed process. In addition, the chart's sensitivity is enhanced through features such as variable sample size, variable sampling interval, and a combination of both. Monte Carlo simulation is employed to estimate zero‐state and steady‐state average time‐to‐signal (ATS) profiles. Performance measures, including expected weighted and relative ATS, are used for evaluating the performances of multivariate charts. Our results indicate that the proposed charts outperform existing charts in detecting various mean shift sizes. The application of these multivariate charts is demonstrated through illustrative examples. [ABSTRACT FROM AUTHOR]

Science gateways have been widely utilized by a large number of user communities to simplify access to complex distributed computing infrastructures. While science gateways are still becoming increasingly popular and the number of user communities is growing, the fast and efficient creation of new science gateways and the flexibility to deploy these gateways on‐demand on heterogeneous computational resources, remain a challenge. Additionally, the increase in the number of users, especially with very different backgrounds, requires intuitive embedded e‐learning tools that support all stakeholders to find related learning material and to guide the learning process. This paper introduces a novel science gateway framework that addresses these challenges. The framework supports the creation, publication, selection, and deployment of cloud‐based reference architectures that can be automatically instantiated and executed even by nontechnical users. The framework also incorporates a knowledge repository exchange and learning module that provides embedded e‐learning support. To demonstrate the feasibility of the proposed solution, two scientific case studies are presented based on the requirements of the plasmasphere, ionosphere, and thermosphere research communities. [ABSTRACT FROM AUTHOR]

Summary: The scale of real‐world graphs is constantly growing. To deal with large‐scale graphs, distributed graph processing has attracted much research efforts. Existing distributed graph processing systems are commonly built on traditional TCP/IP communication stack, which leads to network bottleneck because of low bandwidth and heavy kernel stack operations. Meanwhile, in real power‐law graphs, the average number of mirror vertices after graph partitioning is very large, resulting in significant communication overhead among nodes. The emerging high‐performance Remote Direct Memory Access (RDMA) network has the features of low latency, high bandwidth, and low CPU overhead, which brings new opportunities for distributed graph processing systems. Existing RDMA‐assisted graph processing systems focus on synchronous execution, which imposes barriers between consecutive iterations. Synchronous execution transfers bulk data among nodes and thus only needs a small number of network transfers. However, synchronous execution is usually less efficient than asynchronous execution because of bulk synchronization. Asynchronous execution accelerates graph processing by eliminating barriers, which in turn requires to transfer a large amount of small size data. In this paper, we propose RGraph, an RDMA‐assisted asynchronous distributed graph processing system. RGraph distributes edges into two parts to isolate master and mirror vertices. RGraph exploits the asymmetry of RDMA to accelerate the one‐to‐many communication between master and mirror vertices. We implement RGraph on top of PowerGraph and conduct comprehensive experiments with large‐scale real graphs to evaluate its performance. Results show that compared to existing designs, RGraph reduces the execution time by up to 81%. [ABSTRACT FROM AUTHOR]

Fault tolerance is crucial in ensuring smooth working of distributed and cloud computing. It is challenging to implement because of the constantly changing infrastructure and complex configurations in distributed and cloud computing. Implementation of various fault tolerance methods require domain‐specific knowledge as well as in‐depth understanding of the existing techniques and approaches. Recent surveys on fault tolerance in cloud and distributed environments exist, but they have limitations. This article systematically reviews fault tolerance approaches in distributed and cloud computing and discusses their taxonomy. Based on the taxonomy provided, fault‐tolerance approaches are divided into four types, that is, reactive approaches, proactive approaches, adaptive approaches, and hybrid approaches. Reactive approaches provide a preventive measure after the occurrence of faults in the system. Proactive approaches prevent the system or minimize failure effects by predicting in advance. The adaptive approaches predict, learn, and adapt the changes to deal with new faults in the system. The hybrid approaches combine reactive, proactive, and adaptive approaches. The objective of this article is to give a better understanding of handling faults using suitable approaches and further compare them on various parameters. The paper also presents a promising research direction based on the challenges and issues in multiple approaches. [ABSTRACT FROM AUTHOR]

Summary: Combinatorial test generation, also called t‐way testing, is the process of generating sets of input parameters for a system under test, by considering interactions between values of multiple parameters. In order to decrease total testing time, there is an interest in techniques that generate smaller test suites. In our previous work, we used graph techniques to produce high‐quality test suites. However, these techniques require a lot of computing power and memory, which is why this paper investigates distributed computing for t‐way testing. We first introduce our distributed graph colouring method, with new algorithms for building the graph and for colouring it. Second, we present our distributed hypergraph vertex covering method and a new heuristic. Third, we show how to build a distributed IPOG algorithm by leveraging either graph colouring or hypergraph vertex covering as vertical growth algorithms. Finally, we test these new methods on a computer cluster and compare them to existing t‐way testing tools. [ABSTRACT FROM AUTHOR]

For the monitoring of large‐scale chemical processes, the distributed method is often used to extract local feature information and model the extracted local feature information to obtain a process monitoring model. But the distributed process monitoring model often contains more process variables, which makes the local information of the process data flooded. To make up for the insufficient extraction of local information in traditional distributed process monitoring, supervised sparse preserving projections model based on distributed principal component analysis (DPCA‐SSPP) is proposed in this paper. First, the process data are decomposed by the PCA algorithm, and the principal component space and residual space are obtained. Second, the variables of each sub‐block are selected according to the maximum correlation criterion, and the SSPP process monitoring model is established for each sub‐block. Finally, the monitoring results of each sub‐block are combined together to form a global monitoring result through the Bayesian information fusion strategy. The proposed scheme can be proved to be effective through the simulation on a nonlinear numerical example and the Tennessee Eastman benchmark (TE) process. [ABSTRACT FROM AUTHOR]

Summary: Backtracking branch‐and‐prune (BP) algorithms and their variants are exhaustive search tree techniques widely employed to solve optimization problems in many scientific areas. However, they characteristically often demand significant amounts of computing power for problem sizes representative of real‐world scenarios. Given that their search domains can often be partitioned, these algorithms are frequently designed to execute in parallel by harnessing distributed computing systems. However, to achieve efficient parallel execution times, an effective strategy is required to balance the nonuniform partition workloads across the available resources. Furthermore, with the increasing integration of servers with heterogeneous resources and the adoption of resource sharing, balancing workloads is becoming complex. This paper proposes a strategy to execute parallel BP algorithms more efficiently on even shared or heterogeneous distributed systems. The approach integrates a self‐adjusting dynamic partitioning method in the BP algorithm with a dynamic scheduler, provided by an application middleware, which manages the parallel execution while addressing any issues of imbalance. Empirical results indicate better scalability with efficiencies above 90% for instances of an application case study for the discretizable molecular distance geometry problem (DMDGP). Improvements of up to 38% were obtained in execution speed‐ups compared to a more traditional parallel BP implementation for DMDGP. [ABSTRACT FROM AUTHOR]

Three‐dimensional (3D) reconstruction is a pivotal research area within computer vision and photogrammetry, offering a valuable foundation of data for the development of smart cities. However, existing methods for constructing 3D models from unmanned aerial vehicle (UAV) images often suffer from slow processing speeds and low central processing unit (CPU)/graphics processing unit (GPU) utilization rates. Furthermore, the utilization of cluster‐based distributed computing for 3D modelling frequently results in inefficient resource allocation across nodes. To address these challenges, this paper presents a novel approach to 3D modelling in clusters, incorporating a dynamic load‐balancing strategy. The method divides the 3D reconstruction process into multiple stages to lay the groundwork for distributing tasks across multiple nodes efficiently. Instead of traditional traversal‐based communication, this approach employs gossip communication techniques to reduce the network overhead. To boost the modelling efficiency, a dynamic load‐balancing strategy is introduced that prevents nodes from becoming overloaded, thus optimizing resource usage during the modelling process and alleviating resource waste issues in multidevice clusters. The experimental results indicate that in small‐scale data environments, this approach improves CPU/GPU utilization by 35.8%/23.4% compared with single‐machine utilization. In large‐scale data environments for cluster‐based 3D modelling tests, this method enhances the average efficiency by 61.4% compared with traditional 3D modelling software while maintaining a comparable model accuracy. [ABSTRACT FROM AUTHOR]

Technological advancement in the digital era has continued to produce voluminous amounts of data through various devices. Even though data is produced distributively, it needs to be accumulated centrally for processing, analysis, and knowledge extraction that faces several challenges such as bandwidth, latency, congestion, privacy, and security. Fog computing paradigm addresses some of these issues, and can be used as a distributed data processing unit. Federated learning trains a shared model over distributed nodes. However, a fog node can not process continuously growing data due to computational limitations. In this paper, we propose FIDEL: a fog integrated federated learning framework for neural network training using resource‐constrained devices. The federation of resource‐constrained Internet of Things (IoT) devices creates a shared global model trained on local data, which is generalized on the unseen dataset for prediction/inferences. We have also designed an online training scheme to process continuous data with limited compute resources. The FIDEL supports both synchronous and asynchronous federate learning that empowers resource‐constrained devices to train machine learning models. To test the learning capabilities of the FIDEL, we have trained three neural networks (i) Shallow network; (ii) Deep Network; (iii) Convolutional Neural Network (CNN) models for human position detection in industrial IoT setup on rapidly changing datasets. The experimental results show that the framework can learn input–output relationships with significantly high accuracy. The overall system efficiency of the framework is reasonable in terms of latency and memory usage for resource‐constrained devices. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is distributed type of technology to offers distant services by the internet to manage, access, and store data on their server. The cloud server manages and distributes large amounts of data. During large amounts of complex structured and unstructured data transactions, in order to protect the data and cloud resources from harmful activities by using the main task of the security system. Therefore, a novel intrusion detection system is required in cloud networks to detect malicious activity and improve secure data transmission. In this paper, a novel technique called advanced set containment deep learned Rabin certificateless signcryption (ADRES) technique is employed to secure data transmission for achieving better confidentiality, and integrity, in cloud computing. The ADRES technique includes three major phases namely Registration and key generation, signcryption, and unsigncryption. At the first phase, the user registers details to the cloud server using secure transmission in big data analytics. The cloud server generates the private and public keys for each registered user. In the second phase, the signcryption process is determined by cloud user that encrypts and generates the signature by using the Rabin cryptosystem. Then encrypted data is stored in a cloud server with minimum space complexity. Finally, the unsigncryption is performed by using signature verification and decryption. First, the signature verification is performed by applying an Advanced set containment Deep belief neural network for identifying the authorized or unauthorized user (i.e., attacker). The authorized user decrypts data using the Rabin cryptosystem to achieve the original data. Thus, secure data transmission is obtained through improved data confidentiality and integrity. Simulation of the proposed ADRES technique is performed with respect to data confidentiality level, data integrity rate, and computation time, space complexity with several numbers of data. Experimental results reported ADRES obtains the improved data transmission security with better confidentiality rate by 7%, integrity by 7%, and lesser time 16% and space complexity by 18% than the conventional methods. [ABSTRACT FROM AUTHOR]

Much of what we know results from information sources on which we epistemically rely. This fact about epistemic reliance, however, stands in tension with a very powerful intuition governing knowledge, an intuition that Pritchard (e.g., 2010) has termed the "ability intuition," the idea that a believer's "reliable cognitive faculties are the most salient part of the total set of causal factors that give rise to [their] believing the truth" (Vaesen, 2011, p. 518; compare Greco, 2003; 2009; 2010). In this paper I suggest that this tension may indeed be ineliminable. I proceed by canvassing some representative attempts to reconcile epistemic reliance and the ability intuition. In doing so, I suggest that all of these attempts founder on one or the other of two elements of what I've previously described (Shieber, 2013, 2015) as a "personalist presumption" in discussions of social epistemology: an excessive focus on (i) reliability filters within the persons who are the recipients of information or (ii) on reliable truth‐tracking and ‐conveying abilities in the persons who are the transmitters of information. In conclusion, I suggest how best to resolve the tension: by abandoning the ability intuition. [ABSTRACT FROM AUTHOR]

This paper presents faas‐sim, a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks. faas‐sim bridges this gap by providing (a) a generalized model of serverless systems that builds on the function‐as‐a‐service abstraction, (b) a simulator that uses trace data from real‐world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge‐cloud systems. We present the conceptual design, implementation, and a thorough evaluation of faas‐sim. By running experiments on both real‐world test beds and replicating them using faas‐sim, we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully used faas‐sim to either design, optimize, or evaluate serverless edge computing systems. [ABSTRACT FROM AUTHOR]

Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which exceed the capacity of small devices for inference and incurs long training time. In addition, it is difficult to afford long training time and inference time of big models even in high performance servers, as well. As an efficient approach to compress a large deep model (a teacher model) to a compact model (a student model), knowledge distillation emerges as a promising approach to deal with the big models. Existing knowledge distillation methods cannot exploit the elastic available computing resources and correspond to low efficiency. In this paper, we propose an Elastic Deep Learning framework for knowledge Distillation, that is, EDL‐Dist. The advantages of EDL‐Dist are threefold. First, the inference and the training process is separated. Second, elastic available computing resources can be utilized to improve the efficiency. Third, fault‐tolerance of the training and inference processes is supported. We take extensive experimentation to show that the throughput of EDL‐Dist is up to 3.125 times faster than the baseline method (online knowledge distillation) while the accuracy is similar or higher. [ABSTRACT FROM AUTHOR]

Summary: In modern industrial production processes, process monitoring plays a major role in improving process efficiency and quality of product, and it is an important task in industrial process. The best way to implement process monitoring is to develop models that describe different phenomena in physics, chemistry, and processes. However, because the modeling of industrial processes is often very complex and has significant intrinsic nonlinearities, a reasonable theoretical modeling method are often impractical. With the adoption of distributed control systems, the application of multivariate statistical monitoring is becoming more and more popular in the distributed system environment. This paper uses the technology for multivariate monitoring of continuous production processes. Based on principal component analysis (PCA) and making full use of system statistics information, the method can well deal with the nonlinear and multimode distribution of industrial data, which is difficult to be dealt with by traditional methods. This method can be used not only in process detection but also in fault detection. Finally, the method is applied to the actual example and the Tennessee‐Eastman model; the simulation results prove the feasibility of the proposed method and achieve better results. [ABSTRACT FROM AUTHOR]

This paper investigates the reliable computation offloading in non‐orthogonal multiple access enabled mobile edge computing networks, where the short‐packet technique is adopted to meet the stringent latency requirements of delay‐sensitive computing services. To characterize the reliability of computation offloading with finite blocklength coding, a novel analytical framework is developed to approximate the average overall block error probability (AOBEP) by using a double‐case linearization. With the derived approximate expression for AOBEP, the joint optimization of computation workload and transmit power is further investigated, in order to minimize the AOBEP under the computation/communication delay constraints. To tackle the non‐convexity of the formulated problem, the formulated problem is first decomposed into the computation workload problem and the transmit power allocation problem. For the computing workload problem, a closed‐form solution is derived. Then, by applying the derived workload allocation, the power allocation is transformed into the convex form through some approximations and solved by the successive convex approximation technique. Finally, simulation results are provided to demonstrate the reliability enhancement of computation offloading and reveal the relationship between the workload/power allocation and the communication/computation delay. [ABSTRACT FROM AUTHOR]

A progressive average chart usually triggers initial out‐of‐control (OC) signals more simply and quickly than other memory‐type charts. In this paper, two progressive average control procedures are proposed for monitoring the coefficient of variation (CV) of a normally distributed process variable, namely, the progressive CV (PCV) and progressive resetting CV (PRCV) control charts , respectively. The implementation of the proposed charts is presented, and the necessary design parameters are provided. Through extensive numerical simulations, it is shown that the proposed PCV and PRCV charts outperform several existing control charts to detect the initial OC signals, especially for the small and moderate CV shifts, under each combination of the shift size, the sample size, and the in‐control target value of the CV. In addition, the application of the proposed control charts is illustrated by a detection example for a spinning process. [ABSTRACT FROM AUTHOR]

Introduction: Learning health systems are challenged to combine computable biomedical knowledge (CBK) models. Using common technical capabilities of the World Wide Web (WWW), digital objects called Knowledge Objects, and a new pattern of activating CBK models brought forth here, we aim to show that it is possible to compose CBK models in more highly standardized and potentially easier, more useful ways. Methods: Using previously specified compound digital objects called Knowledge Objects, CBK models are packaged with metadata, API descriptions, and runtime requirements. Using open‐source runtimes and a tool we developed called the KGrid Activator, CBK models can be instantiated inside runtimes and made accessible via RESTful APIs by the KGrid Activator. The KGrid Activator then serves as a gateway and provides a means to interconnect CBK model outputs and inputs, thereby establishing a CBK model composition method. Results: To demonstrate our model composition method, we developed a complex composite CBK model from 42 CBK submodels. The resulting model called CM‐IPP is used to compute life‐gain estimates for individuals based their personal characteristics. Our result is an externalized, highly modularized CM‐IPP implementation that can be distributed and made runnable in any common server environment. Discussion: CBK model composition using compound digital objects and the distributed computing technologies is feasible. Our method of model composition might be usefully extended to bring about large ecosystems of distinct CBK models that can be fitted and re‐fitted in various ways to form new composites. Remaining challenges related to the design of composite models include identifying appropriate model boundaries and organizing submodels to separate computational concerns while optimizing reuse potential. Conclusion: Learning health systems need methods for combining CBK models from a variety of sources to create more complex and useful composite models. It is feasible to leverage Knowledge Objects and common API methods in combination to compose CBK models into complex composite models. [ABSTRACT FROM AUTHOR]

Since the cognitive revolution, psychologists have developed formal theories of cognition by thinking about the mind as a computer. However, this metaphor is typically applied to individual minds. Humans rarely think alone; compared to other animals, humans are curiously dependent on stores of culturally transmitted skills and knowledge, and we are particularly good at collaborating with others. Rather than picturing the human mind as an isolated computer, we can imagine each mind as a node in a vast distributed system. Viewing human cognition through the lens of distributed systems motivates new questions about how humans share computation, when it makes sense to do so, and how we can build institutions to facilitate collaboration. [ABSTRACT FROM AUTHOR]

Summary: Advancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in‐efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un‐encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy‐preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real‐time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Summary: Performance prediction for executing graph applications on distributed systems is a prerequisite to improve system performance. Especially for distributed systems optimized by sacrificing the accuracy of results to improve runtime performance, performance prediction can be used to determine accuracy‐related system parameters to achieve a tradeoff between the runtime and inaccuracy of results. This article presents an approach that predicts the runtime and inaccuracy of executing graph algorithms on bulk synchronous parallel (BSP)‐based systems to optimize system parameters by using an artificial neural network (ANN). The proposed approach samples different scales of subgraphs from the input graph by maintaining that the features of subgraphs are similar to the features of the input graph. Then it executes graph algorithm on each subgraph and extracts their runtime features. An ANN‐based performance prediction model is trained off‐line based on the extracted features and is used to predict the performance of executing graph algorithm on the complete input graph. We have validated the proposed approach by conducting single‐source shortest path, connected component, and PageRank on the BSP‐based distributed systems. The experimental results demonstrate that the prediction method can effectively predict the runtime and the inaccuracy with a relative error rate under 14% and under 25%, respectively, compared with the actual performance results. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue on "Edge computing accelerated deep learning: Technologies and applications." The traditional centralized approach to implementing machine learning (ML) and deep learning (DL) applications has limitations such as high latency, large bandwidth usage, and privacy concerns. Edge computing, which integrates mobile/wireless infrastructure and cloud datacenters, has emerged as a paradigm to address these issues. The special issue aims to promote innovative technologies and applications that accelerate DL in the distributed edge computing environment. The editorial highlights five selected papers that focus on using DL and other ML methods to address challenges in edge computing-based application scenarios and designing efficient and lightweight ML and DL models for the edge computing environment. The Lead Guest Editor expresses gratitude to the authors and reviewers for their contributions. [Extracted from the article]

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING AI/ML is used today in an increasing number of scientific domains. This 2020 special issue represents an active collaboration between the organizers of two science gateway workshops. Pintye et al.5 have developed an AI-reference architecture, which supports the development of AI application classes. MiCADO (Microservices-based Cloud Application-level Dynamic Orchestrator)1 is a framework for switching gateway deployments from one container or cloud orchestration solution to another with minimal effort. [Extracted from the article]

The supposed disruptive and transformational potential of blockchain technology has received widespread attention in the media, from legislators, and from academics across disciplines. While much of this attention has revolved around cryptocurrencies such as Bitcoin, many see the true promise of blockchain technology in its potential use for transactions in traditional assets, as well as for facilitating self‐executing 'smart contracts', which replace vague and imprecise natural language with unambiguous computer code. This article presents a simple legal argument against the feasibility of a meaningful blockchain‐based economic system. Blockchain‐based systems are shown to be unsuitable for transactions in traditional assets, unless design choices are made which render the use of the technology pointless. The same argument is shown to apply to smart contracts. Legal and practical obstacles therefore mean that, outside its original realm of cryptocurrencies, blockchain technology is highly unlikely to transform economic interactions in the real world. [ABSTRACT FROM AUTHOR]

Summary: There is a growing need for next‐generation science gateways to increase the accessibility of emerging large‐scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID‐19‐related challenges. Such science gateways that enable access to distributed computing resources for large‐scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio‐technical approach for developing a next‐generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID‐19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence‐based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug‐in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot‐assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks. [ABSTRACT FROM AUTHOR]

Byzantine broadcast is a fundamental primitive in distributed computing. A highly efficient Byzantine broadcast protocol, motivated by the real‐world performance of practical state machine replication protocols, is increasingly needed. This article focuses on the state‐of‐the‐art partially synchronous Byzantine broadcast protocol proposed by Abraham et al. (PODC'21), which achieves optimal good‐case latency of two rounds and optimal resilience of n ≥ 5f − 1 in this setting. Each step of the protocol is analysed, and then improved by cutting down the number of messages required to be collected and transmitted in the heaviest step of the protocol by about half, without adding any extra cost. This benefits from a new property, named "spread", that we identify and extract from the original protocol. It helps us to eliminate non‐essential work in its view‐change procedure. The authors also show that no further reduction is possible without violating security. A prototype is implemented and the performances of improved and original protocols are evaluated in the same environment. The results show that our improvement can achieve about 50% lower communication cost and 40% shorter latency at a scale of 100 replicas. The latency gap becomes wider as the scale further increases. [ABSTRACT FROM AUTHOR]

Privacy and security EI-enabled CPS revolutionizes the development of new computing paradigms to facilitate the flow of data and the delivery of content. EI-enabled CPS collects physiological data from patients through technologies such as wireless sensor networks, radio frequency identification and wearable devices. Compared with the traditional supply chain financial model, EI-enabled CPS brings changes to the supply chain in four aspects: credit evaluation, risk protection, credit cycle and punishment mechanisms. Cloud computing has become an indispensable auxiliary technology for EI-enabled CPS due to its powerful computing ability and high concurrency. [Extracted from the article]

Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi‐level optimization framework to optimize the computational sequences. Machine‐learning (ML) assisted static load‐balancing, and different dynamic load‐balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high‐throughput drug suit, solvent model, P38 protein, and SARS‐CoV‐2 systems. The results show that the usage rate of given computational resources for high throughput and large‐scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high‐performance computing (HPC) clusters. [ABSTRACT FROM AUTHOR]

The edge computing in knowledge‐defined network (KDN) is a kind of distributed computing architecture, and the format of edge resources stored in different edge computing nodes are different, which yields the data heterogeneity problem and hampers the interaction between edge nodes. Ontology is considered as the solution of data heterogeneity on Semantic Web, and matching ontologies is a high‐efficiency method of addressing the data heterogeneity problem. Ontology meta‐matching investigates how to determine the optimal weights to aggregate multiple similarity measures to achieve high‐quality ontology alignment, which is a challenge about nonlinear mathematical problem in ontology matching domain. To face this challenge, unsupervised learning method such as generative adversarial network (GAN) becomes an effective methodology. GAN consists of two models of different targets that are opposed to each other in training to produce the final best result. To improve the GAN's efficiency, this work further proposes a GAN with simulated annealing algorithm (SA‐GAN), where the stagnation counter is introduced to accelerate GAN's the convergence speed. The experiment uses the famous benchmark in the ontology domain, and the comparisons with the advanced ontology matching systems shows that SA‐GAN is able to find high‐quality alignments to help build bridges between edge nodes on edge computing. [ABSTRACT FROM AUTHOR]

Summary: Task clustering process in distributed systems is used to improve the performance of executing workflow applications. In cloud‐assisted Internet of things (CIoT) framework, performing efficient task clustering is highly challenging because of the dynamic nature of cloud, availability of various billing models, and billing granularity options. This work aims at reducing the security overhead among the dependent tasks by using data transfer aware grouping technique (DTAGT), which takes into consideration the amount of data transferred among dependent tasks to obtain a cluster that reduces the security overhead in the network by optimizing the data transfer rate. Collecting the data in a CIoT environment is difficult, and the redundancy among the data from the sensors tends to decrease the lifetime of the network and also increases the latency has been resolved using proposed DTAGT. The overall objective of the proposed grouping algorithm is to reduce the total completion time and cost of executing workflow application with high reliability in CIoT platform. The proposed system is implemented, and the result analysis shows DTWCT (Dual‐tree complex wavelet transform) outperforms the other related works. [ABSTRACT FROM AUTHOR]

Summary: Map‐Reduce is a programming paradigm widely used for data intensive applications in distributed computing. Divisible load theory (DLT) is another model to partition divisible load for parallel processing. In this work, a new job scheduler named virtual job scheduler (VJS) is developed to schedule the MapReduce jobs based on DLT. VJS constructs a virtual job set from the queue of jobs awaiting execution by considering the CPU and IO resource utilization levels of each job. The core of VJS is in its partitioning algorithm. Two novel partitioning algorithms, namely, two level successive partitioning (TLSP) and predictive partitioning (PRED) have been proposed. TLSP applies DLT to both map and reduce phase in succession. This second load partitioning performed during the comparatively longer reduce phase, exploits the advantage of DLT better. PRED is a modification of TLSP aimed at optimizing the overall schedule length, by taking the idle time of the reducers into consideration. Evaluation of these two models across various execution environments has indicated a profound decrease in the wait time of reducers and as a result has shown a significant reduction in the makespan of the whole job as such. VJS altered to incorporate PRED partitioning algorithm has proved to be suitable for heterogeneous environment, with high resource utilization compared with the default Hadoop MapReduce scheduler. [ABSTRACT FROM AUTHOR]

The memory‐based control charts (CCs) such as exponentially weighted moving average (EWMA) and Cumulative sum (CUSUM) are good for quick detection of small or moderate shifts in the process mean. This study proposes a memory‐based CC to monitor the mean of a normally distributed process. The idea is to estimate the mean shift using an estimator based on an EWMA statistic and after that determine the smoothing constant of plotting EWMA statistics through the proposed continuous function. The performance of the proposed adaptive EWMA (AEWMA) chart is focused on utilizing extensive Monte Carlo simulations method with various shifts in the process mean. Taking into account the run‐length (RL) profiles, as an evaluation tool, the proposed chart is efficient as compared to the competitor charts. The demonstration of the proposed chart is presented by using a real dataset from the manufacturing process of substrates where the quality characteristic is the resistance of flow width. [ABSTRACT FROM AUTHOR]

Summary: Since the last decade, Market Basket Analysis has been propelled by augmentation of revenue information. Termed as high utility itemset mining (HUIM), this task considers the factors of purchase quantity and unit profit of the items in the transaction database. Although several sequential algorithms to mine HUIs exist, their performance degrades as the database becomes voluminous. Distributed computing solutions such as Apache Hadoop and Apache Spark have proven effective in alleviating this bottleneck. In this regard, the current study develops a parallel workflow to adapt a single‐phase tree‐based algorithm called the single phase utility computation (SPUC) algorithm on a Spark cluster. Based on the time taken to mine individual conditional pattern bases in SPUC, an assignment strategy that partitions the search space across the cluster is proposed in parallel SPUC (PSPUC) algorithm. Experimental evaluation conducted using real and synthetic datasets demonstrate that PSPUC outperforms PHUI‐Growth algorithm. Apart from this, PSPUC in conjunction with the time‐aware assignment strategy converges mining faster than a random assignment of items. A linear speedup of PSPUC is also demonstrated. [ABSTRACT FROM AUTHOR]

Studies of the ammonia oxidation reaction (AOR) for the synthesis of nitrite and nitrate (NO2/3−) have been limited to a small number of catalytic materials, majorly Pt based. As the demand for nitrate‐based products such as fertilisers continues to grow, exploration of alternative catalysts is needed. Herein, 19 metals immobilised as particles on carbon fibre electrodes were tested for their catalytic activity for the ammonia electrooxidation to NO2/3− under alkaline conditions (0.1 m KOH). Nickel‐based electrodes showed the highest overall NO2/3− yield with a rate of 5.0±1.0 nmol s−1 cm−2, to which nitrate contributed 62±8 %. Cu was the only catalyst that enabled formation of nitrate, at a rate of 1.0±0.4 nmol s−1 cm−2, with undetectable amounts of nitrite produced. Previously unexplored in this context, Fe and Ag also showed promise and provided new insights into the mechanisms of the process. Ag‐based electrodes showed strong indications of activity towards NH3 oxidation in electrochemical measurements but produced relatively low NO2/3− yields, suggesting the formation of alternate oxidation products. NO2/3− production over Fe‐based electrodes required the presence of dissolved O2 and was more efficient than with Ni on longer timescales. These results highlight the complexity of the AOR mechanism and provide a broad set of catalytic activity and nitrate versus nitrite yield data, which might guide future development of a practical process for the distributed sustainable production of nitrates and nitrites at low and medium scales. [ABSTRACT FROM AUTHOR]

To survive, all organisms must sense and respond to information from their environment. This is true of many organisms, including plants, which need to do all the things that other organisms do while operating under the limitations of being sessile and lacking a central nervous system. In this article, we explore how information theory can apply to plants and briefly review the types and sources of information and the mechanisms that plants use to perceive and respond to their environment. We identify and describe three primary modes by which a plant receives information: chemical, electromagnetic and mechanical. We describe how plants integrate information to detect the state of their neighbors, capture resources and regulate growth and metabolism. Overall, we find that plants interpret information from their surroundings as an emergent property of distributed information processed by a network of cells. We end with a prospectus of directions for future research including decoding signal from noise, storage of information, additional means of information transmission and two‐way information signaling with biotic partners. [ABSTRACT FROM AUTHOR]

Although the distributed monitoring model has been widely employed in monitoring large‐scale processes, the dynamic nonlinear property in process data is rarely investigated. Given the complex dynamic nature of industrial processes, different process variables interact with each other over time. In order to describe the correlation of dynamic variables more accurately, this work proposes a novel dynamic nonlinear fault detection framework based on maximum joint mutual information (MJMI)‐weighted dynamic kernel principal component analysis (WDKPCA). After dividing the process variables using the MJMI scheme, the proposed dynamic weighting method defines the weight of time‐delayed variables, which allows the dynamic characteristics of these variables to be characterized more comprehensively. By these means, the processes can be decomposed into multiple subblocks, and a distributed monitoring scheme based on DKPCA is thus established. Then, the Bayesian fusion strategy is used to fuse the monitoring results of different subblocks. Through a series of experiments on the Tennessee Eastman (TE) process, the results indicate that MJMI‐WDKPCA has superior process monitoring performance. [ABSTRACT FROM AUTHOR]

Large‐scale distributed convolution neural network (CNN) training brings two performance challenges: model performance and system performance. Large batch size usually leads to model test accuracy loss, which counteracts the benefits of parallel SGD. The existing solutions require massive hyperparameter hand‐tuning. To overcome this difficult, we analyze the training process and find that earlier training stages are more sensitive to batch size. Accordingly, we assert that different stages should use different batch size, and propose a variable batch size strategy. In order to remain high test accuracy under larger batch size cases, we design an auto‐tuning engine for automatic parameter tuning in the proposed variable batch size strategy. Furthermore, we develop a dataflow implementation approach to achieve the high‐throughput CNN training on supercomputer system. Our approach has achieved high generalization performance on SOAT CNN networks. For the ShuffleNet, ResNet‐50, and ResNet‐101 training with ImageNet‐1K dataset, we scale the batch size to 120 K without accuracy loss and to 128 K with only a slight loss. And the dataflow implementation approach achieves 93.5% scaling efficiency on 1024 GPUs compared with the state‐of‐the‐art. [ABSTRACT FROM AUTHOR]

Nowadays, progress in the determination of three‐dimensional macromolecular structures from diffraction images is achieved partly at the cost of increasing data volumes. This is due to the deployment of modern high‐speed, high‐resolution detectors, the increased complexity and variety of crystallographic software, the use of extensive databases and high‐performance computing. This limits what can be accomplished with personal, offline, computing equipment in terms of both productivity and maintainability. There is also an issue of long‐term data maintenance and availability of structure‐solution projects as the links between experimental observations and the final results deposited in the PDB. In this article, CCP4 Cloud, a new front‐end of the CCP4 software suite, is presented which mitigates these effects by providing an online, cloud‐based environment for crystallographic computation. CCP4 Cloud was developed for the efficient delivery of computing power, database services and seamless integration with web resources. It provides a rich graphical user interface that allows project sharing and long‐term storage for structure‐solution projects, and can be linked to data‐producing facilities. The system is distributed with the CCP4 software suite version 7.1 and higher, and an online publicly available instance of CCP4 Cloud is provided by CCP4. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms are being adopted to analyze medical data, promising faster interpretation to support doctors' diagnostics. The next frontier is to bring these powerful algorithms to implantable medical devices. Herein, a closed‐loop solution is proposed, where a cellular neural network is used to detect abnormal wavefronts and wavebrakes in cardiac signals recorded in human tissue is trained to achieve >96% accuracy, >92% precision, >99% specificity, and >93% sensitivity, when floating point precision weights are assumed. Unfortunately, the current hardware technologies for floating point precision are too bulky or energy intensive for compact standalone applications in medical implants. Emerging device technologies, such as memristors, can provide the compact and energy‐efficient hardware fabric to support these efforts and can be reliably embedded with existing sensor and actuator platforms in implantable devices. A distributed design that considers the hardware limitations in terms of overhead and limited bit precision is also discussed. The proposed distributed solution can be easily adapted to other medical technologies that require compact and efficient computing, like wearable devices and lab‐on‐chip platforms. [ABSTRACT FROM AUTHOR]

A six‐month‐long Atlantic hurricane season impacts Florida residents every year and can result in devastating consequences, including loss of life, property damage, and business interruptions. Hurricane risk assessment and loss prediction are critical to various uses such as determining homeowner insurance premiums, regulating these premiums, conducting scenario analysis, conducting stress tests for companies, disaster management, and evaluating the benefits of disaster mitigation techniques. This article describes the Florida Public Hurricane Loss Model (FPHLM): a large‐scale catastrophe model with massive databases and analytics tools for business and government decision‐making. We will discuss the design and implementation of each component in FPHLM and explain the tools and techniques utilized to tackle challenges in data availability, data analytics, and the interface between the data, analytical techniques, and computing. Results are shown to validate the software system's effectiveness and reliability and illustrate some of the system's use cases. [ABSTRACT FROM AUTHOR]